Tag Archives: Amazon QuickSight

Trakstar unlocks new analytical opportunities for its HR customers with Amazon QuickSight

Post Syndicated from Brian Kasen original https://aws.amazon.com/blogs/big-data/trakstar-unlocks-new-analytical-opportunities-for-its-hr-customers-with-amazon-quicksight/

This is a guest post by Brian Kasen and Rebecca McAlpine from Trakstar, now a part of Mitratech.

Trakstar, now a part of Mitratech, is a human resources (HR) software company that serves customers from small businesses and educational institutions to large enterprises, globally. Trakstar supercharges employee performance around pivotal moments in talent development. Our team focuses on helping HR leaders make smarter decisions to attract, retain, and engage their workforce. Trakstar has been used by HR leaders for over 20 years, specifically in the areas of applicant tracking, performance management, and learning management.

In 2023, Trakstar joined Mitratech’s world-class portfolio of Human Resources and Compliance products. This includes complementary solutions for OFCCP compliance management; diversity, equity, and inclusion (DEI) strategy and recruiting (now with Circa); advanced background screening; I-9 compliance; workflow automation; policy management; and more.

In 2022, Trakstar launched what is now called Trakstar Insights, which unlocks new analytical insights for HR across the employee life cycle. It’s powered by Amazon QuickSight, a cloud-native business intelligence (BI) tool that enables embedded customized, interactive visuals and dashboards within the product experience.

In this post, we discuss how we use QuickSight to deliver powerful HR analytics to over 3,000 customers and 43,000 users while forecasting savings of 84% year-over-year as compared to our legacy reporting solutions.

The evolving HR landscape

Over the past few years, new realities have emerged, creating unfamiliar challenges for businesses and new opportunities for HR leaders. In 2020, new working arrangements spawned by immediate necessity, with many companies shifting to fully remote or hybrid setups for the first time. As we still adapt to this new environment, organizations have trouble finding talent, with record-level resignation rates and a tight labor market.

As companies look to combat these new challenges, we’ve seen the rise of the Chief People Officer because organizations now recognize people as their greatest asset. With our three products, Trakstar Hire, Trakstar Perform, and Trakstar Learn, HR leaders can use data to take an integrated approach and foster a better employee experience.

Choosing QuickSight to bring solutions to the new reality of work

To help HR leaders navigate the new challenges of our time and answer new questions, we decided to embed interactive dashboards directly into each Trakstar product focused on the growing area of people analytics. QuickSight allowed us to meet our customers’ needs quickly and played a key role in our overall analytics strategy. Because QuickSight is fully managed and serverless, we were able to focus on building value for customers and develop an embedded dashboard delivery solution to support all three products, rather than focusing on managing and optimizing infrastructure. QuickSight allowed us to focus on building dashboards that address key pain points for customers and rapidly innovate.

In a 12-month timespan, we designed and launched five workforce dashboards to over 20,000 users, spanning hiring, performance, learning, and peer group benchmarking. During the same 12-month time period, in addition to our Trakstar Insights dashboard releases, we also migrated Trakstar Learn’s legacy reporting to QuickSight, which supports an additional 20,000 users.

Delighting our customers by embedding QuickSight

Our goal was to build something that would delight our customers by making a material difference in their daily lives. We set out to create something that went beyond a typical Minimum Viable Product, but rather create a Minimum Lovable Product. By this, we mean delivering something that would make the most significant difference for customers in the shortest time possible.

We used QuickSight to build a curated dashboard that went beyond traditional dashboards of bar charts and tables. Our dashboards present retention trends, hiring trends, and learning outcomes supplemented with data narratives that empower our customers to easily interpret trends and make data-driven decisions.

In January 2022, we launched the Perform Insights dashboards. This enabled our customers to see their data in a way they had never seen before. With this dashboard, HR leaders can compare organizational strengths and weaknesses over time. The power of QuickSight lets our customers slice and dice the data in different ways. As shown in the following screenshot, customers can filter by Review Process Type or Group Type and then take actionable next steps based on data. They can see where top and bottom performers reside within teams and take steps to retain top performers and address lower-performing employees. These were net new analytics for our HR customers.

Our investment in building with QuickSight was quickly validated just days after launch. One of our sales reps was able to engage a lost opportunity and land a multi-year contract for double our typical average contract value. We followed up our first dashboard launch by expanding Trakstar Insights into our other products with the Learning Insights dashboard for Trakstar Learn and Hiring Insights for Trakstar Hire (see the following screenshots). These dashboards provided new lenses into how customers can look at their recruitment and training data.

Through our benchmarking dashboards, we empowered our customers so they can now compare their trends against other Trakstar customers in the same industry or of similar size, as shown in the following screenshots. These benchmarking dashboards can help our customers answer the “Am I normal?” question when it comes to talent acquisition and other areas.

Telling a story with people data for reporting

With the custom narrative visual type in QuickSight, our benchmarking dashboards offer dynamic, customer-specific interpretations of their trends and do the heavy lifting interpretation for them while providing action-oriented recommendations. The burden of manual spreadsheet creation, manual export, data manipulation, and analysis has been eliminated for our customers. They can now simply screenshot sections from the dashboards, drop them into a slide deck, and then speak confidently with their executive teams on what the trends mean for their organization, thereby saving tremendous time and effort and opening the door for new opportunities.

A Trakstar Hire customer shared with us, “You literally just changed my life. I typically spend hours creating slides, and this is the content—right here, ready to screenshot for my presentations!”

Building on our success with QuickSight

With the success of launching Trakstar Insights with QuickSight, we knew we could modernize the reporting functionality in Trakstar Learn by migrating to QuickSight from our legacy embedded BI vendor. Our legacy solution was antiquated and expensive. QuickSight brings a more cohesive and modern look to reporting at a significantly lower overall cost. With the session-based pricing model in QuickSight, we are projecting to save roughly 84% this year while offering customers a more powerful analytics experience.


Building with QuickSight has helped us thrive by delivering valuable HR solutions to our customers. We are excited to continue innovating with QuickSight to deliver even more value to our customers.

To learn more about how you can embed customized data visuals and interactive dashboards into any application, visit Amazon QuickSight Embedded.

About the authors:

Brian Kasen is the Director, Business Intelligence at Mitratech. He is passionate about helping HR leaders be more data-driven in their efforts to hire, retain, and engage their employees. Prior to Mitratech, Brian spent much of his career building analytic solutions across a range of industries, including higher education, restaurant, and software.

Rebecca McAlpine is the Senior Product Manager for Trakstar Insights at Mitratech. Her experience in HR tech experience has allowed her to work in various areas, including data analytics, business systems optimization, candidate experience, job application management, talent engagement strategy, training, and performance management.

Defontana provides business administration solutions to Latin American customers using Amazon QuickSight

Post Syndicated from Cynthia Valeriano original https://aws.amazon.com/blogs/big-data/defontana-provides-business-administration-solutions-to-latin-american-customers-using-amazon-quicksight/

This is a guest post by Cynthia Valeriano, Jaime Olivares, and Guillermo Puelles from DeFontana.

Defontana develops fully cloud-based business applications for the administration and management of companies. Based in Santiago, Chile, with operations in Peru, Mexico, and most recently Colombia, our main product is a 100% cloud-based enterprise resource planning (ERP) system that has been providing value to our business customers for about 20 years. In addition to our core ERP product, we have developed integration modules for ecommerce, banks, financial and regulatory institutions, digital signatures, business reports, and many other solutions. Our goal is to continue building solutions for customers and make Defontana the best business administration tool in Chile and Latin America.

Most of our customers are small and medium businesses (SMBs) who need to optimize their resources. Our ERP system helps customers manage cash flow, time, human resources (HR), and other resources. As we were exploring how to continue innovating for our customers and looking for an embedded analytics solution, we chose Amazon QuickSight, which allows us to seamlessly integrate data-driven experiences into our web application.

In this post, we discuss how QuickSight has sped up our development time, enabled us to provide more value to our customers, and even improved our own internal operations.

Saving development time by embedding QuickSight

We built our ERP service as a web application from the beginning. 10 years ago, this was a big differentiator for us, but to continue to serve the SMBs that trust us for information on a daily basis, we wanted to offer even more advanced analytics. By embedding QuickSight into our web application, we have been able to provide business intelligence (BI) functionalities to customers two or three times faster than we would have if we had opted for libraries for generating HTML reports. Thanks to our embedded QuickSight solution, we are able to focus more of our energy on analyzing the requirements and functionalities that we want to offer our customers in each BI report.

The following screenshots show our ERP service, accessed on a web browser, with insights and rich visualizations by QuickSight.

We enjoy using QuickSight because of how well it integrates with other AWS services. Our data is stored in a legacy relational database management system (RDMS), Amazon Aurora, and Amazon DynamoDB. We are in the process of moving away from that legacy RDMS to PostgreSQL through a Babelfish for Aurora PostgreSQL project. This will allow us to reduce costs while also being able to use a multi-Region database with disaster recovery in the future. This would have been too expensive with the legacy RDMS. To seamlessly transfer data from these databases to Amazon Simple Storage Service (Amazon S3), we use AWS Database Migration Service (AWS DMS). Then, AWS Glue allows us to generate several extract, transform, and load (ETL) processes to prepare the data in Amazon S3 to be used in QuickSight. Finally, we use Amazon Athena to generate views to be used as base information in QuickSight.

Providing essential insights for SMB customers

QuickSight simplifies the generation of dashboards. We have made several out-of-the-box dashboards in QuickSight for our customers that they can use directly on our web app right after they sign up. These dashboards provide insights on sales, accounting, cash flow, financial information, and customer clusters based on their data in our ERP service. These free-to-use reports can be used by all customers in the system. We also have dashboards that can be activated by any user of the system for a trial period. Since we launched add-on dashboards, more than 300 companies have activated it, with over 100 of them choosing to continue using it after the free trial.

Besides generic reports, we have created several tailor-made dashboards according to the specific requirements of each customer. These are managed through a customer-focused development process by our engineering team according to the specifications of each customer. With this option, our customers can get reports on accounts payable, accounts receivable, supply information (purchase order flow, receipts and invoices sent by suppliers), inventory details, and more. We have more than 50 customers who have worked with us on tailor-made dashboards. With the broad range of functionalities within QuickSight, we can offer many data visualization options to our customers.

Empowering our own operations

Beyond using QuickSight to serve our customers, we also use QuickSight for our own BI reporting. So far, we have generated more than 80 dashboards to analyze different business flows. For example, we monitor daily sales in specific services, accounting, software as a service (SaaS) metrics, and the operation of our customers. We do all of this from within our own web application, with the power of QuickSight, giving us the opportunity to experience the interface just like our customers do. In 2023, one of our top goals is to provide a 360-degree view of Defontana using QuickSight.


QuickSight has enabled us to seamlessly embed analytics into our ERP service, providing valuable insights to our SMB customers. We have been able to cut costs and continue to grow throughout Latin America. We plan to use QuickSight even more within our own organization, making us more data-driven. QuickSight will empower us to democratize the information that our own employees receive, establish better processes, and create more tools to analyze customer information for behavioral patterns, which we can use to better meet our customers’ needs.

To learn more about how you can embed customized data visuals and interactive dashboards into any application, visit Amazon QuickSight Embedded.

About the authors

Cynthia Valeriano is a Business Intelligence Developer of at Defontana, with skills focused on data analysis and visualization. With 3 years of experience in administrative areas and 2 years of experience in business intelligence projects, she has been in charge of implementing data migration and transformation tasks with various AWS tools, such as AWS DMS and AWS Glue, in addition to generating multiple dashboards in Amazon QuickSight.

Jaime Olivares is a Senior software developer at Defontana, with 6 years of experience in the development of various technologies focused on the analysis of solutions and customer requirements. Experience with AWS in various services, including product development through QuickSight for the analysis of business and accounting data.

Guillermo Puelles is a Technical Manager of the “Appia” Integrations team at Defontana, with 9 years of experience in software development and 5 years working with AWS tools. Responsible for planning and managing various projects for the implementation of BI solutions through QuickSight and other AWS services.

Softbrain provides advanced analytics to sales customers with Amazon QuickSight

Post Syndicated from Kenta Oda original https://aws.amazon.com/blogs/big-data/softbrain-provides-advanced-analytics-to-sales-customers-with-amazon-quicksight/

This is a guest post by Kenta Oda from SOFTBRAIN Co., Ltd.

Softbrain is a leading Japanese producer of software for sales force automation (SFA) and customer relationship management (CRM). Our main product, e-Sales Manager (eSM), is an SFA/CRM tool that provides sales support to over 5,500 companies in Japan. We provide our sales customers with a one-stop source for information and visualization of sales activity, improving their efficiency and agility, which leads to greater business opportunity.

With increasing demand from our customers for analyzing data from different angles throughout the sales process, we needed an embedded analytics tool. We chose Amazon QuickSight, a cloud-native business intelligence (BI) tool that allows you to embed insightful analytics into any application with customized, interactive visuals and dashboards. It integrates seamlessly with eSM and is easy to use at a low cost.

In this post, we discuss how QuickSight is helping us provide our sales customers with the insights they need, and why we consider this business decision a win for Softbrain.

There were four things we were looking for in an embedded analytics solution:

  • Rich visualization – With our previous solution, which was built in-house, there were only four types of visuals, so it was difficult to combine multiple graphs for an in-depth analysis.
  • Development speed – We needed to be able to quickly implement BI functionalities. QuickSight requires minimal development due to its serverless architecture, embedding, and API.
  • Cost – We moved from Tableau to QuickSight because QuickSight allowed us to provide data analysis and visualizations to our customers at a competitive price—ensuring that more of our customers can afford it.
  • Ease of use – QuickSight is cloud-based and has an intuitive UX for our sales customers to work with.

Innovating with QuickSight

Individual productivity must be greatly improved to keep up with the shifting labor market in Japan. At Softbrain, we aim to innovate using the latest technology to provide science-based insights into customer and sales interactions, enabling those who use eSM to be much more productive. Sales reps and managers are able to make informed decisions.

By using QuickSight as our embedded analytics solution, we can offer data visualizations at a much lower price point, making it much more accessible for our customers than we could with other BI solutions. When we combine the process management system offered by eSM with the intuitive user experience and rich visualization capability of QuickSight, we empower customers to understand their sales data, which sits in Amazon Simple Storage Service (Amazon S3) and Amazon Aurora, and act on it.

Seamless console embedding

What sets QuickSight apart from other BI tools is console embedding, which means our customers have the ability to build their own dashboards within eSM. They can choose which visualizations they want and take an in-depth look at their data. Sales strategy requires agility, and our customers need more than a fixed dashboard. QuickSight offers freedom and flexibility with console embedding.

Console embedding allows eSM to be a one-stop source for all the information sales reps and managers need. They can access all the analyses they need to make decisions right from their web browser because QuickSight is fully managed and serverless. With other BI solutions, the user would need to have the client application installed on their computer to create their own dashboards.

Empowering our sales customers

With insights from QuickSight embedded into eSM, sales reps can analyze the gap between their budget and actual revenue to build an action plan to fill the gap. They can use their dashboards to analyze data on a weekly and monthly basis. They can share this information at meetings and explore the data to figure out why there might be low attainment for certain customers. Our customers can use eSM and QuickSight to understand why win or loss opportunities are increasing. Managers can analyze and compare the performance of their sales reps to learn what high-performing reps are doing and help low performers. Sales reps can also evaluate their own performance.

Driving 95% customer retention rate

All of these insights come from putting sales data into eSM and QuickSight. It’s no secret that our customers love QuickSight. We can boast a 95% customer retention rate and offer QuickSight as an embedded BI solution at largest scale in Japan.

To learn more about how you can embed customized data visuals and interactive dashboards into any application, visit Amazon QuickSight Embedded.

About the author

Kenta Oda is the Chief Technology Officer at SOFTBRAIN Co., Ltd. He is in responsible of new product development with keen insight on better customer experience and go-to-market strategy.

Improve power utility operational efficiency using smart sensor data and Amazon QuickSight

Post Syndicated from Bin Qiu original https://aws.amazon.com/blogs/big-data/improve-power-utility-operational-efficiency-using-smart-sensor-data-and-amazon-quicksight/

This blog post is co-written with Steve Alexander at PG&E.

In today’s rapidly changing energy landscape, power disturbances cause businesses millions of dollars due to service interruptions and power quality issues. Large utility territories make it difficult to detect and locate faults when power outages occur, leading to longer restoration times, recurring outages, and unhappy customers. Although it’s complex and expensive to modernize distribution networks, many utilities choose to use their capital through the application of smart sensor technologies. These smart sensors are installed in selected locations on distribution networks to monitor various disturbances, such as momentary and permanent outages, line disturbances, voltage sags and surges. The sensors provide analysts with fault waveforms and alerts in addition to graphical representation of regular loads. Different communication infrastructure types such as mesh network and cellular can be used to send load information on a pre-defined schedule or event data in real time to the backend servers residing in the utility UDN (Utility Data Network).

In this series of posts, we walk you through how we use Amazon QuickSight, a serverless, fully managed, business intelligence (BI) service that enables data-driven decision making at scale. QuickSight meets varying analytics needs with modern interactive dashboards, paginated reports, natural language queries, ML-insights, and embedded analytics, from one unified service.

In this first post of the series, we show you how data collected from smart sensors is used for building automated dashboards using QuickSight to help distribution network engineers manage, maintain and troubleshoot smart sensors and perform advanced analytics to support business decision making.

Current challenges in power utility operations

To have a comprehensive monitoring coverage of the distribution networks, utilities normally deploy hundreds, if not thousands, of smart sensors. Similar to any other equipment or device, smart sensors could encounter different issues, such as having defective parts, wearing out over time, becoming obsolete due to technological advances, or suffering loss of communication due to power outages or low cellular signal coverage. Managing such a large number of devices can be challenging.

Furthermore, based on the use case, utilities normally apply sensor technologies from different vendors. Solutions from different vendors can vary, such as data protocols, formats, native connectors, and communication media, which further increases the complexity of managing these smart sensors.

To effectively solve smart sensor management issues and improve operational efficiency, distribution engineers need a BI application that is simple to use and has a powerful data processing and analytics engine. QuickSight provides an ideal solution to meet these business needs.

Solution overview

The following highly simplified architectural diagram illustrates the smart sensor data collection and processing. Smart sensors send data via cellular communication based on a predefined schedule or triggered by real-time events. Data collection and processing are handled by a third-party smart sensor manufacturer application residing in Amazon Virtual Private Cloud (Amazon VPC) private subnets behind a Network Load Balancer. Amazon Kinesis Data Streams interacts with the third-party application through a native connection and conducts necessary data transformation in real time, and Amazon Kinesis Data Firehose stores the data in Amazon Simple Storage Service (Amazon S3) buckets. The AWS Glue Data Catalog contains the table definitions for the smart sensor data sources stored in the S3 buckets. Amazon Athena runs queries using a variety of SQL statements on data stored in Amazon S3, and QuickSight is used for business intelligence and data visualization.

After the smart sensor’s data is collected and stored in Amazon S3 and is accessible via Athena, we can focus on building the following QuickSight dashboards for distribution network engineers:

  • Sensor status dashboard – Analyze and monitor the status of smart sensors
  • Distribution network events dashboard – Analyze the operational information of the distribution networks


This solution requires an active AWS account with the permission to create and modify AWS Identity and Access Management (IAM) roles along with the following services enabled:

  • Athena
  • AWS Glue
  • Kinesis Data Firehose
  • Kinesis Data Streams
  • Network Load Balancer
  • QuickSight
  • Amazon S3
  • Amazon VPC

Additionally, data collection and data processing are functional blocks of the third-party smart sensor manufacturer application. The smart sensor application solution must be already deployed in the same AWS account and Region that you will use for the dashboards.

This solution uses QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine) storage to improve dashboard performance.

Sensor status dashboard

When hundreds or thousands of line sensors are installed, it’s critical for distribution engineers to understand the status of all smart sensors on a regular basis and fix issues to ensure smart sensors provide real-time information for operator decision-making. Assuming a utility has 5,000 smart sensors installed, even if only 1% of the sensors have communication issues (a realistic scenario based on utility experience), distribution engineers need to check and troubleshoot 50 sensors per day on average. The smart sensor communication losses could be caused by low cellular signal strength, low power supply, or planned or unplanned outages. If it takes 10 minutes to analyze one sensor, it will cause the engineering team around 500 minutes per day just to analyze the questionable smart sensors.

Rather than checking smart sensor information from different applications or systems to find answers, a sensor status dashboard solves this problem by aggregating status statistics across all sensors by different attributes, including sensor location, communication status, and distributions in different regions, substations, and circuits.

In the following sensor status dashboard, a hypothetical utility has 102 smart sensors (each location needs three sensors for phases A, B, and C) deployed in five substations and six circuits. During normal operations, smart sensor reports load data every 5–15 minutes, and the event data (different fault events) could come at any time depending on the circuit situation.

Multiple panes are designed to help distribution engineers answer critical questions on smart sensors and facilitate troubleshooting in case communication issues happen to smart sensors:

  • Summary – The top summary pane provides a quick glance of the smart sensor statistics, such as number of substations, circuits, smart sensors with good communications, or smart sensors that have communication issues.
  • Smart Sensor Status By Location – This pane shows the geographical distributions of all the smart sensors. Different colors are used to demonstrate smart sensor operational status. In this case, four of the sensors have communication issues, which are shown in red on the map. The operator can identify the questionable sensors, zoom in, and determine the actual location of these sensors. When operators pick up the questionable smart sensors, the geo-map can auto focus on these smart sensors as well.
  • Sensor Status By Substation and Circuit – This pane gives operators a glance of smart sensors by substation and circuit, such as number of healthy smart sensors and number of sensors with communication issues.
  • Unhealthy Sensor Details – This pane provides information about questionable smart sensor data.
  • Cellular Communication Signal Strength Distribution – Smart sensors transmit data to the cloud using cellular communication. If the signal strength is lower than -100 dBm to -109 dBm (considered poor signal of 1 to 2 bars), the signal might be too weak for the sensor to transmit data. Distribution lines provide power to the smart sensors. If the line current is lower than 5-10 Amps, the sensor may not have enough power to transmit data as well. Therefore, cellular communication strength and circuit loads provide critical information for operators to narrow down the potential root causes of the smart sensor communication loss issues. The Cellular Communication Signal Strength Distribution pane provides this information. Red dots represent smart sensors with either very low signal strength or very low circuit load, orange dots show moderate signal strength and circuit load, and green dots are the sensors with strong signal strength as well as large circuit load.
  • Smart Sensor Health Status Trend – Although real-time information is important to understand the smart sensors’ status live, it’s critical to learn the health trend of smart sensors as well. The Smart Sensor Health Status Trend pane provides a pattern showing whether the overall operations of the smart sensor are better or worse by week or day. Operators can choose the time range, substation, or circuit to learn more granular information.
  • Sensor Distribution by Substation and Sensor Distribution by Circuit – These panes help the operator learn the smart sensor deployment distribution information.
  • Smart Sensor List – This sensor detail pane provides comprehensive information of the smart sensors in a tabular view in case the operator wants to search or sort sensors by detail information.

With aggregated smart sensor data (geo location, cellular signal strength, distributed circuit power flow), operators can quickly identify problematic sensors and narrow down the possible root causes. This approach can save a significant amount of time performing sensor maintenance and troubleshooting—up to 90% or more.

In future posts in this series, we’ll show you how to use the paginated reports function to generate daily reports to improve the operational efficiency even more. The communication pane also shows the smart sensor distribution using a bar chart, and provides insights of smart sensor deployment information based on region, division, substation, and circuit.

Distribution network events dashboard

Smart sensors measure and provide the operational information of the distribution networks. This information is critical for operators to understand the circuit running status and the distribution of different events, such as permanent outages, momentary outages, line disturbance, or voltage sags and swells. QuickSight helps operators quickly configure different views, insights, and calculations on smart sensor information.

When an operator specifies a time range, QuickSight is able to provide smart sensor statistics on various metrics, such as the following:

  • Total number of events compared to a previous time frame
  • Distribution of events across selected regions, substations, or circuits
  • Distribution of events by region, substation, or circuit
  • Distribution of events by event type such as permanent or momentary faults

This information can help operators determine the areas or fault types of interest and study more detailed information. It can also help operators identify the substations or circuits with the most events and take proactive actions to fix any existing or hidden issues. The trend information can also be used to validate the equipment repair or circuit enhancement works.


Many utilities today are experiencing increased integration of distributed energy resources (DERs), such as solar photovoltaic, and power electronics loads such as variable speed drive and electric vehicle battery chargers. However, the existing grid wasn’t originally designed to coordinate these DERs, which can cause hidden issues on the existing networks. A large number of smart sensors are widely used to monitor the distribution networks to improve grid resiliency and stability.

In this post, we showed how QuickSight can help power utility distribution network engineers or operators to visualize smart sensor status in real time and troubleshoot smart sensor issues. We discussed out-of-the-box QuickSight features such as its rich suite of visualizations, analytical functions and calculations, in-memory data engine, and scalability, which will greatly reduce the time, cost, and effort of managing large number of smart sensors and fixing any problems early.

Smart sensors are the eyes and ears of utility distribution networks. With QuickSight BI functions, operators can quickly and easily create circuit event dashboards; search, sort, filter, and analyze different mission-critical events; and help engineers take early action when certain abnormalities occur on the distribution networks.

In the following posts in this series, we’ll show you how to use QuickSight to generate daily paginated reports and use advanced features such as natural language processing to conduct advanced search and analytics functions.

About the Authors

Bin Qiu is a Global Partner Solutions Architect focusing on ER&I at AWS. He has more than 20 years’ experience in the energy and power industries, designing, leading, and building different smart grid projects, such as distributed energy resources, microgrid, AI/ML implementation for resource optimization, IoT smart sensor application for equipment predictive maintenance, EV car and grid integration, and more. Bin is passionate about helping utilities achieve digital and sustainability transformations.

Steve Alexander is a Senior Manager, IT Products at PG&E. He leads product teams building wildfire prevention and risk mitigation data products. Recent work has been focused on integrating data from various sources including weather, asset data, sensors, and dynamic protective devices to improve situational awareness and decision-making. Steve has over 20 years of experience with data systems and cutting-edge IT research and development, and is passionate about applying creative thinking in technical domains.

Karthik Tharmarajan is a Senior Specialist Solutions Architect for Amazon QuickSight. Karthik has over 15 years of experience implementing enterprise business intelligence (BI) solutions and specializes in integration of BI solutions with business applications and enabling data-driven decisions.

Ranjan Banerji is a Principal Partner Solutions Architect at AWS focused on the power and utilities vertical. Ranjan has been at AWS for 5 years, first on the department of defense (DoD) team helping the branches of the DoD migrate and/or build new systems on AWS ensuring security and compliance requirements and now supporting the power and utilities team. Ranjan’s expertise ranges from server less architecture to security and compliance for regulated industries. Ranjan has over 25 years of experience building and designing systems for the DoD, federal agencies, energy, and financial industry.

Perform secure database write-backs with Amazon QuickSight

Post Syndicated from Srikanth Baheti original https://aws.amazon.com/blogs/big-data/perform-secure-database-write-backs-with-amazon-quicksight/

Amazon QuickSight is a scalable, serverless, machine learning (ML)-powered business intelligence (BI) solution that makes it easy to connect to your data, create interactive dashboards, get access to ML-enabled insights, and share visuals and dashboards with tens of thousands of internal and external users, either within QuickSight itself or embedded into any application.

A write-back is the ability to update a data mart, data warehouse, or any other database backend from within BI dashboards and analyze the updated data in near-real time within the dashboard itself. In this post, we show how to perform secure database write-backs with QuickSight.

Use case overview

To demonstrate how to enable a write-back capability with QuickSight, let’s consider a fictional company, AnyCompany Inc. AnyCompany is a professional services firm that specializes in providing workforce solutions to their customers. AnyCompany determined that running workloads in the cloud to support its growing global business needs is a competitive advantage and uses the cloud to host all its workloads. AnyCompany decided to enhance the way its branches provide quotes to its customers. Currently, the branches generate customer quotes manually, and as a first step in this innovation journey, AnyCompany is looking to develop an enterprise solution for customer quote generation with the capability to dynamically apply local pricing data at the time of quote generation.

AnyCompany currently uses Amazon Redshift as their enterprise data warehouse platform and QuickSight as their BI solution.

Building a new solution comes with the following challenges:

  • AnyCompany wants a solution that is easy to build and maintain, and they don’t want to invest in building a separate user interface.
  • AnyCompany wants to extend the capabilities of their existing QuickSight BI dashboard to also enable quote generation and quote acceptance. This will simplify feature rollouts because their employees already use QuickSight dashboards and enjoy the easy-to-use interface that QuickSight provides.
  • AnyCompany wants to store the quote negotiation history that includes generated, reviewed, and accepted quotes.
  • AnyCompany wants to build a new dashboard with quote history data for analysis and business insights.

This post goes through the steps to enable write-back functionality to Amazon Redshift from QuickSight. Note that the traditional BI tools are read-only with little to no options to update source data.

Solution overview

This solution uses the following AWS services:

  • Amazon API Gateway – Hosts and secures the write-back REST API that will be invoked by QuickSight
  • AWS Lambda – Runs the compute function required to generate the hash and a second function to securely perform the write-back
  • Amazon QuickSight – Offers BI dashboards and quote generation capabilities
  • Amazon Redshift – Stores quotes, prices, and other relevant datasets
  • AWS Secrets Manager – Stores and manages keys to sign hashes (message digest)

Although this solution uses Amazon Redshift as the data store, a similar approach can be implemented with any database that supports creating user-defined functions (UDFs) that can invoke Lambda.

The following figure shows the workflow to perform write-backs from QuickSight.

The first step in the solution is to generate a hash or a message digest of the set of attributes in Amazon Redshift by invoking a Lambda function. This step prevents request tampering. To generate a hash, Amazon Redshift invokes a scalar Lambda UDF. The hashing mechanism used here is the popular BLAKE2 function (available in the Python library hashlib). To further secure the hash, keyed hashing is used, which is a faster and simpler alternative to hash-based message authentication code (HMAC). This key is generated and stored by Secrets Manager and should be accessible only to allowed applications. After the secure hash is generated, it’s returned to Amazon Redshift and combined in an Amazon Redshift view.

Writing the generated quote back to Amazon Redshift is performed by the write-back Lambda function, and an API Gateway REST API endpoint is created to secure and pass requests to the write-back function. The write-back function performs the following actions:

  1. Generate the hash based on the API input parameters received from QuickSight.
  2. Sign the hash by applying the key from Secrets Manager.
  3. Compare the generated hash with the hash received from the input parameters using the compare_digest method available in the HMAC module.
  4. Upon successful validation, write the record to the quote submission table in Amazon Redshift.

The following section provide detailed steps with sample payloads and code snippets.

Generate the hash

The hash is generated using a Lambda UDF in Amazon Redshift. Additionally, a Secrets Manager key is used to sign the hash. To create the hash, complete the following steps:

  1. Create the Secrets Manager key from the AWS Command Line Interface (AWS CLI):
aws secretsmanager create-secret --name “name_of_secret” --description "Secret key to sign hash" --secret-string '{" name_of_key ":"value"}' --region us-east-1
  1. Create a Lambda UDF to generate a hash for encryption:
import boto3	
import base64
import json
from hashlib import blake2b
from botocore.exceptions import ClientError

def get_secret(): 	#This key is used by the Lambda function to further secure the hash.

    secret_name = "<name_of_secret>"
    region_name = "<aws_region_name>"

    # Create a Secrets Manager client
    session = boto3.session.Session()
    client = session.client(service_name='secretsmanager', region_name=<aws_region_name>    )

    # In this sample we only handle the specific exceptions for the 'GetSecretValue' API.
    # See https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html
    # We rethrow the exception by default.

        get_secret_value_response = client.get_secret_value(SecretId=secret_name)
    except Exception as e:
            raise e

   if "SecretString" in get_secret_value_response:
       access_token = get_secret_value_response["SecretString"]
       access_token = get_secret_value_response["SecretBinary"]

   return json.loads(access_token)[<token key name>]

SECRET_KEY = get_secret()

def sign(payload):
    h = blake2b(digest_size=AUTH_SIZE, key=SECRET_KEY)
    return h.hexdigest().encode('utf-8')

def lambda_handler(event, context):
ret = dict()
  res = []
  for argument in event['arguments']:
     msg = json.dumps(argument)
     signed_key = sign(str.encode(msg))
   ret['success'] = True
   ret['results'] = res
except Exception as e:
  ret['success'] = False
  ret['error_msg'] = str(e)
 return json.dumps(ret)
  1. Define an Amazon Redshift UDF to call the Lambda function to create a hash:
CREATE OR REPLACE EXTERNAL FUNCTION udf_get_digest (par1 varchar)
LAMBDA 'redshift_get_digest'
IAM_ROLE 'arn:aws:iam::<AWSACCOUNTID>role/service-role/<role_name>';

The AWS Identity and Access Management (IAM) role in the preceding step should have the following policy attached to be able to invoke the Lambda function:

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": "lambda:InvokeFunction",
            "Resource": "arn:aws:lambda:us-east-1:<AWSACCOUNTID>1:function:redshift_get_digest"
  1. Fetch the key from Secrets Manager.

This key is used by the Lambda function to further secure the hash. This is indicated in the get_secret function in Step 2.

Set up Amazon Redshift datasets in QuickSight

The quote generation dashboard uses the following Amazon Redshift view.

Create an Amazon Redshift view that uses all the preceding columns along with the hash column:

create view quote_gen_vw as select *, udf_get_digest 
( customername || BGCheckRequired || Skill|| Shift ||State ||Cost ) from billing_input_tbl

The records will look like the following screenshot.

The preceding view will be used as the QuickSight dataset to generate quotes. A QuickSight analysis will be created using the dataset. For near-real-time analysis, you can use QuickSight direct query mode.

Create API Gateway resources

The write-back operation is initiated by QuickSight invoking an API Gateway resource, which invokes the Lambda write-back function. As a prerequisite for creating the calculated field in QuickSight to call the write-back API, you must first create these resources.

API Gateway secures and invokes the write-back Lambda function with the parameters created as URL query string parameters with mapping templates. The mapping parameters can be avoided by using the Lambda proxy integration.

Create a REST API resource of method type GET that uses Lambda functions (created in the next step) as the integration type. For instructions, refer to Creating a REST API in Amazon API Gateway and Set up Lambda integrations in API Gateway.

The following screenshot shows the details for creating a query string parameter for each parameter passed to API Gateway.

The following screenshot shows the details for creating a mapping template parameter for each parameter passed to API Gateway.

Create the Lambda function

Create a new Lambda function for the API Gateway to invoke. The Lambda function performs the following steps:

  1. Receive parameters from QuickSight through API Gateway and hash the concatenated parameters.

The following code example retrieves parameters from the API Gateway call using the event object of the Lambda function:

   customer= event['customer’])
    bgc = event['bgc']

The function performs the hashing logic as shown in the create hash step earlier using the concatenated parameters passed by QuickSight.

  1. Compare the hashed output with the hash parameter.

If these don’t match, the write-back won’t happen.

  1. If the hashes match, perform a write-back. Check for the presence of a record in the quote generation table by generating a query from the table using the parameters passed from QuickSight:
query_str = "select * From tbquote where cust = '" + cust + "' and bgc = '" + bgc +"'" +" and skilledtrades = '" + skilledtrades + "'  and shift = '" +shift + "' and jobdutydescription ='" + jobdutydescription + "'"
  1. Complete the following action based on the results of the query:
    1. If no record exists for the preceding combination, generate and run an insert query using all parameters with the status as generated.
    2. If a record exists for the preceding combination, generate and run an insert query with the status as in review. The quote_Id for the existing combination will be reused.

Create a QuickSight visual

This step involves creating a table visual that uses a calculated field to pass parameters to API Gateway and invoke the preceding Lambda function.

  1. Add a QuickSight calculated field named Generate Quote to hold the API Gateway hosted URL that will be triggered to write back the quote history into Amazon Redshift:
  1. Create a QuickSight table visual.
  2. Add required fields such as Customer, Skill, and Cost.
  3. Add the Generate Quote calculated field and style this as a hyperlink.

Choosing this link will write the record into Amazon Redshift. This is incumbent on the same hash value returning when the Lambda function performs the hash on the parameters.

The following screenshot shows a sample table visual.

Write to the Amazon Redshift database

The Secrets Manager key is fetched and used by the Lambda function to generate the hash for comparison. The write-back will be performed only if the hash matches with the hash passed in the parameter.

The following Amazon Redshift table will capture the quote history as populated by the Lambda function. Records in green represent the most recent records for the quote.

Considerations and next steps

Using secure hashes prevents the tampering of payload parameters that are visible in the browser window when the write-back URL is invoked. To further secure the write-back URL, you can employ the following techniques:

  • Deploy the REST API in a private VPC that is accessible only to QuickSight users.
  • To prevent replay attacks, a timestamp can be generated alongside the hashing function and passed as an additional parameter in the write-back URL. The backend Lambda function can then be modified to only allow write-backs within a certain time-based threshold.
  • Follow the API Gateway access control and security best practices.
  • Mitigate potential Denial of Service for public-facing APIs.

You can further enhance this solution to render a web-based form when the write-back URL is opened. This could be implemented by dynamically generating an HTML form in the backend Lambda function to support the input of additional information. If your workload requires a high number of write-backs that require higher throughput or concurrency, a purpose-built data store like Amazon Aurora PostgreSQL-Compatible Edition might be a better choice. For more information, refer to Invoking an AWS Lambda function from an Aurora PostgreSQL DB cluster. These updates can then be synchronized into Amazon Redshift tables using federated queries.


This post showed how to use QuickSight along with Lambda, API Gateway, Secrets Manager, and Amazon Redshift to capture user input data and securely update your Amazon Redshift data warehouse without leaving your QuickSight BI environment. This solution eliminates the need to create an external application or user interface for database update or insert operations, and reduces related development and maintenance overhead. The API Gateway call can also be secured using a key or token to ensure only calls originating from QuickSight are accepted by the API Gateway. This will be covered in subsequent posts.

About the Authors

Srikanth Baheti is a Specialized World Wide Principal Solutions Architect for Amazon QuickSight. He started his career as a consultant and worked for multiple private and government organizations. Later he worked for PerkinElmer Health and Sciences & eResearch Technology Inc, where he was responsible for designing and developing high traffic web applications, highly scalable and maintainable data pipelines for reporting platforms using AWS services and Serverless computing.

Raji Sivasubramaniam is a Sr. Solutions Architect at AWS, focusing on Analytics. Raji is specialized in architecting end-to-end Enterprise Data Management, Business Intelligence and Analytics solutions for Fortune 500 and Fortune 100 companies across the globe. She has in-depth experience in integrated healthcare data and analytics with wide variety of healthcare datasets including managed market, physician targeting and patient analytics.

New scatter plot options in Amazon QuickSight to visualize your data

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/new-scatter-plot-options-in-amazon-quicksight-to-visualize-your-data/

Are you looking to understand the relationships between two numerical variables? Scatter plots are a powerful visual type that allow you to identify patterns, outliers, and strength of relationships between variables. In this post, we walk you through the newly launched scatter plot features in Amazon QuickSight, which will help you take your correlation analysis to the next level.

Feature overview

The scatter plot is undoubtedly one of the most effective visualizations for correlation analysis, helping to identify patterns, outliers, and the strength of the relationship between two or three variables (using a bubble chart). We have improved the performance and versatility of our scatter plots, supporting five additional use cases. The following functionalities have been added in this release:

  • Display unaggregated values – Previously, when there was no field placed on Color, QuickSight displayed unaggregated values, and when a field was placed on Color, the metrics would be aggregated and grouped by that dimension. Now, you can choose to plot unaggregated values even if you’re using a field on Color by using the new aggregate option called None from the field menu, in addition to aggregation options like Sum, Min, and Max. If one value is set to be aggregated, the other value will be automatically set as aggregated, and the same applies to unaggregated scenarios. Mixed aggregation scenarios are not supported, meaning that one value can’t be set as aggregated while the other is unaggregated. It’s worth noting that the unaggregated scenario (the None option) is only supported for numerical values, whereas categorical values (like dates and dimensions) will only display aggregate values such as Count and Count distinct.
  • Support for an additional Label field – We’re introducing a new field well called Label alongside the existing Color field. This will allow you to color by one field and label by another, providing more flexibility in data visualization.
  • Faster load time – The load time is up to six times faster, which impacts both new and existing use cases. Upon launch, you’ll notice that scatter plots render noticeably faster, especially when dealing with larger datasets.

Explore advanced scatter plot use cases

You can choose to set both X and Y values to either aggregated or unaggregated (the None option) from the X and Y axis field menus. This will define if values will be aggregated by dimensions in the Color and Label field wells or not. To get started, add the required fields and choose the appropriate aggregation based on your use case.

Unaggregated use cases

The following screenshot shows an example of unaggregated X and Y value with Color.

The following screenshot shows an example of unaggregated X and Y with Label.

The following screenshot shows an example of unaggregated X and Y with Color and Label.

Aggregated use cases

The following screenshot shows an example of X and Y aggregated by Color.

The following screenshot shows an example of X and Y aggregated by Label.

The following screenshot shows an example of X and Y aggregated by Color and Label.


In summary, our enhanced scatter plots offer users greater performance and versatility, catering to a wider range of use cases than before. The ability to display unaggregated values and support for additional label fields gives users the flexibility they need to visualize the data they want. For further details, refer to Amazon QuickSight Scatterplot. Try out the new scatter plot updates and let us know your feedback in the comments section.

About the authors

Bhupinder Chadha is a senior product manager for Amazon QuickSight focused on visualization and front end experiences. He is passionate about BI, data visualization and low-code/no-code experiences. Prior to QuickSight he was the lead product manager for Inforiver, responsible for building a enterprise BI product from ground up. Bhupinder started his career in presales, followed by a small gig in consulting and then PM for xViz, an add on visualization product.

Build an analytics pipeline for a multi-account support case dashboard

Post Syndicated from Sindhura Palakodety original https://aws.amazon.com/blogs/big-data/build-an-analytics-pipeline-for-a-multi-account-support-case-dashboard/

As organizations mature in their cloud journey, they have many accounts (even hundreds) that they need to manage. Imagine having to manage support cases for these accounts without a unified dashboard. Administrators have to access each account either by switching roles or with single sign-on (SSO) in order to view and manage support cases.

This post demonstrates how you can build an analytics pipeline to push support cases created in individual member AWS accounts into a central account. We also show you how to build an analytics dashboard to gain visibility and insights on all support cases created in various accounts within your organization.

Overview of solution

In this post, we go through the process to create a pipeline to ingest, store, process, analyze, and visualize AWS support cases. We use the following AWS services as key components:

The following diagram illustrates the architecture.

The central account is the AWS account that you use to centrally manage the support case data.

Member accounts are the AWS accounts where, whenever the support cases are created, the data flows into an S3 bucket in the central account that can be visualized using the QuickSight dashboard in the central account.

To implement this solution, you complete the following high-level steps:

  1. Determine the AWS accounts to use for the central account and member accounts.
  2. Set up permissions for AWS CloudFormation StackSets on the central account and member accounts.
  3. Create resources on the central account using AWS CloudFormation.
  4. Create resources on the member accounts using CloudFormation StackSets.
  5. Open up support cases on the member accounts.
  6. Visualize the data in a QuickSight dashboard in the central account.


Complete the following prerequisite steps:

  1. Create AWS accounts if you haven’t done so already.
  2. Before you get started, make sure that you have a Business or Enterprise support plan for your member accounts.
  3. Sign up for QuickSight if you have never used QuickSight in this account before. To use the forecast capability in QuickSight, sign up for the Enterprise Edition.

Preparation for CloudFormation StackSets

In this section, we go through the steps to set up permissions for StackSets in both the central and member accounts.

Set up permissions for StackSets on the central account

To set up permissions on the central account, complete the following steps:

  1. Sign in to the AWS Management Console of the central account.
  2. Download the administrator role CloudFormation template.
  3. On the AWS CloudFormation console, choose Create stack and With new resources.
  4. Leave the Prepare template setting as default.
  5. For Template source, select Upload a template file.
  6. Choose Choose file and supply the CloudFormation template you downloaded: AWSCloudFormationStackSetAdministrationRole.yml.
  7. Choose Next.
  8. For Stack name, enter StackSetAdministratorRole.
  9. Choose Next.
  10. For Configure stack options, we recommend configuring tags, which are key-value pairs that can help you identify your stacks and the resources they create. For example, enter Owner as the key, and your email address as the value.
  11. We don’t use additional permissions or advanced options, so accept the default values and choose Next.
  12. Review your configuration and select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  13. Choose Create stack.

The stack takes about 30 seconds to complete.

Set up permissions for StackSets on member accounts

Now that we’ve created a StackSet administrator role on the central account, we need to create the StackSet execution role on the member accounts. Perform the following steps on all member accounts:

  1. Sign in to the console on the member account.
  2. Download the execution role CloudFormation template.
  3. On the AWS CloudFormation console, choose Create stack and With new resources.
  4. Leave the Prepare template setting as default.
  5. For Template source, select Upload a template file.
  6. Choose Choose file and supply the CloudFormation template you downloaded: AWSCloudFormationStackSetExecutionRole.yml.
  7. Choose Next.
  8. For Stack name, use StackSetExecutionRole.
  9. For Parameters, enter the 12-digit account ID for the central account.
  10. Choose Next.
  11. For Configure stack options, we recommend configuring tags. For example, enter Owner as the key and your email address as the value.
  12. We don’t use additional permissions or advanced options, so choose Next.

For more information, see Setting AWS CloudFormation stack options.

  1. Review your configuration and select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  2. Choose Create stack.

The stack takes about 30 seconds to complete.

Set up the infrastructure for the central account and member accounts

In this section, we go through the steps to create your resources for both accounts and launch the StackSets.

Create resources on the central account with AWS CloudFormation

To launch the provided CloudFormation template, complete the following steps:

  1. Sign in to the console on the central account.
  2. Choose Launch Stack:
  3. Choose Next.
  4. For Stack name, enter a name. For example, support-case-central-account.
  5. For AWSMemberAccountIDs, enter the member account IDs separated by commas from where support case data is gathered.
  6. For Support Case Raw Data Bucket, enter the S3 bucket in the central account that holds the support case raw data from all member accounts. Note the name of this bucket to use in future steps.
  7. For Support Case Transformed Data Bucket, enter the S3 bucket in central account that holds the support case transformed data. Note the name of this bucket to use in future steps.
  8. Choose Next.
  9. Enter any tags you want to assign to the stack and choose Next.
  10. Select the acknowledgement check boxes and choose Create stack.

The stack takes approximately 5 minutes to complete. Wait until the stack is complete before proceeding to the next steps.

Launch CloudFormation StackSets from the central account

To launch StackSets, complete the following steps:

  1. Sign in to the console on the central account.
  2. On the AWS CloudFormation console, choose StackSets in the navigation pane.
  3. Choose Create StackSet.
  4. Leave the IAM execution role name as AWSCloudFormationStackSetExecutionRole.
  5. If AWS Organizations is enabled, under permissions, select Service-managed permissions.
  6. Leave the Prepare template setting as default.
  7. For Template source, select Amazon S3 URL.
  8. Enter the following Amazon S3 URL under Specify Template: https://aws-blogs-artifacts-public.s3.amazonaws.com/artifacts/BDB-2583/AWS_MemberAccount_SupportCaseDashboard_CF.yaml
  9. Choose Next.
  10. For StackSet name, enter a name. For example, support-case-member-account.
  11. For CentralSupportCaseRawBucket, enter the name of the Support Case Raw Data Bucket created in the central account, which you noted previously.
  12. For CentralAccountID, enter the account ID of the central account.
  13. For Configure StackSet options, we recommend configuring tags.
  14. Leave the rest as default and choose Next.
  15. If AWS Organizations is enabled, in the Set deployment options step, for Deployment targets, you can either choose Deploy to organization or Deploy to organizational units (OU).
    • If you deploy to OUs, you will need to specify the AWS OU ID.
  16. If AWS Organizations is not enabled, on the Set Deployment Options page, under Accounts, select Deploy stacks in accounts.
    • Under Account numbers, enter the 12-digit account IDs for the member accounts as a comma-separated list. For example: 111111111111,222222222222.
  17. Under Specify regions, choose US East (N. Virginia).

Due to the limitation of EventBridge with the AWS Support API, this StackSet has to be deployed only in the US East (N. Virginia) Region.

  1. Optionally, you can change the maximum concurrent accounts to match the number of member accounts, adjust the failure tolerance to at least 1, and choose Region Concurrency to be Parallel to set up resources in parallel on the member accounts.
  2. Review your selections, select the acknowledgement check boxes, and choose Submit.

The operation takes about 2–3 minutes to complete.

Visualize your support cases in QuickSight in the central account

In this section, we go through the steps to visualize your support cases in QuickSight.

Grant QuickSight permissions

To grant QuickSight permissions, complete the following steps:

  1. Sign in to the console on the central account.
  2. On the QuickSight console, on the Admin drop-down menu in top right-hand corner, choose Manage QuickSight.
  3. In the navigation pane, choose Security & permissions.
  4. Under QuickSight access to AWS services, choose Manage.
  5. Select Amazon Athena.
  6. Select Amazon S3 to edit QuickSight access to your S3 buckets.
  7. Select the bucket you specified during stack creation.
  8. Choose Finish.
  9. Choose Save.

Prepare the datasets

To prepare your datasets, complete the following steps:

  1. On the QuickSight console, choose Datasets in the navigation pane.
  2. Choose New dataset.
  3. Choose Athena.
  4. For Data source name, enter support-case-data-source.
  5. Choose Validate connection.
  6. After your connection is validated, choose Create data source.
  7. For Database, choose support-case-transformed-data.
  8. For Tables, select the table under the database (there should only be one table that matches the name of the S3 bucket you set as the destination for the transformed data).
  9. Choose Edit/Preview data.
  10. Leave Query mode set as Direct Query.
  11. Choose the options menu (three dots) next to the field case_creation_year and set Change data type to Date.
  12. Enter the date format as yyyy, then choose Validate and Update.
  13. Similarly, right-click on the field case_creation_month and set Change data type to Date.
  14. Enter the date format as MM, then choose Validate and Update.
  15. Right-click on the field case_creation_day and set Change data type to Date.
  16. Enter the date format as dd, then choose Validate and Update.
  17. Right-click on the field case_creation_time and set Change data type to Date.
  18. Enter the date format as yyyy-MM-dd’T’HH:mm:ss.SSSZ, then choose Validate and Update.
  19. Change the name of the QuickSight dataset to support-cases-dataset.
  20. Choose Save & publish.
  21. Note the dataset ID from the URL (alpha-numeric string between datasets and view, excluding slashes) to use later for QuickSight dashboard creation.

  1. Choose Cancel to exit this page.

Set up the QuickSight dashboard from a template

To set up your QuickSight dashboard, complete the following steps:

  1. Navigate to the following link, then right-click and choose Save As to download the QuickSight dashboard JSON template from the browser.
  2. On the console, choose the user profile drop-down menu.
  3. Choose the copy icon next to the Account ID: field (of the central account).

  1. Open the JSON file with a text editor and replace xxxxx with the account ID. This will be replaced in two places.
  2. Replace yyyyy with the dataset ID that you previously noted.
  3. Replace rrrrr with the Region where you deployed resources in the central account.

To determine the principal (user) to be used for the dashboard creation, you can use AWS CloudShell.

  1. Navigate to CloudShell on the console. Ensure it’s the same Region where your resources are deployed.

  1. Wait until the environment gets created and you see the CloudShell prompt.

  1. Run the following command, providing your account ID (central account) and Region:
    aws quicksight list-users –region <region> --aws-account-id <account-id> --namespace default

  2. From the output, select the value of the ARN field. Replace the value of zzzzz with the ARN.
  3. Optionally, you can change the name of the dashboard by changing the value of the fields in the JSON file:
    • For DashboardId, enter SupportCaseCentralDashboard.
    • For Name, enter SupportCaseCentralDashboard.
  4. Save the changes to the JSON file.

Now we use CloudShell to upload the JSON file provided in the previous step.

  1. On the Actions menu, choose Upload file.

  1. To create the QuickSight dashboard from the JSON template, use the following AWS Command Line Interface (AWS CLI) command and pass the updated JSON file as an argument, providing your Region:
    aws quicksight create-dashboard –region <region> --cli-input-json file://support-case-dashboard-template.json

The output of the command looks similar to the following screenshot.

  1. In case of any issues or if you want to see more details about the dashboard, you can use the following command:
    aws quicksight describe-dashboard --region <region> --aws-account-id <central-account-id> --dashboard-id <DashboardId in screenshot above>

  2. On the QuickSight console, choose Dashboards in the navigation pane.
  3. Choose Support Cases Dashboard.

You should see a dashboard similar to the screenshot shown at the beginning of this post, but there should only be one case.

Add additional member accounts

If you want to add additional member accounts, you need to update the CloudFormation stack that you created earlier on the central account. If you followed our name recommendation, the stack is called support-case-central-account-stack. Add the additional account number in the Member Account IDs parameter.

Next, go to the StackSet in the central account. If you followed our naming recommendation, the StackSet is called support-case-member-account. Select the StackSet and on the Actions menu, choose Add stacks to StackSet. Then follow the same instructions that you followed previously when you created the StackSet.

Monitor support cases created in the central account

So far, our setup will monitor all support cases created in the member accounts that you specified. However, it doesn’t include support cases that you create in the central account. To set up monitoring for the central account, complete the following steps:

  1. Update the CloudFormation stack that you created earlier on the central account. If you followed our name recommendation, the stack is called support-case-central-account-stack. Add the central account ID in the Member Account IDs parameter.
  2. Sign in to the CloudFormation console in the central account.
  3. Choose Launch Stack:
  4. Choose Next.
  5. For Stack name, enter a name. For example, support-case-central-as-member-account.
  6. For CentralAccountIDs, enter the central account ID.
  7. For CentralSupportCaseRawBucket, enter the S3 bucket in the central account that holds the support case raw data from all member accounts.
  8. Choose Next.
  9. Enter any tags you want to assign to the stack and choose Next.
  10. Select the acknowledgement check boxes and choose Create stack.

Clean up

To avoid incurring future charges, delete the resources you created as part of this solution.


Note the following troubleshooting tips:

  • Make sure that you create the CloudFormation stacks and StackSet in the correct accounts: central and member.
  • If you get a permission denied error from Athena on the S3 path (see the following screenshot), review the steps to grant QuickSight permissions.

  • When creating the QuickSight dashboard using the template, if you get an error similar to the following, make sure that you use the ARN value from the output generated by the aws quicksight list-users --region <region> --aws-account-id <account-id> --namespace default command.

An error occurred (InvalidParameterValueException) when calling the CreateDashboard operation: Principal ARN xxxx is not part of the same account yyyy

  • When deleting the stack, if you encounter the DELETE_FAILED error, it means that your S3 bucket is not empty. To fix it, empty the contents of the bucket and try to delete the Stack again.


Congratulations! You have successfully built an analytics pipeline to push support cases created in individual member accounts into a central account. You have also built an analytics dashboard to gain visibility and insights on all support cases created in various accounts. As you start creating support cases in your member accounts, you will be able to view them in a single pane of glass.

With the steps and resources described in this post, you can build your own analytics dashboard to gain visibility and insights on all support cases created in various accounts within your organization.

About the authors

Sindhura Palakodety is a Solutions Architect at AWS. She is passionate about helping customers build enterprise-scale Well-Architected solutions on the AWS platform and specializes in the data analytics domain.

Shu Sia Lukito is a Partner Solutions Architect at AWS. She is on a mission to help AWS partners build successful AWS practices and help their customers accelerate their journey to the cloud. In her spare time, she enjoys spending time with her family and making spicy food.

How Huron built an Amazon QuickSight Asset Catalogue with AWS CDK Based Deployment Pipeline

Post Syndicated from Corey Johnson original https://aws.amazon.com/blogs/big-data/how-huron-built-an-amazon-quicksight-asset-catalogue-with-aws-cdk-based-deployment-pipeline/

This is a guest blog post co-written with Corey Johnson from Huron.

Having an accurate and up-to-date inventory of all technical assets helps an organization ensure it can keep track of all its resources with metadata information such as their assigned oners, last updated date, used by whom, how frequently and more. It helps engineers, analysts and businesses access the most up-to-date release of the software asset that bring accuracy to the decision-making process. By keeping track of this information, organizations will be able to identify technology gaps, refresh cycles, and expire assets as needed for archival.

In addition, an inventory of all assets is one of the foundational elements of an organization that facilitates the security and compliance team to audit the assets for improving privacy, security posture and mitigate risk to ensure the business operations run smoothly. Organizations may have different ways of maintaining an asset inventory, that may be an Excel spreadsheet or a database with a fully automated system to keep it up-to-date, but with a common objective of keeping it accurate. Even if organizations can follow manual approaches to update the inventory records but it is recommended to build automation, so that it is accurate at any point of time.

The DevOps practices which revolutionized software engineering in the last decade have yet to come to the world of Business Intelligence solutions. Business intelligence tools by their nature use a paradigm of UI driven development with code-first practices being secondary or nonexistent. As the need for applications that can leverage the organizations internal and client data increases, the same DevOps practices (BIOps) can drive and delivery quality insights more reliably

In this post, we walk you through a solution that Huron and manage lifecycle for all Amazon QuickSight resources across the organization by collaborating with AWS Data Lab Resident Architect & AWS Professional Services team.

About Huron

Huron is a global professional services firm that collaborates with clients to put possible into practice by creating sound strategies, optimizing operations, accelerating digital transformation, and empowering businesses and their people to own their future. By embracing diverse perspectives, encouraging new ideas, and challenging the status quo, Huron creates sustainable results for the organizations we serve. To help address its clients’ growing cloud needs, Huron is an AWS Partner.

Use Case Overview

Huron’s Business Intelligence use case represents visualizations as a service, where Huron has core set of visualizations and dashboards available as products for its customers. The products exist in different industry verticals (healthcare, education, commercial) with independent development teams. Huron’s consultants leverage the products to provide insights as part of consulting engagements. The insights from the product help Huron’s consultants accelerate their customer’s transformation. As part of its overall suite of offerings, there are product dashboards that are featured in a software application following a standardized development lifecycle. In addition, these product dashboards may be forked for customer-specific customization to support a consulting engagement while still consuming from Huron’s productized data assets and datasets. In the next stage of the cycle, Huron’s consultants experiment with new data sources and insights that in turn fed back into the product dashboards.

When changes are made to a product analysis, challenges arise when a base reference analysis gets updated because of new feature releases or bug fixes, and all the customer visualizations that are created from it also need to be updated. To maintain the integrity of embedded visualizations, all metadata and lineage must be available to the parent application. This access to the metadata supports the need for updating visuals based on changes as well as automating row and column level security ensuring customer data is properly governed.

In addition, few customers request customizations on top of the base visualizations, for which Huron team needs to create a replica of the base reference and then customize it for the customer. These are maintained by Huron’s in the field consultants rather than the product development team. These customer specific visualizations create operational overhead because they require Huron to keep track of new customer specific visualizations and maintain them for future releases when the product visuals change.

Huron leverages Amazon QuickSight for their Business Intelligence (BI) reporting needs, enabling them to embed visualizations at scale with higher efficiency and lower cost. A large attraction for Huron to adopt QuickSight came from the forward-looking API capabilities that enable and set the foundation for a BIOps culture and technical infrastructure. To address the above requirement, Huron Global Product team decided to build a QuickSight Asset Tracker and QuickSight Asset Deployment Pipeline.

The QuickSight Asset tracker serves as a catalogue of all QuickSight resources (datasets, analysis, templates, dashboards etc.) with its interdependent relationship. It will help;

  • Create an inventory of all QuickSight resources across all business units
  • Enable dynamic embedding of visualizations and dashboards based on logged in user
  • Enable dynamic row and column level security on the dashboards and visualizations based on the logged-in user
  • Meet compliance and audit requirements of the organization
  • Maintain the current state of all customer specific QuickSight resources

The solution integrates an AWS CDK based pipeline to deploy QuickSight Assets that:

  • Supports Infrastructure-as-a-code for QuickSight Asset Deployment and enables rollbacks if required.
  • Enables separation of development, staging and production environments using QuickSight folders that reduces the burden of multi-account management of QuickSight resources.
  • Enables a hub-and-spoke model for Data Access in multiple AWS accounts in a data mesh fashion.

QuickSight Asset Tracker and QuickSight Asset Management Pipeline – Architecture Overview

The QuickSight Asset Tracker was built as an independent service, which was deployed in a shared AWS service account that integrated Amazon Aurora Serverless PostgreSQL to store metadata information, AWS Lambda as the serverless compute and Amazon API Gateway to provide the REST API layer.

It also integrated AWS CDK and AWS CloudFormation to deploy the product and customer specific QuickSight resources and keep them in consistent and stable state. The metadata of QuickSight resources, created using either AWS console or the AWS CDK based deployment were maintained in Amazon Aurora database through the QuickSight Asset Tracker REST API service.

The CDK based deployment pipeline is triggered via a CI/CD pipeline which performs the following functions:

  1. Takes the ARN of the QuickSight assets (dataset, analysis, etc.)
  2. Describes the asset and dependent resources (if selected)
  3. Creates a copy of the resource in another environment (in this case a QuickSight folder) using CDK

The solution architecture integrated the following AWS services.

  • Amazon Aurora Serverless integrated as the backend database to store metadata information of all QuickSight resources with customer and product information they are related to.
  • Amazon QuickSight as the BI service using which visualization and dashboards can be created and embedded into the online applications.
  • AWS Lambda as the serverless compute service that gets invoked by online applications using Amazon API Gateway service.
  • Amazon SQS to store customer request messages, so that the AWS CDK based pipeline can read from it for processing.
  • AWS CodeCommit is integrated to store the AWS CDK deployment scripts and AWS CodeBuild, AWS CloudFormation integrated to deploy the AWS resources using an infrastructure as a code approach.
  • AWS CloudTrail is integrated to audit user actions and trigger Amazon EventBridge rules when a QuickSight resource is created, updated or deleted, so that the QuickSight Asset Tracker is up-to-date.
  • Amazon S3 integrated to store metadata information, which is used by AWS CDK based pipeline to deploy the QuickSight resources.
  • AWS LakeFormation enables cross-account data access in support of the QuickSight Data Mesh

The following provides a high-level view of the solution architecture.

Architecture Walkthrough:

The following provides a detailed walkthrough of the above architecture.

  • QuickSight Dataset, Template, Analysis, Dashboard and visualization relationships:
    • Steps 1 to 2 represent QuickSight reference analysis reading data from different data sources that may include Amazon S3, Amazon Athena, Amazon Redshift, Amazon Aurora or any other JDBC based sources.
    • Step 3 represents QuickSight templates being created from reference analysis when a customer specific visualization needs to be created and step 4.1 to 4.2 represents customer analysis and dashboards being created from the templates.
    • Steps 7 to 8 represent QuickSight visualizations getting generated from analysis/dashboard and step 6 represents the customer analysis/dashboard/visualizations referring their own customer datasets.
    • Step 10 represents a new fork being created from the base reference analysis for a specific customer, which will create a new QuickSight template and reference analysis for that customer.
    • Step 9 represents end users accessing QuickSight visualizations.
  • Asset Tracker REST API service:
    • Step 15.2 to 15.4 represents the Asset Tracker service, which is deployed in a shared AWS service account, where Amazon API Gateway provides the REST API layer, which invokes AWS Lambda function to read from or write to backend Aurora database (Aurora Serverless v2 – PostgreSQL engine). The database captures all relationship metadata between QuickSight resources, its owners, assigned customers and products.
  • Online application – QuickSight asset discovery and creation
    • Step 15.1 represents the front-end online application reading QuickSight metadata information from the Asset Tracker service to help customers or end users discover visualizations available and be able to dynamically render based on the user login.
    • Step 11 to 12 represents the online application requesting creation of new QuickSight resources, which pushes requests to Amazon SQS and then AWS Lambda triggers AWS CodeBuild to deploy new QuickSight resources. Step 13.1 and 13.2 represents the CDK based pipeline maintaining the QuickSight resources to keep them in a consistent state. Finally, the AWS CDK stack invokes the Asset Tracker service to update its metadata as represented in step 13.3.
  • Tracking QuickSight resources created outside of the AWS CDK Stack
    • Step 14.1 represents users creating QuickSight resources using the AWS Console and step 14.2 represents that activity getting logged into AWS CloudTrail.
    • Step 14.3 to 14.5 represents triggering EventBridge rule for CloudTrail activities that represents QuickSight resource being created, updated or deleted and then invoke the Asset Tracker REST API to register the QuickSight resource metadata.

Architecture Decisions:

The following are few architecture decisions we took while designing the solution.

  • Choosing Aurora database for Asset Tracker: We have evaluated Amazon Neptune for the Asset Tracker database as most of the metadata information we capture are primarily maintaining relationship between QuickSight resources. But when we looked at the query patterns, we found the query pattern is always just one level deep to find who is the parent of a specific QuickSight resource and that can be solved with a relational database’s Primary Key / Foreign Key relationship and with simple self-join SQL query. Knowing the query pattern does not require a graph database, we decided to go with Amazon Aurora to keep it simple, so that we can avoid introducing a new database technology and can reduce operational overhead of maintaining it. In future as the use case evolve, we can evaluate the need for a Graph database and plan for integrating it. For Amazon Aurora, we choose Amazon Aurora Serverless as the usage pattern is not consistent to reserve a server capacity and the serverless tech stack will help reduce operational overhead.
  • Decoupling Asset Tracker as a common REST API service: The Asset Tracker has future scope to be a centralized metadata layer to keep track of all the QuickSight resources across all business units of Huron. So instead of each business unit having its own metadata database, if we build it as a service and deploy it in a shared AWS service account, then we will get benefit from reduced operational overhead, duplicate infrastructure cost and will be able to get a consolidated view of all assets and their integrations. The service provides the ability of applications to consume metadata about the QuickSight assets and then apply their own mapping of security policies to the assets based on their own application data and access control policies.
  • Central QuickSight account with subfolder for environments: The choice was made to use a central account which reduces developer friction of having multiple accounts with multiple identities, end users having to manage multiple accounts and access to resources. QuickSight folders allow for appropriate permissions for separating “environments”. Furthermore, by using folder-based sharing with QuickSight groups, users with appropriate permissions already have access to the latest versions of QuickSight assets without having to share their individual identities.

The solution included an automated Continuous Integration (CI) and Continuous Deployment (CD) pipeline to deploy the resources from development to staging and then finally to production. The following provides a high-level view of the QuickSight CI/CD deployment strategy.

Aurora Database Tables and Reference Analysis update flow

The following are the database tables integrated to capture the QuickSight resource metadata.

  • QS_Dataset: This captures metadata of all QuickSight datasets that are integrated in the reference analysis or customer analysis. This includes AWS ARN (Amazon Resource Name), data source type, ID and more.
  • QS_Template: This table captures metadata of all QuickSight templates, from which customer analysis and dashboards will be created. This includes AWS ARN, parent reference analysis ID, name, version number and more.
  • QS_Folder: This table captures metadata about QuickSight folders which logically groups different visualizations. This includes AWS ARN, name, and description.
  • QS_Analysis: This table captures metadata of all QuickSight analysis that includes AWS ARN, name, type, dataset IDs, parent template ID, tags, permissions and more.
  • QS_Dashboard: This table captures metadata information of QuickSight dashboards that includes AWS ARN, parent template ID, name, dataset IDs, tags, permissions and more.
  • QS_Folder_Asset_Mapping: This table captures folder to QuickSight asset mapping that includes folder ID, Asset ID, and asset type.

As the solution moves to the next phase of implementation, we plan to introduce additional database tables to capture metadata information about QuickSight sheets and asset mapping to customers and products. We will extend the functionality to support visual based embedding to enable truly integrated customer data experiences where embedded visuals mesh with the native content on a web page.

While explaining the use case, we have highlighted it creates a challenge when a base reference analysis gets updated and we need to track the templates that are inherited from it make sure the change is pushed to the linked customer analysis and dashboards. The following example scenarios explains, how the database tables change when a reference analysis is updated.

Example Scenario: When “reference analysis” is updated with a new release

When a base reference analysis is updated because of a new feature release, then a new QuickSight reference analysis and template needs to be created. Then we need to update all customer analysis and dashboard records to point to the new template ID to form the lineage.

The following sequential steps represent the database changes that needs to happen.

  • Insert a new record to the “Analysis” table to represent the new reference analysis creation.
  • Insert a new record to the “Template” table with new reference analysis ID as parent, created in step 1.
  • Retrieve “Analysis” and “Dashboard” table records that points to previous template ID and then update those records with the new template ID, created in step 2.

How will it enable a more robust embedding experience

The QuickSight asset tracker integration with Huron’s products provide users with a personalized, secure and modern analytics experience. When user’s login through Huron’s online application, it will use logged in user’s information to dynamically identify the products they are mapped to and then render the QuickSight visualizations & dashboards that the user is entitled to see. This will improve user experience, enable granular permission management and will also increase performance.

How AWS collaborated with Huron to help build the solution

AWS team collaborated with Huron team to design and implement the solution. AWS Data Lab Resident Architect collaborated with Huron’s lead architect for initial architecture design that compared different options for integration and deriving tradeoffs between them, before finalizing the final architecture. Then with the help of AWS Professional service engineer, we could build the base solution that can be extended by Huron team to roll it out to all business units and integrate additional reporting features on top of it.

The AWS Data Lab Resident Architect program provides AWS customers with guidance in refining and executing their data strategy and solutions roadmap. Resident Architects are dedicated to customers for 6 months, with opportunities for extension, and help customers (Chief Data Officers, VPs of Data Architecture, and Builders) make informed choices and tradeoffs about accelerating their data and analytics workloads and implementation.

The AWS Professional Services organization is a global team of experts that can help customers realize their desired business outcomes when using the AWS Cloud. The Professional Services team work together with customer’s team and their chosen member of the AWS Partner Network (APN) to execute their enterprise cloud computing initiatives.

Next Steps

Huron has rolled out the solution for one business unit and as a next step we plan to roll it out to all business units, so that the asset tracker service is populated with assets available across all business units of the organization to provide consolidated view.

In addition, Huron will be building a reporting layer on top of the Amazon Aurora asset tracker database, so that the leadership has a way to discover assets by business unit, by owner, created between specific date range or the reports that are not updated since a while.

Once the asset tracker is populated with all QuickSight assets, it will be integrated into the front-end online application that can help end users discover existing assets and request creation of new assets.

Newer QuickSight API’s such as assets-as-a-bundle and assets-as-code further accelerate the capabilities of the service by improving the development velocity and reliability of making changes.


This blog explained how Huron built an Asset Tracker to keep track of all QuickSight resources across the organization. This solution may provide a reference to other organizations who would like to build an inventory of visualization reports, ML models or other technical assets. This solution leveraged Amazon Aurora as the primary database, but if an organization would also like to build a detailed lineage of all the assets to understand how they are interrelated then they can consider integrating Amazon Neptune as an alternate database too.

If you have a similar use case and would like to collaborate with AWS Data Analytics Specialist Architects to brainstorm on the architecture, rapidly prototype it and implement a production ready solution then connect with your AWS Account Manager or AWS Solution Architect to start an engagement with AWS Data Lab team.

About the Authors

Corey Johnson is the Lead Data Architect at Huron, where he leads its data architecture for their Global Products Data and Analytics initiatives.

Sakti Mishra is a Principal Data Analytics Architect at AWS, where he helps customers modernize their data architecture, help define end to end data strategy including data security, accessibility, governance, and more. He is also the author of the book Simplify Big Data Analytics with Amazon EMR. Outside of work, Sakti enjoys learning new technologies, watching movies, and visiting places with family.

How Dafiti made Amazon QuickSight its primary data visualization tool

Post Syndicated from Valdiney Gomes original https://aws.amazon.com/blogs/big-data/how-dafiti-made-amazon-quicksight-its-primary-data-visualization-tool/

This is a guest post by Valdiney Gomes, Hélio Leal, and Flávia Lima from Dafiti.

Data and its various uses is increasingly evident in companies, and each professional has their preferences about which technologies to use to visualize data, which isn’t necessarily in line with the technological needs and infrastructure of a company. At Dafiti, a Brazilian fashion and style e-commerce retailer, it was no different. Five tools were used by different sectors of the company, which caused misalignment and management overhead, spreading our resources thin to support them. Looking for a tool that would enable us to democratize our data, we chose Amazon QuickSight, a cloud-native, serverless business intelligence (BI) service that powers interactive dashboards that lets us make better data-driven decisions, as a corporate solution for data visualization.

In this post, we discuss why we chose QuickSight and how we implemented it.

Why we chose QuickSight

We had specific requirements for our BI solution and looked at many different options. The following factors guided our decision:

  • Tool close to data – It was important to have the data visualization tool as close to the data as possible. At Dafiti, the entire infrastructure is on AWS, and we use Amazon Redshift as our Data Warehouse. QuickSight, when using SPICE (Super-fast, Parallel, In-memory Calculation Engine), extracts data from Amazon Redshift as efficiently as possible using UNLOAD, which optimizes the use of Amazon Redshift.
  • Highly available and accessible solution – We wanted to be able to be access the tool by web or mobile interface, in addition to being able to do almost anything through API calls.
  • Serverless solution – All the other data visualization solutions that were used at Dafiti were on premises, which created unnecessary cost and effort to maintain these services, taking the focus away from what was most important to us: data.
  • Flexible pricing model – We needed a pricing model that would allow us to provide access to everyone in the company and at a price defined by usage and not by license. Thanks to AWS pay-as-you-go pricing, with more than double the number of users we had on our previous main data visualization solution, our cost with QuickSight is about 10 times lower.
  • Robust documentation – The material provided by AWS proved to be helpful, allowing our team to put the project into production.

Unifying our solution

We were previously using Qlikview, Sisense, Tableau, SAP, and Excel to analyze our data across different teams. We were already using other AWS services and learning about QuickSight when we hosted a Data Battle with AWS, a hybrid event for more than 230 Dafiti employees. This event had a hands-on approach with a workshop followed by a friendly QuickSight competition. Participants had to get information in their own dashboard to answer correctly. This 5-hour event flew by, accelerated the learning path of technical and business teams, and proved that QuickSight was the right tool for us.

QuickSight has brought all of our teams into one tool, while lowering costs by 80% and enabling us to do so much more together. Currently, over 400 employees, including our CEO, across nine different business units are using QuickSight as their sole source of truth on a daily basis. This includes human resources, auditing, and customer service, which previously had their analyses spread across several sources.

Data democratization

Data democratization is one of Dafiti’s main objectives. We believe that allowing everyone to analyze the data, following Brazilian, Argentinean, and Colombian privacy laws, unlocks potential for improving decision-making processes by extracting value from the data generated by the company. However, the democratization of data comes with the responsible use of resources. Yes, we want all users to be able to access and extract value from the data, but the cost can never be greater than the value that this generates.

How we organized the project

Data democratization drives Dafiti’s strategy. When implementing QuickSight, the obsession of becoming an even more data-driven company (we talk about this at the AWS Summit SP 2022) and having data increasingly accessible was what guided the project.

We organized QuickSight by folders, as can be seen in the following figure, and each folder represents a business area. This makes it easier to grant access and ensures that all people from the same area have access to exactly the same set of data and reports.

model of Dafiti's QuickSight folders

In this model, people from the corporate data area can view and edit any resource from any area, while customer service users can view and edit resources only for customer service.

Expanding the model a bit, the reports created by one area can be shared with others, as can be seen in the following figure, in which the SAC report was shared with Support, creating what we call a reporting portfolio.

an expansion of the folders

In this way, all users who join any of the groups will have exactly the same view as any of their peers, eliminating privileges in accessing data. In addition, the portfolio is enriched every day with reports that are created and maintained by other areas, but which may be of interest to areas other than the one responsible for creating it.

For this to work correctly, a certain rigidity is necessary in relation to the few naming and documentation standards that have been defined. On the other hand, designers have complete freedom to define the characteristics of their reports.

Another highlight in this model is that no report can be shared directly with a specific user; this restriction was defined using custom permissions in QuickSight. Therefore, the reports are always shared only through the folders. After all, we want the data to be accessible equally to everyone in the company.

Technical configurations

QuickSight offers a comprehensive API, and all the activities we carry out on a daily basis take place through these APIs. Among these activities, we highlight the granting of access and the monitoring of various aspects of the tool.

The QuickSight visual interface allows most of the tool’s maintenance activities to be performed and integration with Active Directory or the use of AWS Identity and Access Management (IAM) users is possible, but we understand that it wouldn’t be the ideal choice to grant access. Therefore, we defined an access grant flow for users and groups based on the QuickSight API, as can be seen in the following figure. In this model, the creation and removal of users is done through a JSON file with the following structure:

   {"UserName":"[email protected]","Active":"True","GroupList":[{"GroupName":"QUICKSIGHT_DATA_EDITOR"}]},
   {"UserName":"[email protected]","Active":"True","GroupList":[{"GroupName":"QUICKSIGHT_SAC_VIEWER"}]},

Whenever a user needs to be added or changed, the file is edited and a pull request is submitted to GitHub. If the request is approved, an action is triggered to send the file to an Amazon Simple Storage Service (Amazon S3) bucket. From this, an AWS Lambda function is triggered that performs two activities: the first is the maintenance of users and groups, and the second is the sending of an invitation through Amazon Simple Email Service (Amazon SES) for users to join QuickSight. In our case, we opted for a personalized invitation model that would emphasize the data democratization initiative that is being conducted.

an architecture diagram from JSON to QuickSight

To monitor the tool, we implemented the architecture shown in the following figure, in which we used AWS CloudTrail to pull out the QuickSight logs and the QuickSight API to extract information from the tool’s resources, such as reports, users, datasets, data sources, and more. All of this data is processed by Glove, our data integration tool, stored in Amazon Redshift, and analyzed in QuickSight itself. This allows us to understand the behavior of our users and concentrate efforts on the most-used resources, in addition to allowing optimal cost control and the use of SPICE.

an architecture diagram from QuickSight to Redshift

To update the datasets, we don’t use the QuickSight internal scheduler, due to the large volume of data and the complexity of the DAGs. We prefer updating the datasets within our ETL (extract, transform, and load) and ELT process orchestration flow. For this purpose, we use Hanger, our orchestration tool. This approach allows the datasets to be updated only when the data source is changed and the data quality processes are executed. This model is represented by the following figure.

an architecture diagram with Redshift, Hanger, and QuickSight API


Choosing a data visualization tool is not a simple task. It involves many considerations, and several aspects must be analyzed in order for the choice to fit the characteristics of the company and to be consistent with the profile of business users.

For Dafiti, QuickSight was a natural choice from the moment we learned about its features. We needed a service that was in the same cloud as our main data sources, extremely fast using SPICE, and solved the maintenance and cost problem of on-premises applications. In terms of functionalities that are necessary for our business, it met our needs perfectly.

Do you want to know more about what we are doing in the data area here at Dafiti? Check out the following videos:

About the Authors

Valdiney Gomes is Data Engineering Coordinator at Dafiti. He worked for many years in software engineering, migrated to data engineering, and currently leads an amazing team responsible for the data platform for Dafiti in Latin America.

Hélio Leal is a Data Engineering Specialist at Dafiti, responsible for maintaining and evolving the entire data platform at Dafiti using AWS solutions.

Flávia Lima is a Data Engineer at Dafiti, responsible for sustaining the data platform and providing the data from many sources to internal customers.

AWS recognized as a Challenger in the 2023 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

Post Syndicated from Jose Kunnackal original https://aws.amazon.com/blogs/big-data/aws-recognized-as-a-challenger-in-the-2023-gartner-magic-quadrant-for-analytics-and-business-intelligence-platforms/

AWS has been named a Challenger in the 2023 Gartner Magic Quadrant for Analytics and Business Intelligence (ABI) Platforms. Previously, AWS was positioned as a Niche player in the Magic Quadrant for ABI platforms. The Gartner Magic Quadrant evaluates 20 ABI companies based on their Ability to Execute and Completeness of Vision.

In our view, this recognition in the Magic Quadrant reinforces the progress we have made by tirelessly innovating on behalf of our customers. And this is just the beginning.

Benefits of QuickSight

AWS built QuickSight from the ground up as a cloud BI service to overcome challenges customers faced with alternative offerings. QuickSight powers data-driven organizations with unified business intelligence at hyperscale. With QuickSight, organizations of any size can meet the analytical needs of all users from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics, and natural language queries. Since introducing QuickSight in 2016, we have been on a journey to democratize access to data for everyone in an organization. In 2022 alone, QuickSight added more than 80 capabilities, making it easier for you to deliver valuable business insights throughout your organization, when and where needed.

Today, over 100,000 customers use QuickSight as their BI service. Organizations of all sizes are choosing QuickSight for their BI needs and enabling users to understand, visualize, and derive insights and predictions from data, regardless of technical expertise.

Review the Gartner Magic Quadrant

 2023 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

Access a complimentary copy of the full report to see why Gartner positioned AWS as a Challenger, and dive deep into the strengths and cautions of AWS.

We are excited about our momentum, strong vision, and the pace at which we are enabling our customers to democratize access to data for everyone in their organization.

Gartner Disclaimer

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Gartner, Magic Quadrant for Analytics and Business Intelligence Platforms, Kurt Schlegel, Julian Sun, David Pidsley, Anirudh Ganeshan, Fay Fei, Aura Popa, Radu Miclaus, Edgar Macari, Kevin Quinn, Christopher Long, 5 April 2023

Gartner is a registered trademark and service mark and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Amazon Web Services, Inc.

About the Author

Jose Kunnackal is Director of Product Management for Amazon QuickSight, AWS’ cloud-native, fully managed BI service. Jose started his career with Motorola, writing software for telecom and first responder systems. Later he was Director of Engineering at Trilibis Mobile, where he built a SaaS mobile web platform using AWS services. Jose is excited by the potential of cloud technologies to help customers make the most of their data.

Alexa Smart Properties creates value for hospitality, senior living, and healthcare properties with Amazon QuickSight Embedded

Post Syndicated from Preet Jassi original https://aws.amazon.com/blogs/big-data/alexa-smart-properties-creates-value-for-hospitality-senior-living-and-healthcare-properties-with-amazon-quicksight-embedded/

This is a guest post by Preet Jassi from Alexa Smart Properties.

Alexa Smart Properties (ASP) is powered by a set of technologies that property owners, property managers, and third-party solution providers can use to deploy and manage Alexa-enabled devices at scale. Alexa can simplify tasks like playing music, controlling lights, or communicating with on-site staff. Our team got its start by building products for hospitality and residential properties, but we have since expanded our products to serve senior living and healthcare properties.

With Alexa now available in hotels, hospitals, senior living homes, and other facilities, we hear stories from our customers every day about how much they love Alexa. Everything from helping veterans with visual impairments gain access to information, to enabling a senior living home resident who had fallen and sustained an injury to immediately alert staff. It’s a great feeling when you can say, “The product I work on every day makes a difference in people’s lives!”

Our team builds the software that leading hospitality, healthcare, and senior living facilities use to manage Alexa devices in their properties. We partner directly with organizations that manage their own properties as well as third-party solution providers to provide comprehensive strategy and deployment support for Alexa devices and skills, making sure that they are ready for end-user customers. Our primary goal is to create value for properties through improved customer satisfaction, cost savings, and incremental revenue. We wanted a way to measure that impact in a fast, efficient, easily accessible way from a return on investment (ROI) perspective.

After we had established what capabilities we needed to close our analytics gap, we got in touch with the Amazon QuickSight team to help. In this post, we discuss our requirements and why Amazon QuickSight Embedded was the right fit for what we needed.

Telling the ROI story with data

As a business-to-business-to-consumer product, our team serves the needs of two customers: the end-users who enjoy Alexa-enabled devices at the properties, and the property managers or solution providers that manage the Alexa deployment. We needed to prove to the latter group of customers that deploying Alexa would not only help them delight their customers, but save money as well.

We had the data necessary to tell that ROI story, but we needed an analytics solution that would allow us to provide insights that can be communicated to leadership.

These were our requirements:

  • Embeddable dashboards – We wanted to embed analytics into our Alexa Smart Properties management console, used by both enterprise customers and solution providers. With QuickSight, dashboards are embedded for aggregated Alexa usage analytics.
  • Easy access to insights – We wanted a tool that was accessible to all of our customers, whether they had a technical background or not. QuickSight provides a beautiful, user-friendly user interface (UI) that our customers can use to interpret their data and analytics.
  • Customizable and rich visuals – Our customers needed to be able to dive deep. QuickSight allows you to drill down into the data to easily create and change whatever visuals you need. Our customers love the look of the visuals and how easy it is to share them with their customers.

Analytics drive engagement

With QuickSight, we can now show detailed device usage information, including quantity and frequency, with insights that connect the dots between that engagement and cost savings. For example, property managers can look at total dialog counts to determine that their guests are using Alexa often, which validates their investment.

The following screenshots show an example of the dashboard our solution providers can access, which they can use to send reports to staff at the properties they serve.

Active devices dashboard

Dialogs dashboard

The following screenshots show an example of the Communications tab, which shows how properties use communications features to save costs (both in terms of time and equipment). Customers save time and money on protective equipment by using Alexa’s remote communication features, which enable caretakers to virtually check in on patients instead of visiting a property in person. These metrics help our customers calculate the cost savings from using Alexa.

Communications tab of analytics dashboard

All actions dashboard

In the last year, the Analytics page in our management console has had over 20,000 page views from customers who are accessing the data and insights there to understand the impact Alexa has had on their businesses.

Insights validate investment

With QuickSight embedded dashboards, our direct-property customers and solution providers now have an easy-to-understand visual representation of how Alexa is making a difference for the guests and patients at each property. Embedded dashboards simplify the viewing, analyzing, and insight gathering for key usage metrics that help both enterprise property owners and solution providers connect the dots between Alexa’s use and money saved. Because we use Amazon Redshift to house our data, QuickSight’s seamless integration made it a fantastic choice.

Going forward, we plan to expand and improve upon the analytics foundation we’ve built with QuickSight by providing programmatic access to data—for example, a CSV file that can be sent to a customer’s Amazon Simple Storage Service (Amazon S3) bucket—as well as adding more data to our dashboards, thereby creating new opportunities for deeper insights.

To learn more about how you can embed customized data visuals, interactive dashboards, and natural language querying into any application, visit Amazon QuickSight Embedded.

About the Author

Preet Jassi is a Principal Product Manager Technical with Alexa Smart Properties. Preet fell in love with technology in Grade 5 where he built his first website for his elementary school. Prior to completing his MBA at Cornell, Preet was a UI Team Lead with over 6 years of experience as a software engineer post BSc. Preet’s passion is combining his love of technology (specifically analytics and artificial intelligence), with design, and business strategy to build products that customers love, spending time with family, and keeping active. He currently manages the Developer Experience for Alexa Smart Properties focusing on making it quick and easy to deploy Alexa devices in properties and he loves hearing quotes from end customers on how Alexa has changed their lives.

SANS Institute uses Amazon QuickSight to drive transformational security awareness maturity within organizations

Post Syndicated from Carl R. Marrelli original https://aws.amazon.com/blogs/big-data/sans-institute-uses-amazon-quicksight-to-drive-transformational-security-awareness-maturity-within-organizations/

This is a guest post by Carl Marrelli from SANS Institute.

The SANS Institute is a world leader in cybersecurity training and certification. For over 30 years, SANS has worked with leading organizations to help ensure security across their organization, as well as with individual IT professionals who want to build and grow their security careers. We partner with over 500 organizations and support over 200,000 IT professionals with more than 90 technical training courses and over 40 professional (GIAC) certifications.

Our Security Awareness products include more than 70 instructional modules and have been deployed to over 6.5 million end-users to bring cybersecurity training to each employee within an organization.

As the Security Awareness department in particular began developing product strategies to deliver data-driven insights to customers, we were clear on using existing analytics services to rapidly build customer-facing analytics solutions. Building on a proven cloud provider would allow us to focus on our core expertise of helping organizations train, learn, and mature their programs instead of spending extra time and resources building and maintaining analytics from scratch.

We identified Amazon QuickSight, a fully managed, cloud-native business intelligence (BI) service, as the product that fit all our criteria. With it, we found an intuitive product with rich visualizations that we could build and grow with rapidly, allowing us to innovate without monetary risks or being locked in to cumbersome contracts. We considered other options, but they couldn’t support the licensing model that fit our needs.

In this post, we go over how we use QuickSight to serve our security customers.

Helping manage human risk with data-driven insights

SANS Security Awareness helps organizations use best-in-class security awareness and training solutions to transform their ability to measure and manage human risk. Security awareness programs are initiatives aimed at educating individuals about the importance of information security and the best practices for maintaining the confidentiality, integrity, and availability of information. We deliver expertly authored training materials to organizations, including computer-based video training sessions, interactive learning modules, supplemental materials, and reinforcement curriculum to keep security top-of-mind for all employees.

As organizations rapidly adopt and expand their use of digital technologies in their day-to-day work, the number of touchpoints with humans increases. As threat landscapes become increasingly more severe, managing human risk is critical to the success of the security program in any organization. Not only do organizations have to conduct security awareness training programs, but they also need insights into data and metrics that identify points of weakness to take data-driven corrective courses of action. As a leader in the space, we wanted to innovate by bringing relevant data-driven insights to our Security Awareness partners and customers in the journey to ensuring human-centered security across their organizations.

New data products to enhance and gamify risk assessment

We built one of our first insights products to support our Behavioral Risk Assessment. This service allows senior security and risk leaders to assess human risk with data handling, digital behavior, and compliance in an organization by individual, team, geography, business unit, and more. Leaders use the assessment to mature their security awareness capability with risk-informed interventions, identify process and procedure gaps, surface shadow IT, and reduce overall awareness training costs by focusing attention on the most important areas of risk.

Behavioral Risk Assessment dashboard with various charts

Delivered via a survey customized to the data types and risk profile of an organization, this assessment allows risk management leaders to more easily understand the data handling practices across roles and departments. Dashboards built in QuickSight empower stakeholders to quickly visualize what areas may need added attention by way of training intervention or updated policy.

Another product area where we invested in analytics to help organizations identify human risk is in gamified awareness training. The SANS Scavenger Hunt utilizes QuickSight in a unique way as a real-time game scoreboard. Players compete in the hunt while solving cybersecurity-related challenges, giving security teams a fun way to engage the workforce and promote good cyber behaviors.

Security Awareness challenge dashboard

The Scavenger Hunt was widely deployed during global Cybersecurity Awareness Month—a time for security awareness practitioners to shine a light on the purpose and mission of security awareness and also have a little fun. Typically, programs run during this time take place outside any regulated training cycle and are typically not delivered as mandatory training. This being the case, we identified dashboards as a way to gamify the experience to increase engagement among participants. These dashboards, built using QuickSight, provided users access to a leaderboard to not only track their own progress, but to also see how they compared to their fellow participants.

Building on the success of their experience with QuickSight and the Scavenger Hunt, we wanted to push the gamification and dashboards concept further so Chief Information and Security Officers (CISOs) and security teams could identify and mitigate the human side of ransomware risk. We developed Snack Attack!, a gamified learning experience that shows an organization how employees are performing in six key defensive areas where ransomware can be prevented. In 2021, over 80% of cyber breaches involved human error of some kind. Employees must have a fundamental awareness of cybersecurity and the ability to apply cyber knowledge within the scope of their jobs. Snack Attack! and QuickSight proved to be a great product to visualize and action on areas of human risk and sentiment for senior leadership.

A screenshot of a Snack Attack! dashboard

With Snack Attack!, we looked at Cybersecurity Awareness Month from the viewpoint of the awareness practitioner. The program itself focuses on driving engagement through an entertaining storyline with creative visuals. We chose to use the data from the training to help our customers build their awareness programs going forward. The dashboards included in Snack Attack! give the security awareness practitioner insights into the learned behavior of their users. Quick visualizations of learners’ scoring in Snack Attack! can act as an audit of the effectiveness of their existing program and provide a roadmap for future trainings.

Paving the way in using analytics for customer security

The SANS Institute brings together security awareness training programs with a metrics-based approach through out-of-the-box analytics dashboards so our customers can assess and manage human risk successfully. With QuickSight, we were able to rapidly innovate, developing valuable data products at a speed we could not have otherwise. Without up-front investments to get started and with the low cost to try with usage-based pricing, we were able to quickly ideate, build, and deploy customer-facing analytic products to drive security awareness within our customer organizations. Our analytics solutions differentiate us from existing enterprise products. With QuickSight, we are able to show organizations where they have cyber risk.

With the delivery of analytics solutions to customers, the SANS Institute is not only a top cybersecurity training, learning, and certification platform, but also a technology provider that helps customers use data and insights to make meaningful change in their organization. Moving forward, we have identified an expansion of QuickSight dashboards into our larger suite of assessments as the next logical step. Along with the Behavioral Risk Assessment, we offer Knowledge and Culture assessments to help security awareness practitioners better understand where and how to apply training and gauge the effectiveness of their programs. Because of the success we have had with QuickSight on our existing projects, we feel that similar dashboards can provide even more value to our customers.

To learn more about how QuickSight can help your business with dashboards, reports, and more, visit Amazon QuickSight.

About the Author

Carl R. Marrelli is the Director of Business Development and Digital Programs at SANS Institute. Based in Charlotte, NC, he has extensive experience in cross-functional team leadership, product management, and product marketing. Previously as Head of Product at SANS, Carl led the product management team for the Online Training and Security Awareness divisions through a significant growth period. Carl’s unique perspective and innovative ideas, support SANS as the company continues its mission to empower cybersecurity practitioners around the world.

Reference guide to build inventory management and forecasting solutions on AWS

Post Syndicated from Jason Dalba original https://aws.amazon.com/blogs/big-data/reference-guide-to-build-inventory-management-and-forecasting-solutions-on-aws/

Inventory management is a critical function for any business that deals with physical products. The primary challenge businesses face with inventory management is balancing the cost of holding inventory with the need to ensure that products are available when customers demand them.

The consequences of poor inventory management can be severe. Overstocking can lead to increased holding costs and waste, while understocking can result in lost sales, reduced customer satisfaction, and damage to the business’s reputation. Inefficient inventory management can also tie up valuable resources, including capital and warehouse space, and can impact profitability.

Forecasting is another critical component of effective inventory management. Accurately predicting demand for products allows businesses to optimize inventory levels, minimize stockouts, and reduce holding costs. However, forecasting can be a complex process, and inaccurate predictions can lead to missed opportunities and lost revenue.

To address these challenges, businesses need an inventory management and forecasting solution that can provide real-time insights into inventory levels, demand trends, and customer behavior. Such a solution should use the latest technologies, including Internet of Things (IoT) sensors, cloud computing, and machine learning (ML), to provide accurate, timely, and actionable data. By implementing such a solution, businesses can improve their inventory management processes, reduce holding costs, increase revenue, and enhance customer satisfaction.

In this post, we discuss how to streamline inventory management forecasting systems with AWS managed analytics, AI/ML, and database services.

Solution overview

In today’s highly competitive business landscape, it’s essential for retailers to optimize their inventory management processes to maximize profitability and improve customer satisfaction. With the proliferation of IoT devices and the abundance of data generated by them, it has become possible to collect real-time data on inventory levels, customer behavior, and other key metrics.

To take advantage of this data and build an effective inventory management and forecasting solution, retailers can use a range of AWS services. By collecting data from store sensors using AWS IoT Core, ingesting it using AWS Lambda to Amazon Aurora Serverless, and transforming it using AWS Glue from a database to an Amazon Simple Storage Service (Amazon S3) data lake, retailers can gain deep insights into their inventory and customer behavior.

With Amazon Athena, retailers can analyze this data to identify trends, patterns, and anomalies, and use Amazon ElastiCache for customer-facing applications with reduced latency. Additionally, by building a point of sales application on Amazon QuickSight, retailers can embed customer 360 views into the application to provide personalized shopping experiences and drive customer loyalty.

Finally, we can use Amazon SageMaker to build forecasting models that can predict inventory demand and optimize stock levels.

With these AWS services, retailers can build an end-to-end inventory management and forecasting solution that provides real-time insights into inventory levels and customer behavior, enabling them to make informed decisions that drive business growth and customer satisfaction.

The following diagram illustrates a sample architecture.

With the appropriate AWS services, your inventory management and forecasting system can have optimized collection, storage, processing, and analysis of data from multiple sources. The solution includes the following components.

Data ingestion and storage

Retail businesses have event-driven data that requires action from downstream processes. It’s critical for an inventory management application to handle the data ingestion and storage for changing demands.

The data ingestion process is typically triggered by an event such as an order being placed, kicking off the inventory management workflow, which requires actions from backend services. Developers are responsible for the operational overhead of trying to maintain the data ingestion load from an event driven-application.

The volume and velocity of data can change in the retail industry each day. Events like Black Friday or a new campaign can create volatile demand in what is required to process and store the inventory data. Serverless services designed to scale to businesses’ needs help reduce the architectural and operational challenges that are driven from high-demand retail applications.

Understanding the scaling challenges that occur when inventory demand spikes, we can deploy Lambda, a serverless, event-driven compute service, to trigger the data ingestion process. As inventory events occur like purchases or returns, Lambda automatically scales compute resources to meet the volume of incoming data.

After Lambda responds to the inventory action request, the updated data is stored in Aurora Serverless. Aurora Serverless is a serverless relational database that is designed to scale to the application’s needs. When peak loads hit during events like Black Friday, Aurora Serverless deploys only the database capacity necessary to meet the workload.

Inventory management applications have ever-changing demands. Deploying serverless services to handle the ingestion and storage of data will not only optimize cost but also reduce the operational overhead for developers, freeing up bandwidth for other critical business needs.

Data performance

Customer-facing applications require low latency to maintain positive user experiences with microsecond response times. ElastiCache, a fully managed, in-memory database, delivers high-performance data retrieval to users.

In-memory caching provided by ElastiCache is used to improve latency and throughput for read-heavy applications that online retailers experience. By storing critical pieces of data in-memory like commonly accessed product information, the application performance improves. Product information is an ideal candidate for a cached store due to data staying relatively the same.

Functionality is often added to retail applications to retrieve trending products. Trending products can be cycled through the cache dependent on customer access patterns. ElastiCache manages the real-time application data caching, allowing your customers to experience microsecond response times while supporting high-throughput handling of hundreds of millions of operations per second.

Data transformation

Data transformation is essential in inventory management and forecasting solutions for both data analysis around sales and inventory, as well as ML for forecasting. This is because raw data from various sources can contain inconsistencies, errors, and missing values that may distort the analysis and forecast results.

In the inventory management and forecasting solution, AWS Glue is recommended for data transformation. The tool addresses issues such as cleaning, restructuring, and consolidating data into a standard format that can be easily analyzed. As a result of the transformation, businesses can obtain a more precise understanding of inventory, sales trends, and customer behavior, influencing data-driven decisions to optimize inventory management and sales strategies. Furthermore, high-quality data is crucial for ML algorithms to make accurate forecasts.

By transforming data, organizations can enhance the accuracy and dependability of their forecasting models, ultimately leading to improved inventory management and cost savings.

Data analysis

Data analysis has become increasingly important for businesses because it allows leaders to make informed operational decisions. However, analyzing large volumes of data can be a time-consuming and resource-intensive task. This is where Athena come in. With Athena, businesses can easily query historical sales and inventory data stored in S3 data lakes and combine it with real-time transactional data from Aurora Serverless databases.

The federated capabilities of Athena allow businesses to generate insights by combining datasets without the need to build ETL (extract, transform, and load) pipelines, saving time and resources. This enables businesses to quickly gain a comprehensive understanding of their inventory and sales trends, which can be used to optimize inventory management and forecasting, ultimately improving operations and increasing profitability.

With Athena’s ease of use and powerful capabilities, businesses can quickly analyze their data and gain valuable insights, driving growth and success without the need for complex ETL pipelines.


Inventory forecasting is an important aspect of inventory management for businesses that deal with physical products. Accurately predicting demand for products can help optimize inventory levels, reduce costs, and improve customer satisfaction. ML can help simplify and improve inventory forecasting by making more accurate predictions based on historical data.

SageMaker is a powerful ML platform that you can use to build, train, and deploy ML models for a wide range of applications, including inventory forecasting. In this solution, we use SageMaker to build and train an ML model for inventory forecasting, covering the basic concepts of ML, the data preparation process, model training and evaluation, and deploying the model for use in a production environment.

The solution also introduces the concept of hierarchical forecasting, which involves generating coherent forecasts that maintain the relationships within the hierarchy or reconciling incoherent forecasts. The workshop provides a step-by-step process for using the training capabilities of SageMaker to carry out hierarchical forecasting using synthetic retail data and the scikit-hts package. The FBProphet model was used along with bottom-up and top-down hierarchical aggregation and disaggregation methods. We used Amazon SageMaker Experiments to train multiple models, and the best model was picked out of the four trained models.

Although the approach was demonstrated on a synthetic retail dataset, you can use the provided code with any time series dataset that exhibits a similar hierarchical structure.

Security and authentication

The solution takes advantage of the scalability, reliability, and security of AWS services to provide a comprehensive inventory management and forecasting solution that can help businesses optimize their inventory levels, reduce holding costs, increase revenue, and enhance customer satisfaction. By incorporating user authentication with Amazon Cognito and Amazon API Gateway, the solution ensures that the system is secure and accessible only by authorized users.

Next steps

The next step to build an inventory management and forecasting solution on AWS would be to go through the Inventory Management workshop. In the workshop, you will get hands-on with AWS managed analytics, AI/ML, and database services to dive deep into an end-to-end inventory management solution. By the end of the workshop, you will have gone through the configuration and deployment of the critical pieces that make up an inventory management system.


In conclusion, building an inventory management and forecasting solution on AWS can help businesses optimize their inventory levels, reduce holding costs, increase revenue, and enhance customer satisfaction. With AWS services like IoT Core, Lambda, Aurora Serverless, AWS Glue, Athena, ElastiCache, QuickSight, SageMaker, and Amazon Cognito, businesses can use scalable, reliable, and secure technologies to collect, store, process, and analyze data from various sources.

The end-to-end solution is designed for individuals in various roles, such as business users, data engineers, data scientists, and data analysts, who are responsible for comprehending, creating, and overseeing processes related to retail inventory forecasting. Overall, an inventory management and forecasting solution on AWS can provide businesses with the insights and tools they need to make data-driven decisions and stay competitive in a constantly evolving retail landscape.

About the Authors

Jason D’Alba is an AWS Solutions Architect leader focused on databases and enterprise applications, helping customers architect highly available and scalable solutions.

Navnit Shukla is an AWS Specialist Solution Architect, Analytics, and is passionate about helping customers uncover insights from their data. He has been building solutions to help organizations make data-driven decisions.

Vetri Natarajan is a Specialist Solutions Architect for Amazon QuickSight. Vetri has 15 years of experience implementing enterprise business intelligence (BI) solutions and greenfield data products. Vetri specializes in integration of BI solutions with business applications and enable data-driven decisions.

Sindhura Palakodety is a Solutions Architect at AWS. She is passionate about helping customers build enterprise-scale Well-Architected solutions on the AWS platform and specializes in Data Analytics domain.

It’s the Amazon QuickSight Community’s 1st birthday!

Post Syndicated from Kristin Mandia original https://aws.amazon.com/blogs/big-data/its-the-amazon-quicksight-communitys-1st-birthday/

Happy birthday Amazon QuickSight Community! We are celebrating 1 year since the launch of our new Community. The Amazon QuickSight Community website is a one-stop-shop where business intelligence (BI) authors and developers from across the globe can ask and answer questions, stay up to date, network, and learn together about Amazon QuickSight. In this post, we celebrate the rapid growth of our first year of the QuickSight Community, discuss new Community features, and share voices from our Community. We also invite you to sign up for the QuickSight Community (it’s easy and free) and get started on your learning journey.

Happy 1st birthday Amazon QuickSight Community!

We’re growing strong

Since last year’s announcement of the new QuickSight Community, we are witnessing hundreds of thousands of visits to the QuickSight Community each month. Answers to thousands of searchable questions have been posted, and our online Learning Series is now running every week. An events calendar has been added, and hundreds of learning resources have been posted (how-to videos and articles, Getting Started resources, blog posts, and What’s New posts). Furthermore, a Community Manager has been brought on board. And Community Experts and QuickSight Solution Architects are posting robust answers in the Q&A, supported by AWS Professional Services, AWS Data Lab, and Amazon QuickSight Service Delivery Partners—led by West Loop Strategy. In addition, other QuickSight Partners and content creators are bringing new life to the Community.

QuickSgiht Community members at an Amazon conference

New QuickSight Community members at an internal Amazon conference

Why are we excited? Community members share their voices

As we celebrate year one, QuickSight Community members share their voices:

“The community has been an awesome space to exchange ideas, problems, and solutions, which has really helped every one of us in adopting QuickSight more effectively!” #thank you #community — Sagnik Mukherjee, Data and Analytics Architect, QuickSight Expert

“I am so excited for the QuickSight Learning Series! And I am happy to know that there is a supportive community…as I begin my QS journey!“ — Rachel Krentz, Configuration Analyst, Madaket

“The community has evolved so much in just one year!” — Darren Demicoli, BI Team Lead, The Mill Adventure, QuickSight Expert

“Happy Birthday QuickSight Community! I’m pretty new here but have found this to be a really great resource with answers to every question I have been able to come up with so far. Cheers!” — Brian Jager, Sales Operations Lead, Amazon Business

“I love being connected directly with other users and solutions architects…The QuickSight Community helps me out a ton not having to spend as much of my time on training when I can point new users to a resource where their questions have likely already been answered.” — Ryane Brady, Programmer Analyst, GROWMARK, Inc., QuickSight Expert

Hear more voices from our Community on our birthday post.

The QuickSight Community: Your one-stop-shop for BI learning

Here’s a quick tour of the QuickSight Community website as well as its new features.

The following figure shows the homepage view and everything that’s available.

A One-Stop-Shop for your Business Intelligence journey includes resource toolbar, searchable Q&A, learning resources, what’s new, blog, events and featured content

Learning Center: What’s new

In addition to growing a robust, searchable Q&A, in the last year, we’ve added tons of new learning resources to the QuickSight Community, including:

When you select Learning Center on the homepage, you can choose from these various resources.

Amazon QuickSight Learning Center – includes getting started resources, how to videos, webinar videos, articles, and other resources.

New: Learning events

In the last year, we’ve added an Events section to the QuickSight Community. We now offer weekly and monthly learning webinars as well as featured in-person and online events. Ongoing virtual events include:

New: Experts and user groups

We’ve been excited to see QuickSight user groups pop up over the past year, including one in the Twin Cities and one in Chicago—both hosted by QuickSight Service Delivery Partners (Charter Solutions and West Loop Strategy, respectively).

In addition, we’ve launched our QuickSight Experts program, honoring top question-answerers in the Community. These rock stars have relentlessly helped their peers take their learning to the next level.

QuickSight Community Expert

Ryane Brady, Programmer Analyst, GROWMARK, Inc., is a QuickSight Expert and top question-answerer in the Community. Here she is showing off her Expert swag. See the 2022 to Q1 2023 list of Experts at the bottom of this post.

First External Viz Challenge

In the last year, AWS hosted its very first external data visualization design competition to invited QuickSight Service Delivery Partners. This enabled QuickSight Partners to show off their visual analytics and storytelling skills. We were proud to feature the QuickSight Partner Viz Challenge winners of 2022 on the QuickSight Community.

Amazon QuickSight Service Delivery Partner Viz Challenge winners

Want to be a part of the QuickSight Community?

Sign up now to join the QuickSight Community (it’s free and easy) and take your QuickSight learning to the next level.

As part of the Community, you can:

  • Encourage your teams and colleagues to create QuickSight Community user profiles—signup up is easy
  • Click the bell icon in the top right corner of the page to get notifications and keep up to date with QuickSight news
  • Share your community ideas with the Community Manager

Come in on the ground floor and help us take the QuickSight Community to the next level:

  • Become an Expert and share your knowledge
  • Pioneer a user group online or in your area
  • Contribute a how-to article

If you’re interested in any of these opportunities, reach out to Kristin, the Sr. Online Community Manager for QuickSight, at [email protected].

We’re just getting started

Thank you to everyone who has helped the QuickSight Community grow over the last year! We can’t wait to see what this next year has in store!

Special thanks to our QuickSight Experts who make the QuickSight Community possible (listed alphabetically): Ryane Brady, Biswajit Dash, Darren Demicoli, Max Engelhard, Charles Greenacre, Naveed Hashimi, Todd Hoffman, Mike Khuns, Thomas Kotzurek, Sanjeeb Mohapatra, Sagnik Mukherjee and David Wong. Incredible gratitude to Lillie Atkins and Mia Heard, who launched the Community a year ago. Also, thanks to Jose Kunnackal John, Jill Florant, the QuickSight Solution Architects, Amazon QuickSight Service Delivery Partners—led by West Loop Strategy, AWS Service Acceleration team, AWS ProServe team, and AWS Data Lab team for your incredible investment in the QuickSight Community.

About the Authors

Kristin Mandia is Senior Online Community Manager for Amazon QuickSight, Amazon Web Service’s cloud-native, fully managed BI service.

Ian McNamara
is a Program Manager and writer on the Customer Success Team for Amazon QuickSight, Amazon Web Service’s cloud-native, fully managed BI service.

Showpad accelerates data maturity to unlock innovation using Amazon QuickSight

Post Syndicated from Shruthi Panicker original https://aws.amazon.com/blogs/big-data/showpad-accelerates-data-maturity-to-unlock-innovation-using-amazon-quicksight/

Showpad aligns sales and marketing teams around impactful content and powerful training, helping sellers engage with buyers and generate the insights needed to continuously improve conversion rates. In 2021, Showpad set forth the vision to use the power of data to unlock innovations and drive business decisions across its organization. Showpad’s legacy solution was fragmented and expensive, with different tools providing conflicting insights and lengthening time to insight. The company decided to use AWS to unify its business intelligence (BI) and reporting strategy for both internal organization-wide use cases and in-product embedded analytics targeted at its customers.

Showpad built new customer-facing embedded dashboards within Showpad eOSTM and migrated its legacy dashboards to Amazon QuickSight, a unified BI service providing modern interactive dashboards, natural language querying, paginated reports, machine learning (ML) insights, and embedded analytics at scale.

In this post, we share how Showpad used QuickSight to streamline data and insights access across teams and customers. Showpad migrated over 70 dashboards with over 1,000 visuals. They have rolled out the solution to all its 600 employees, increased dashboard development activity by three times, and reduced dashboard turnaround time from months to weeks. Showpad also launched dashboards and reports to over 1,300 customers worldwide, providing access to tens of thousands of users across all its customers.

Streamlining data-driven decisions by defragmenting the data and reporting architecture

Founded in 2011, dual headquartered in Belgium and Chicago and with offices around the world, Showpad provides a single destination for sales representatives to access all sales content and information, along with coaching and training tools to create informed, upskilled, and trusted buying teams. The platform also provides analytics and insights to support successful information sharing and fuel continuous improvement. In 2021, Showpad decided to take the next step in its data evolution and set forth the vision to power innovation, product decisions, and customer engagement using data-driven insights. This required Showpad to accelerate its data maturity as a company by mindfully using data and technology holistically to help its customers.

But the company’s legacy BI solution and data were fragmented across multiple tools, some with proprietary logic. “Each of these tools were getting data from a different place, and that’s where it gets difficult,” says Jeroen Minnaert, head of data at Showpad. “If each tool tells a different story because it has different data, we won’t have alignment within the business on what this data means.” Showpad also struggled with data quality issues in terms of consistency, ownership, and insufficient data access across its targeted user base due to a complex BI access process, licensing challenges, and insufficient education.

Showpad wanted to unify all the data into a single unified interface through a data lake, democratize that data through a BI solution such that autonomous teams across the company could effectively use data, and drive and unlock innovation in the company through advanced insights data, artificial intelligence, and ML. The company already used AWS in other aspects of its business and found that QuickSight would not only meet all its BI and reporting needs with seamless integrations into the AWS stack, but also bring with it several unique benefits unlike incumbents and other tools evaluated. “We chose QuickSight because of its embedded analytic capabilities, serverless architecture, and consumption-based pricing,” says Minnaert. “Using QuickSight to launch interactive dashboards and reporting for our customers, along with the ability for our customer success teams to create or alter dashboard prototypes on our internal-facing QuickSight instance and then promote those dashboards to customers through the product, was a very compelling use case.”

QuickSight would help local data stewards, who weren’t technical but knew the use cases intimately, to create their own dashboards and prototype them with their customers before promoting them through the product. “The serverless model was also compelling because we did not have to pay for server instances nor license fees per reader. With QuickSight, we pay for usage. This makes it easy for us to provide access to everyone by default. This is a key pillar in our ability to democratize data,” says Minnaert.

A screenshot of Showpad's Platform/Adoption dashboard

Architecting a portable data layer and migrating to QuickSight to accelerate time to value

After choosing QuickSight as its solution in November 2021, Showpad took on two streams of development: migrating internal organization-wide BI reporting and building in-product reporting using embedded analytics. Showpad worked closely alongside the QuickSight team to have a smooth rollout. The company involved members of the QuickSight team in its internal communications and set up meetings every 2 weeks to resolve difficult issues quickly.

On the internal reporting front, the data team took a “working backwards” approach to make sure it had the right approach before going all in with all existing dashboards. Showpad selected five difficult and complex dashboards and took 3 months to explore various possibilities using QuickSight. For example, the team determined that user log-in would be automated with single sign-on and Okta and built a plan for assets organization, asset promotion, and access controls. The company also used the opportunity to reimagine its data pipeline and architecture. A key architectural decision that Showpad took during this time was to create a portable data layer by decoupling the data transformation from visualization, ML, or ad hoc querying tools and centralizing its business logic. The portable data layer facilitated the creation of data products for varied use cases, made available within various tools based on the need of the consumer—be it a business analyst, data scientist, or business user.

After the solution approach was decided on and the foundation was built, the team wanted to scale. However, with 70 dashboards with over 1,000 visuals with varying levels of complexities, including proprietary logic unique to certain tools, and data from over 1,000 tables ingesting data from over 20 data sources, the team decided to take a measured approach to the migration. The entire data team of 20 people were “all on hands on deck” for the project.

First, Showpad created a landscape of all the dashboards, connecting data sources and dependencies before prioritizing the migration order. The company decided to start with dashboards with the fewest dependencies, like product and engineering dashboards that had a single data source, followed by revenue operations dashboards with a couple of data sources, and lastly with customer success and marketing dashboards that combined product and engineering and revenue operations data. After migration was complete, Showpad validated all the numbers and worked with business stakeholders for quality assurance. It launched the first set of dashboards in April 2022, followed by customer success and marketing dashboards in July 2022. As of January 2023, Showpad’s QuickSight instance includes over 2,433 datasets and 199 dashboards.

Showpad also achieves benefits for its customers by using QuickSight to deliver a wide variety of insights, including usage dashboards, industry comparisons, user comparison, group comparison, and revenue attribution. On the second workstream of in-product customer-facing reporting, Showpad released its first version of QuickSight reporting to customers in June 2022. “We went through user research, development, and beta tests in a span of 6 months, which was a fast turnaround and a big win for us,” says Minnaert. And Showpad aims to further accelerate the turnaround time to make a report and ship it to a customer.

With the foundational architecture now in place, shipping to a customer can happen in a few sprints, with most of the time spent on iterating and fine-tuning insights instead of engineering a scalable reporting solution. Showpad can also improve the insights it offers to customers. “Using QuickSight in our product makes it a powerful tool,” says Minnaert. “We can launch reporting for our customers so that they can look at and interpret data by themselves.” Showpad can then follow up with tailor-made reporting for each customer using the same data so that it tells a consistent story.

After a dashboard is agreed on, the dashboard can go through Showpad’s automated dashboard promotion process that can take an idea from development to production to a smile on a customer’s face in weeks, not months.

A screenshot of their Shared Space engagement dashboard

Unlocking innovation with self-service BI and rapid prototyping

By providing dashboard and report building to analysts and nontechnical users, Showpad drastically reduced overall turnaround time to build and deliver insights, down from months to weeks. Showpad also increased dashboard development activity by three times across the organization.

Showpad users can quickly prototype reports in a well-known environment—building reports using QuickSight, and then testing them with customers by helping customers understand how the reporting would look and function. “After we settle on reports or dashboards, it does not take much engineering effort to bring them to production,” says Minnaert. “We can make a lot of innovation happen by quickly prototyping and bringing validated prototypes to production.” Showpad’s users and customers also benefit from performance gains with 10 times increased speed when using SPICE (Super-fast, Parallel, In-memory Calculation Engine), which is the robust in-memory engine that QuickSight uses. It takes only seconds to load dashboards.

Because QuickSight is serverless and uses a session-based pricing model, Showpad expects to see cost savings. By paying per use, Showpad can easily provide access to all its users and customers without purchasing expensive per-reader licenses. Showpad also doesn’t need to pay for server instances or maintain infrastructure for BI. In addition, Showpad can deprecate custom reporting, infrastructure, and multiple tools with the new data architecture and QuickSight. “Much of our cost savings will come from being able to deprecate the custom reporting that we’ve made in the past,” says Minnaert. “The custom reporting used a lot of infrastructure that we no longer need to maintain.” Showpad expects to see a three times increase in projected return on investment in the upcoming year.

Showpad completed its internal BI migration to QuickSight by the end of 2022. Showpad also continues to expand the in-product reporting while continuing to optimize performance for the best customer experience. Showpad hopes to further reduce the time it takes to load a dashboard to under 1 second.

In conjunction with Showpad’s new portable data layer, QuickSight helps users of all types across its organization and customers self-serve data and insights rapidly. Everyone in Showpad gets access to data and insights the day they onboard with Showpad. To make self-service even easier, Showpad will soon launch embedded Amazon QuickSight Q so anyone can ask questions in natural language and receive accurate answers with relevant visualizations that help them gain insights from the data. By helping business users and experts rapidly prototype dashboards and reports in line with user and customer needs, Showpad uses the power of data to unlock innovation and drive growth across its organization. “QuickSight has become our go-to tool for any BI requirement at Showpad—both internal and external customer facing, especially when it comes to correlating data across departments and business units,” says Minnaert.

Get started with QuickSight

Migrating to QuickSight enabled Showpad to streamline data and insights access across teams and customers and reduced overall turnaround time to build and deliver insights from months to weeks.

Learn more about unleashing your organization’s ability to accelerate revenue growth with Showpad. To learn more about how QuickSight can help your business with dashboards, reports, and more, visit Amazon QuickSight.

About the Author

Shruthi Panicker is a Senior Product Marketing Manager with Amazon QuickSight at AWS. As an engineer turned product marketer, Shruthi has spent over 15 years in the technology industry in various roles from software engineering, to solution architecting to product marketing. She is passionate about working at the intersection of technology and business to tell great product stories that help drive customer value.

Create threshold alerts on tables and pivot tables in Amazon QuickSight

Post Syndicated from Lillie Atkins original https://aws.amazon.com/blogs/big-data/create-threshold-alerts-on-tables-and-pivot-tables-in-amazon-quicksight/

Amazon QuickSight previously launched threshold alerts on KPIs and gauge charts. Now, QuickSight supports creating threshold alerts on tables and pivot tables—our most popular visual types. This allows readers and authors to track goals or key performance indicators (KPIs) and be notified via email when they are met. These alerts allow readers and authors to relax and rely on notifications for when their attention is needed. In this post, we share how to create threshold alerts on tables or pivot tables to track important metrics.

Background information

Threshold alerts are a QuickSight Enterprise Edition feature and available for dashboards consumed on the QuickSight website. Threshold alerts aren’t yet available in embedded QuickSight dashboards or on the mobile app.

Alerts are created based on the visual at that point in time and are not affected by potential future changes to the visual’s design. This means the visual can be changed or deleted and the alert continues to work as long as the data in the dataset remains valid. In addition, you can create multiple alerts off of one visual, and rename them as appropriate.

Finally, alerts respect RLS and CLS rules.

Set up an alert on a table or pivot table

Threshold alerts are configured for dashboards. On a dashboard, there are three different ways to create an alert on a table or pivot table.

First, you can create directly from a pivot table or table. You click directly on the cell you would like to create an alert on (if there is another action enabled, you may have to right-click to get this option to show). This needs to be on a numeric value (no dates or strings allow for creation of alerts). Then choose Create Alert to start creating the alert.

Let’s assume you want to track the profit coming from online purchases for auto-related merchandise being shipped first class. Choose the appropriate cell and then choose Create Alert.

Create Alert

You’re presented with the creation pane for alerts. The only difference from KPIs or gauge visual alerts is that here you’ll find the other dimensions in the row that you’re creating the alert on. This will help you identify what value from the table you have selected, because there can be duplicates of the numeric values.

In the following screenshot, the value to track is profit, which currently is $437.39. This is the value that will be compared to the threshold you set. You will also see the dimensions being used to define this alert, which are taken from the row of the table. In this case, the Category is Auto, the Segement is Online, and the Ship Mode is First Class.

Now that you have checked that the value is correct, you can update the name of the alert that is automatically filled with the name of the visual it is created off of, set the condition (between Is above, Is below, and Is equal to), and pick the threshold value, notification frequency, and whether you want to be emailed when there is no data.

In the following example, the alert has been configured so that you will receive an email when the profit is above the threshold of $1,000. You’ve also left the notification frquency at Daily at most and haven’t requested to be emailed when there is no data.

If you have a date field, you also will see an option to control the date. This will automatically set the date field to be the most recent of whatever aggregation you’re looking at, such as hour, week, day, month. However, you could override to use the specific date applied to the value you have selected if you would prefer.

Below is an example where the data was aggregated based on the week and so Latest Week has been selected rather than the historical Week of Jan 4, 2015.

You can then choose Save if you’re happy with the alert and it will load the Manage alert pane.

The Create Alert button is also at the bottom of the pane. This is the second way you can start creating an alert off of a table or pivot table.

You can also get to this pane from the upper right alert button on the dashboard.

Create Alert through the icon on dashboard

If you have no alerts, this will automatically drop you into the creation pane. There you will be asked to select a visual that supports alerts to begin creating an alert. If you already have alerts (as previously demonstrated), then all you need to do is choose Create Alert.

Then select a visual and choose Next.

You’re prompted to select a cell if you have picked a table or pivot table visual.

Then you repeat the same steps as creating off a cell within a table or pivot table.

Finally, you can start creating an alert from the bell icon on the pivot table or table. This is the third way to create an alert.

bell icon

You’ll be prompted to select a cell from the table, and the creation pane appears.

After you choose the cell that you want to track, you start the creation process just like the first two examples.

Update and delete alerts

To update or delete an alert, you need to navigate back to the Manage alerts pane. You get there from the bell icon on the top right corner of the dashboard.

Create Alert through the icon on dashboard

You can then choose the options menu (three dots) on the alert you want to manage. Three options appear: Edit alert, View history (to view recent times the alert has breached and notified you), and Delete.


You’ll receive an email when your alert breaches the rule you set. The following is an example of what that looks like (the alert has been adjusted to be alerted if profit is over $100 and to be notified as frequently as possible).

notification if alert is breached

The current profit breach is highlighted and the historical numbers are shown along with the date and time of the recorded breaches. You can also navigate to the dashboard by choosing View Dashboard.

Evaluate alerts

The evaluation schedule for threshold alerts is based on the dataset. For SPICE datasets, alert rules are checked against the data after a successful data refresh. With datasets querying your data sources directly, alerts are evaluated daily at a random time between 6:00 PM and 8:00 AM. This is based on the the timezone of the AWS Region your dataset was created in. Dataset owners can set up their own schedules for checking alerts and increase the frequency up to hourly (to learn more, refer to Working with threshold alerts in Amazon QuickSight).

Restrict alerts

The admin for the QuickSight account can restrict who has access to set threshold alerts through custom permissions. For more information, see the section Customizing user permissions in Embed multi-tenant analytics in applications with Amazon QuickSight.


Threshold alerts are billed for each evaluation, and follow the familiar pricing used for anomaly detection, starting at $0.50 per 1,000 evaluations. For example, if you set up an alert on a SPICE dataset that refreshes daily, you have 30 evaluations of the alert rule in a month, which costs 30 * $0.5/1000 = $0.015 in a month. For more information, refer to Amazon QuickSight Pricing.


In this post, you learned how to create threshold alerts on tables and pivot tables within QuickSight dashboards so that you can track important metrics. For more information about how to create threshold alerts on KPIs or gauge charts, refer to Create threshold-based alerts in Amazon QuickSight. Additional information is available in the Amazon QuickSight User Guide.

About the Author

Lillie Atkins is a Product Manager for Amazon QuickSight, Amazon Web Service’s cloud-native, fully managed BI service.

Deep Pool boosts software quality control using Amazon QuickSight

Post Syndicated from Shruthi Panicker original https://aws.amazon.com/blogs/big-data/deep-pool-boosts-software-quality-control-using-amazon-quicksight/

Deep Pool Financial Solutions, an investor servicing and compliance solutions supplier, was looking to build key performance indicators to track its software tests, failures, and successful fixes to pinpoint the specific areas for improvement in its client software. Deep Pool was unable to access the large amounts of data that its project management software provided, so it used AWS to access, manage, and analyze that data more efficiently.

During a larger migration to the AWS Cloud, the company discovered Amazon QuickSight, a cloud-native, serverless business intelligence (BI) service that powers interactive dashboards that let companies make better data-driven decisions. With QuickSight, Deep Pool could democratize access to this unused data and pinpoint areas for improvement in its software development processes, thereby improving the overall quality of its software.

According to Brett Promisel, Chief Operating Officer for Deep Pool, the company wanted to manage the data that it was collecting from a BI point of view to help it make more informed decisions. Because word of mouth and high-quality software are critical in Deep Pool’s industry, the company wanted to add additional rigorous quality controls to its product development and testing so that it continues to provide top-notch, stable software that its clients can rely on.

In this post, we share how Deep Pool boosted its software quality control using QuickSight.

Enhancing software testing to with data-driven insights

Continuous improvement is a hallmark of leading organizations. Deep Pool wanted achieve greater software quality and decided to improve how it monitored and managed its software testing processes using data.

Typical development processes involve extensive testing. First, the original developer tests the code, and then the code is unit tested. Next, larger groups test the code. For all this testing to be successful and result in product improvement, it needs to be measured and tracked so that developers can learn from it and implement improvements during the development process.

Using QuickSight for data-driven insights, Deep Pool has implemented significant software testing and control. It can now count the number of bugs found or tests failed and time how long it took to create patches and repair issues. It can also better track its work backlog and the progress of functionality requests. Monitoring this information lets the company know that it has successfully implemented improvements, because a decreasing number of bugs over time is a strong indicator of quality control.

Monitoring development to increase efficiency

Better software test management benefits two groups: internal teams and external customers. Deep Pool can now log and communicate important information, such as when a request is made, how it’s being resolved or addressed, and how it’s being tested. In addition to helping internal teams streamline their processes, the company can also use this data to track communications with customers, which are also stored in its project management software. Such knowledge helps the company determine whether customer requests are being promptly addressed and identify common trends that require action on a larger scale.

Seven development teams at Deep Pool independently write the code for the components of the company’s software products, and they must integrate those components to create the final products. With the granularity of the data that is provided by QuickSight, Deep Pool can thoroughly analyze the development and testing of these products. The company now has the ability to trace software bugs down to their original coding, which makes it simple to quickly locate and address any issues that come up. Deep Pool can also measure the results of those mitigating actions and determine whether its repairs were successful.

Attention to detail helps Deep Pool improve software quality, leading to better products and customers who are more likely to give positive referrals.

“Amazon QuickSight is extremely valuable to us when performing quality control. We can now expose our whole development team to how we’re managing databases and servers and measuring performance in a more optimal way,” says Promisel.

Deep Pool has successfully proven that it can use QuickSight to measure its quality control to improve its software and, ultimately, better support its customers. Since the migration to AWS, the number of software issues discovered and logged has dropped by 57%.

The increased quality control that has come from the company’s focus on accessing all its data and optimizing its use has led to better efficiency, which results in the ability to expand its growth without increasing its costs.

“We should be able to increase our customer base without adding the equivalent costs. Making sure we are as efficient as possible lets me manage that way,” says Promisel.

Expanding into more data sources

Deep Pool is also exploring how to expand its use of QuickSight to extract and use data from even more of its databases. In the future, it hopes to analyze its internal metrics, such as sales data, and its external client-related information, such as assets and holdings, to guide how it builds its client software and provides even more custom products.

Deep Pool is also committed to helping its employees be innovative and successful by investing in their futures and skill sets. It understands that well-trained employees can optimize the use of their tools, which results in better products. As such, the company will continue to invest in the training offered by AWS. Using cutting-edge tools and promoting its intent to invest in its employees indicate to Deep Pool’s customers that the company plans to stay innovative and ahead of the technological curve.

To learn more about how QuickSight can help your business with dashboards, reports, and more, visit Amazon QuickSight.

About the Author

Shruthi Panicker is a Senior Product Marketing Manager with Amazon QuickSight at AWS. As an engineer turned product marketer, Shruthi has spent over 15 years in the technology industry in various roles from software engineering, to solution architecting to product marketing. She is passionate about working at the intersection of technology and business to tell great product stories that help drive customer value.

Visualize Confluent data in Amazon QuickSight using Amazon Athena

Post Syndicated from Ahmed Zamzam original https://aws.amazon.com/blogs/big-data/visualize-confluent-data-in-amazon-quicksight-using-amazon-athena/

This is a guest post written by Ahmed Saef Zamzam and Geetha Anne from Confluent.

Businesses are using real-time data streams to gain insights into their company’s performance and make informed, data-driven decisions faster. As real-time data has become essential for businesses, a growing number of companies are adapting their data strategy to focus on data in motion. Event streaming is the central nervous system of a data in motion strategy and, in many organizations, Apache Kafka is the tool that powers it.

Today, Kafka is well known and widely used for streaming data. However, managing and operating Kafka at scale can still be challenging. Confluent offers a solution through its fully managed, cloud-native service that simplifies running and operating data streams at scale. Confluent extends open-source Kafka through a suite of related services and features designed to enhance the data in motion experience for operators, developers, and architects in production.

In this post, we demonstrate how Amazon Athena, Amazon QuickSight, and Confluent work together to enable visualization of data streams in near-real time. We use the Kafka connector in Athena to do the following:

  • Join data inside Confluent with data stored in one of the many data sources supported by Athena, such as Amazon Simple Storage Service (Amazon S3)
  • Visualize Confluent data using QuickSight


Purpose-built stream processing engines, like Confluent ksqlDB, often provide SQL-like semantics for real-time transformations, joins, aggregations, and filters on streaming data. With ksqlDB, you can create persistent queries, which continuously process streams of events according to specific logic, and materialize streaming data in views that can be queried at a point in time (pull queries) or subscribed to by clients (push queries).

ksqlDB is one solution that made stream processing accessible to a wider range of users. However, pull queries, like those supported by ksqlDB, may not be suitable for all stream processing use cases, and there may be complexities or unique requirements that pull queries are not designed for.

Data visualization for Confluent data

A frequent use case for enterprises is data visualization. To visualize data stored in Confluent, you can use one of over 120 pre-built connectors, provided by Confluent, to write streaming data to a destination data store of your choice. Next, you connect your business intelligence (BI) tool to the data store to begin visualizing the data.

The following diagram depicts a typical architecture utilized by many Confluent customers. In this workflow, data is written to Amazon S3 through the Confluent S3 sink connector and then analyzed with Athena, a serverless interactive analytics service that enables you to analyze and query data stored in Amazon S3 and various other data sources using standard SQL. You can then use Athena as an input data source to QuickSight, a highly scalable cloud native BI service, for further analysis.

typical architecture utilized by many Confluent customers.

Although this approach works well for many use cases, it requires data to be moved, and therefore duplicated, before it can be visualized. This duplication not only adds time and effort for data engineers who may need to develop and test new scripts, but also creates data redundancy, making it more challenging to manage and secure the data, and increases storage cost.

Enriching data with reference data in another data store

With ksqlDB queries, the source and destination are always Kafka topics. Therefore, if you have a data stream that you need to enrich with external reference data, you have two options. One option is to import the reference data into Confluent, model it as a table, and use ksqlDB’s stream-table join to enrich the stream. The other option is to ingest the data stream into a separate data store and perform join operations there. Both require data movement and result in duplicate data storage.

Solution overview

So far, we have discussed two challenges that are not addressed by conventional stream processing tools. Is there a solution that addresses both challenges simultaneously?

When you want to analyze data without separate pipelines and jobs, a popular choice is Athena. With Athena, you can run SQL queries on a wide range of data sources—in addition to Amazon S3—without learning a new language, developing scripts to extract (and duplicate) data, or managing infrastructure.

Recently, Athena announced a connector for Kafka. Like Athena’s other connectors, queries on Kafka are processed within Kafka and return results to Athena. The connector supports predicate pushdown, which means that adding filters to your queries can reduce the amount of data scanned, improve query performance, and reduce cost.

For example, when using this connector, the amount of data scanned by the query SELECT * FROM CONFLUENT_TABLE could be significantly higher than the amount of data scanned by the query SELECT * FROM CONFLUENT_TABLE WHERE COUNTRY = 'UK'. The reason is that the AWS Lambda function which provides the runtime environment for the Athena connector, filters data at the source before returning it to Athena.

Let’s assume we have a stream of online transactions flowing into Confluent and customer reference data stored in Amazon S3. We want to use Athena to join both data sources together and produce a new dataset for QuickSight. Instead of using the S3 sink connector to load data into Amazon S3, we use Athena to query Confluent and join it with S3 data—all without moving data. The following diagram illustrates this architecture.

Athena to join both data sources together and produce a new dataset for QuickSight

We perform the following steps:

  1. Register the schema of your Confluent data.
  2. Configure the Athena connector for Kafka.
  3. Optionally, interactively analyze Confluent data.
  4. Create a QuickSight dataset using Athena as the source.

Register the schema

To connect Athena to Confluent, the connector needs the schema of the topic to be registered in the AWS Glue Schema Registry, which Athena uses for query planning.

The following is a sample record in Confluent:

  "transaction_id": "23e5ed25-5818-4d4f-acb3-73ef04d51d21",
  "customer_id": "126-58-9758",
  "amount": 986,
  "timestamp": "2023-01-03T15:40:42",
  "product_category": "health_fitness"

The following is the schema of this record:

  "topicName": "transactions",
  "message": {
    "dataFormat": "json",
    "fields": [
        "name": "transaction_id",
        "mapping": "transaction_id",
        "type": "VARCHAR"
        "name": "customer_id",
        "mapping": "customer_id",
        "type": "VARCHAR"
        "name": "amount",
        "mapping": "amount",
        "type": "INTEGER"
        "name": "timestamp",
        "mapping": "timestamp",
        "type": "timestamp",
        "formatHint": "yyyy-MM-dd\'T\'HH:mm:ss"
        "name": "product_category",
        "mapping": "product_category",
        "type": "VARCHAR"
        "name": "customer_id",
        "mapping": "customer_id",
        "type": "VARCHAR"

The data producer writing the data can register this schema with the AWS Glue Schema Registry. Alternatively, you can use the AWS Management Console or AWS Command Line Interface (AWS CLI) to create a schema manually.

We create the schema manually by running the following CLI command. Replace <registry_name> with your registry name and make sure that the text in the description field includes the required string {AthenaFederationKafka}:

aws glue create-registry –registry-name <registry_name> --description {AthenaFederationKafka}

Next, we run the following command to create a schema inside the newly created schema registry:

aws glue create-schema –registry-id RegistryName=<registry_name> --schema-name <schema_name> --compatibility <Compatibility_Mode> --data-format JSON –schema-definition <Schema>

Before running the command, be sure to provide the following details:

  • Replace <registry_name> with our AWS Glue Schema Registry name
  • Replace <schema_name> with the name of our Confluent Cloud topic, for example, transactions
  • Replace <Compatibility_Mode> with one of the supported compatibility modes, for example, ‘Backward’
  • Replace <Schema> with our schema

Configure and deploy the Athena Connector

With our schema created, we’re ready to deploy the Athena connector. Complete the following steps:

  1. On the Athena console, choose Data sources in the navigation pane.
  2. Choose Create data source.
  3. Search for and select Apache Kafka.
    Add Apache Kafka as data source
  4. For Data source name, enter the name for the data source.
    Enter name for data source

This data source name will be referenced in your queries. For example:

FROM <data_source_name>.<registry_name>.<schema_name>

Applying this to our use case and previously defined schema, our query would be as follows:

FROM "Confluent"."transactions_db"."transactions"
WHERE product_category='Kids'
  1. In the Connection details section, choose Create Lambda function.
    create lambda function

You’re redirected to the Applications page on the Lambda console. Some of the application settings are already filled.

The following are the important settings required for integrating with Confluent Cloud. For more information on these settings, refer to Parameters.

  1. For LambdaFunctionName, enter the name for the Lambda function the connector will use. For example, athena_confluent_connector.

We use this parameter in the next step.

  1. For KafkaEndpoint, enter the Confluent Cloud bootstrap URL.

You can find this on the Cluster settings page in the Confluent Cloud UI.

enter the Confluent Cloud bootstrap URL

Confluent Cloud supports two authentication mechanisms: OAuth and SASL/PLAIN (API keys). The connector doesn’t support OAuth; this leaves us with SASL/PLAIN. SASL/PLAIN uses SSL as a security protocol and PLAIN as SASL mechanism.

  1. For AuthType, enter SASL_SSL_PLAIN.

The API key and secret used by the connector to access Confluent need to be stored in AWS Secrets Manager.

  1. Get your Confluent API key or create a new one.
  2. Run the following AWS CLI command to create the secret in Secrets Manager:
    aws secretsmanager create-secret \
        --name <SecretNamePrefix>\
        --secret-string "{\"username\":\"<Confluent_API_KEY>\",\"password\":\"<Confluent_Secret>\"}"

The secret string should have two key-value pairs, one named username and the other password.

  1. For SecretNamePrefix, enter the secret name prefix created in the previous step.
  2. If the Confluent cloud cluster is reachable over the internet, leave SecurityGroupIds and SubnetIds blank. Otherwise, your Lambda function needs to run in a VPC that has connectivity to your Confluent Cloud network. Therefore, enter a security group ID and three private subnet IDs in this VPC.
  3. For SpillBucket, enter the name of an S3 bucket where the connector can spill data.

Athena connectors temporarily store (spill) data to Amazon S3 for further processing by Athena.

  1. Select I acknowledge that this app creates custom IAM roles and resource policies.
  2. Choose Deploy.
  3. Return to the Connection details section on the Athena console and for Lambda, enter the name of the Lambda function you created.
  4. Choose Next.
    Return to the Connection details section on the Athena console and for Lambda, enter the name of the Lambda function you created. And Choose Next.
  5. Choose Create data source.

Perform interactive analysis on Confluent data

With the Athena connector set up, our streaming data is now queryable from the same service we use to analyze S3 data lakes. Next, we use Athena to conduct point-in-time analysis of transactions flowing through Confluent Cloud.


We can use standard SQL functions to aggregate the data. For example, we can get the revenue by product category:

SELECT product_category, SUM(amount) AS Revenue
FROM "Confluent"."athena_blog"."transactions"
GROUP BY product_category
ORDER BY Revenue desc

SQL function to aggregate data

Enrich transaction data with customer data

The aggregation example is also available with ksqlDB pull queries. However, Athena’s connector allows us to join the data with other data sources like Amazon S3.

In our use case, the transactions streamed to Confluent Cloud lack detailed information about customers, apart from a customer_id. However, we have a reference dataset in Amazon S3 that has more information about the customers. With Athena, we can join both datasets together to gain insights about our customers. See the following code:

FROM "Confluent"."athena_blog"."transactions" a
INNER JOIN "AwsDataCatalog"."athenablog"."customer" b 
ON a.customer_id=b.customer_id

join data

You can see from the results that we were able to enrich the streaming data with customer details, stored in Amazon S3, including name and address.

Visualize data using QuickSight

Another powerful feature this connector brings is the ability to visualize data stored in Confluent using any BI tool that supports Athena as a data source. In this post, we use QuickSight. QuickSight is a machine learning (ML)-powered BI service built for the cloud. You can use it to deliver easy-to-understand insights to the people you work with, wherever they are.

For more information about signing up for QuickSight, see Signing up for an Amazon QuickSight subscription.

Complete the following steps to visualize your streaming data with QuickSight:

  1. On the QuickSight console, choose Datasets in the navigation pane.
  2. Choose New dataset.
  3. Choose Athena as the data source.
  4. For Data source name, enter a name.
  5. Choose Create data source.
  6. In the Choose your table section, choose Use custom SQL.
    In the Choose your table section, choose Use custom SQL.
  7. Enter the join query like the one given previously, then choose Confirm query.
    Enter the join query like the one given previously, then choose Confirm query.
  8. Next, choose to import the data into SPICE (Super-fast, Parallel, In-memory Calculation Engine), a fully managed in-memory cache that boosts performance, or directly query the data.

Utilizing SPICE will enhance performance, but the data may need to be periodically updated. You can choose to incrementally refresh your dataset or schedule regular refreshes with SPICE. If you want near-real-time data reflected in your dashboards, select Directly query your data. Note that with the direct query option, user actions in QuickSight, such as applying a drill-down filter, may invoke a new Athena query.

  1. Choose Visualize.
    Choose Visualize

That’s it, we have successfully connected QuickSight to Confluent through Athena. With just a few clicks, you can create a few visuals displaying data from Confluent.

successfully connected QuickSight to Confluent through Athena.

Clean up

To avoid incurring ongoing charges, delete the resources you provisioned by completing the following steps:

  1. Delete the AWS Glue schema and registry.
  2. Delete the Athena Kafka connector.
  3. Delete the QuickSight dataset.


In this post, we discussed use cases for Athena and Confluent. We provided examples of how you can use both for near-real-time data visualization with QuickSight and interactive analysis involving joins between streaming data in Confluent and data stored in Amazon S3.

The Athena connector for Kafka simplifies the process of querying and analyzing streaming data from Confluent Cloud. It removes the need to first move streaming data to persistent storage before it can be used in downstream use cases like business intelligence. This complements the existing integration between Confluent and Athena, using the S3 sink connector, which enables loading streaming data into a data lake, and is an additional option for customers who want to enable interactive analysis on Confluent data.

About the authors

Ahmed Zamzam is a Senior Partner Solutions Architect at Confluent, with a focus on the AWS partnership. In his role, he works with customers in the EMEA region across various industries to assist them in building applications that leverage their data using Confluent and AWS. Prior to Confluent, Ahmed was a Specialist Solutions Architect for Analytics AWS specialized in data streaming and search. In his free time, Ahmed enjoys traveling, playing tennis, and cycling.

Geetha Anne is a Partner Solutions Engineer at Confluent with previous experience in implementing solutions for data-driven business problems on the cloud, involving data warehousing and real-time streaming analytics. She fell in love with distributed computing during her undergraduate days and has followed her interest ever since. Geetha provides technical guidance, design advice, and thought leadership to key Confluent customers and partners. She also enjoys teaching complex technical concepts to both tech-savvy and general audiences.

Manage users and group memberships on Amazon QuickSight using SCIM events generated in IAM Identity Center with Azure AD

Post Syndicated from Wakana Vilquin-Sakashita original https://aws.amazon.com/blogs/big-data/manage-users-and-group-memberships-on-amazon-quicksight-using-scim-events-generated-in-iam-identity-center-with-azure-ad/

Amazon QuickSight is cloud-native, scalable business intelligence (BI) service that supports identity federation. AWS Identity and Access Management (IAM) allows organizations to use the identities managed in their enterprise identity provider (IdP) and federate single sign-on (SSO) to QuickSight. As more organizations are building centralized user identity stores with all their applications, including on-premises apps, third-party apps, and applications on AWS, they need a solution to automate user provisioning into these applications and keep their attributes in sync with their centralized user identity store.

When architecting a user repository, some organizations decide to organize their users in groups or use attributes (such as department name), or a combination of both. If your organization uses Microsoft Azure Active Directory (Azure AD) for centralized authentication and utilizes its user attributes to organize the users, you can enable federation across all QuickSight accounts as well as manage users and their group membership in QuickSight using events generated in the AWS platform. This allows system administrators to centrally manage user permissions from Azure AD. Provisioning, updating, and de-provisioning users and groups in QuickSight no longer requires management in two places with this solution. This makes sure that users and groups in QuickSight stay consistent with information in Azure AD through automatic synchronization.

In this post, we walk you through the steps required to configure federated SSO between QuickSight and Azure AD via AWS IAM Identity Center (Successor to AWS Single Sign-On) where automatic provisioning is enabled for Azure AD. We also demonstrate automatic user and group membership update using a System for Cross-domain Identity Management (SCIM) event.

Solution overview

The following diagram illustrates the solution architecture and user flow.

solution architecture and user flow.

In this post, IAM Identity Center provides a central place to bring together administration of users and their access to AWS accounts and cloud applications. Azure AD is the user repository and configured as the external IdP in IAM Identity Center. In this solution, we demonstrate the use of two user attributes (department, jobTitle) specifically in Azure AD. IAM Identity Center supports automatic provisioning (synchronization) of user and group information from Azure AD into IAM Identity Center using the SCIM v2.0 protocol. With this protocol, the attributes from Azure AD are passed along to IAM Identity Center, which inherits the defined attribute for the user’s profile in IAM Identity Center. IAM Identity Center also supports identity federation with SAML (Security Assertion Markup Language) 2.0. This allows IAM Identity Center to authenticate identities using Azure AD. Users can then SSO into applications that support SAML, including QuickSight. The first half of this post focuses on how to configure this end to end (see Sign-In Flow in the diagram).

Next, user information starts to get synchronized between Azure AD and IAM Identity Center via SCIM protocol. You can automate creating a user in QuickSight using an AWS Lambda function triggered by the CreateUser SCIM event originated from IAM Identity Center, which was captured in Amazon EventBridge. In the same Lambda function, you can subsequently update the user’s membership by adding into the specified group (whose name is comprised of two user attributes: department-jobTitle, otherwise create the group if it doesn’t exist yet, prior to adding the membership.

In this post, this automation part is omitted because it would be redundant with the content discussed in the following sections.

This post explores and demonstrates an UpdateUser SCIM event triggered by the user profile update on Azure AD. The event is captured in EventBridge, which invokes a Lambda function to update the group membership in QuickSight (see Update Flow in the diagram). Because a given user is supposed to belong to only one group at a time in this example, the function will replace the user’s current group membership with the new one.

In Part I, you set up SSO to QuickSight from Azure AD via IAM Identity Center (the sign-in flow):

  1. Configure Azure AD as the external IdP in IAM Identity Center.
  2. Add and configure an IAM Identity Center application in Azure AD.
  3. Complete configuration of IAM Identity Center.
  4. Set up SCIM automatic provisioning on both Azure AD and IAM Identity Center, and confirm in IAM Identity Center.
  5. Add and configure a QuickSight application in IAM Identity Center.
  6. Configure a SAML IdP and SAML 2.0 federation IAM role.
  7. Configure attributes in the QuickSight application.
  8. Create a user, group, and group membership manually via the AWS Command Line Interface (AWS CLI) or API.
  9. Verify the configuration by logging in to QuickSight from the IAM Identity Center portal.

In Part II, you set up automation to change group membership upon an SCIM event (the update flow):

  1. Understand SCIM events and event patterns for EventBridge.
  2. Create attribute mapping for the group name.
  3. Create a Lambda function.
  4. Add an EventBridge rule to trigger the event.
  5. Verify the configuration by changing the user attribute value at Azure AD.


For this walkthrough, you should have the following prerequisites:

  • IAM Identity Center. For instructions, refer to Steps 1–2 in the AWS IAM Identity Center Getting Started guide.
  • A QuickSight account subscription.
  • Basic understanding of IAM and privileges required to create an IAM IdP, roles, and policies.
  • An Azure AD subscription. You need at least one user with the following attributes to be registered in Azure AD:
    • userPrincipalName – Mandatory field for Azure AD user.
    • displayName – Mandatory field for Azure AD user.
    • Mail – Mandatory field for IAM Identity Center to work with QuickSight.
    • jobTitle – Used to allocate user to group
    • department – Used to allocate user to group.
    • givenName – Optional field.
    • surname – Optional field.

Part I: Set up SSO to QuickSight from Azure AD via IAM Identity Center

This section presents the steps to set up the sign-in flow.

Configure an external IdP as Azure AD in IAM Identity Center

To configure your external IdP, complete the following steps:

  1. On the IAM Identity Center console, choose Settings.
  2. Choose Actions on the Identity source tab, then choose Change identity source.
  3. Choose External identity provider, then choose Next.

The IdP metadata is displayed. Keep this browser tab open.

Add and configure an IAM Identity Center application in Azure AD

To set up your IAM Identity Center application, complete the following steps:

  1. Open a new browser tab.
  2. Log in to the Azure AD portal using your Azure administrator credentials.
  3. Under Azure services, choose Azure Active Directory.
  4. In the navigation pane, under Manage, choose Enterprise applications, then choose New application.
  5. In the Browse Azure AD Galley section, search for IAM Identity Center, then choose AWS IAM Identity Center (successor to AWS Single Sign-On).
  6. Enter a name for the application (in this post, we use IIC-QuickSight) and choose Create.
  7. In the Manage section, choose Single sign-on, then choose SAML.
  8. In the Assign users and groups section, choose Assign users and groups.
  9. Choose Add user/group and add at least one user.
  10. Select User as its role.
  11. In the Set up single sign on section, choose Get started.
  12. In the Basic SAML Configuration section, choose Edit, and fill out following parameters and values:
  13. Identifier – The value in the IAM Identity Center issuer URL field.
  14. Reply URL – The value in the IAM Identity Center Assertion Consumer Service (ACS) URL field.
  15. Sign on URL – Leave blank.
  16. Relay State – Leave blank.
  17. Logout URL – Leave blank.
  18. Choose Save.

The configuration should look like the following screenshot.


  1. In the SAML Certificates section, download the Federation Metadata XML file and the Certificate (Raw) file.
    Federation Metadata XML file and the Certificate (Raw) file

You’re all set with Azure AD SSO configuration at this moment. Later on, you’ll return to this page to configure automated provisioning, so keep this browser tab open.

Complete configuration of IAM Identity Center

Complete your IAM Identity Center configuration with the following steps:

  1. Go back to the browser tab for IAM Identity Center console which you have kept open in previous step.
  2. For IdP SAML metadata under the Identity provider metadata section, choose Choose file.
  3. Choose the previously downloaded metadata file (IIC-QuickSight.xml).
  4. For IdP certificate under the Identity provider metadata section, choose Choose file.
  5. Choose the previously downloaded certificate file (IIC-QuickSight.cer).
  6. Choose Next.
  7. Enter ACCEPT, then choose Change Identity provider source.

Set up SCIM automatic provisioning on both Azure AD and IAM Identity Center

Your provisioning method is still set as Manual (non-SCIM). In this step, we enable automatic provisioning so that IAM Identity Center becomes aware of the users, which allows identity federation to QuickSight.

  1. In the Automatic provisioning section, choose Enable.
    choose Enable
  2. Choose Access token to show your token.
    access token
  3. Go back to the browser tab (Azure AD), which you kept open in Step 1.
  4. In the Manage section, choose Enterprise applications.
  5. Choose IIC-QuickSight, then choose Provisioning.
  6. Choose Automatic in Provisioning Mode and enter the following values:
  7. Tenant URL – The value in the SCIM endpoint field.
  8. Secret Token – The value in the Access token field.
  9. Choose Test Connection.
  10. After the test connection is successfully complete, set Provisioning Status to On.
    set Provisioning Status to On
  11. Choose Save.
  12. Choose Start provisioning to start automatic provisioning using the SCIM protocol.

When provisioning is complete, it will result in propagating one or more users from Azure AD to IAM Identity Center. The following screenshot shows the users that were provisioned in IAM Identity Center.

the users that were provisioned in IAM Identity Center

Note that upon this SCIM provisioning, the users in QuickSight should be created using the Lambda function triggered by the event originated from IAM Identity Center. In this post, we create a user and group membership via the AWS CLI (Step 8).

Add and configure a QuickSight application in IAM Identity Center

In this step, we create a QuickSight application in IAM Identity Center. You also configure an IAM SAML provider, role, and policy for the application to work. Complete the following steps:

  1. On the IAM Identity Center console, on the Applications page, choose Add Application.
  2. For Pre-integrated application under Select an application, enter quicksight.
  3. Select Amazon QuickSight, then choose Next.
  4. Enter a name for Display name, such as Amazon QuickSight.
  5. Choose Download under IAM Identity Center SAML metadata file and save it in your computer.
  6. Leave all other fields as they are, and save the configuration.
  7. Open the application you’ve just created, then choose Assign Users.

The users provisioned via SCIM earlier will be listed.

  1. Choose all of the users to assign to the application.

Configure a SAML IdP and a SAML 2.0 federation IAM role

To set up your IAM SAML IdP for IAM Identity Center and IAM role, complete the following steps:

  1. On the IAM console, in the navigation pane, choose Identity providers, then choose Add provider.
  2. Choose SAML as Provider type, and enter Azure-IIC-QS as Provider name.
  3. Under Metadata document, choose Choose file and upload the metadata file you downloaded earlier.
  4. Choose Add provider to save the configuration.
  5. In the navigation pane, choose Roles, then choose Create role.
  6. For Trusted entity type, select SAML 2.0 federation.
  7. For Choose a SAML 2.0 provider, select the SAML provider that you created, then choose Allow programmatic and AWS Management Console access.
  8. Choose Next.
  9. On the Add Permission page, choose Next.

In this post, we create QuickSight users via an AWS CLI command, therefore we’re not creating any permission policy. However, if the self-provisioning feature in QuickSight is required, the permission policy for the CreateReader, CreateUser, and CreateAdmin actions (depending on the role of the QuickSight users) is required.

  1. On the Name, review, and create page, under Role details, enter qs-reader-azure for the role.
  2. Choose Create role.
  3. Note the ARN of the role.

You use the ARN to configure attributes in your IAM Identity Center application.

Configure attributes in the QuickSight application

To associate the IAM SAML IdP and IAM role to the QuickSight application in IAM Identity Center, complete the following steps:

  1. On the IAM Identity Center console, in the navigation pane, choose Applications.
  2. Select the Amazon QuickSight application, and on the Actions menu, choose Edit attribute mappings.
  3. Choose Add new attribute mapping.
  4. Configure the mappings in the following table.
User attribute in the application Maps to this string value or user attribute in IAM Identity Center
Subject ${user:email}
https://aws.amazon.com/SAML/Attributes/RoleSessionName ${user:email}
https://aws.amazon.com/SAML/Attributes/Role arn:aws:iam::<ACCOUNTID>:role/qs-reader-azure,arn:aws:iam::<ACCOUNTID>:saml-provider/Azure-IIC-QS
https://aws.amazon.com/SAML/Attributes/PrincipalTag:Email ${user:email}

Note the following values:

  • Replace <ACCOUNTID> with your AWS account ID.
  • PrincipalTag:Email is for the email syncing feature for self-provisioning users that need to be enabled on the QuickSight admin page. In this post, don’t enable this feature because we register the user with an AWS CLI command.
  1. Choose Save changes.

Create a user, group, and group membership with the AWS CLI

As described earlier, users and groups in QuickSight are being created manually in this solution. We create them via the following AWS CLI commands.

The first step is to create a user in QuickSight specifying the IAM role created earlier and email address registered in Azure AD. The second step is to create a group with the group name as combined attribute values from Azure AD for the user created in the first step. The third step is to add the user into the group created earlier; member-name indicates the user name created in QuickSight that is comprised of <IAM Role name>/<session name>. See the following code:

aws quicksight register-user \
--aws-account-id <ACCOUNTID> --namespace default \
--identity-type IAM --email <email registered in Azure AD> \
--user-role READER --iam-arn arn:aws:iam::<ACCOUNTID>:role/qs-reader-azure \
--session-name <email registered in Azure AD>

 aws quicksight create-group \
--aws-account-id <ACCOUNTID> --namespace default \
--group-name Marketing-Specialist

 aws quicksight create-group-membership \
--aws-account-id <ACCOUNTID> --namespace default \
--member-name qs-reader-azure/<email registered in Azure AD> \
–-group-name Marketing-Specialist

At this point, the end-to-end configuration of Azure AD, IAM Identity Center, IAM, and QuickSight is complete.

Verify the configuration by logging in to QuickSight from the IAM Identity Center portal

Now you’re ready to log in to QuickSight using the IdP-initiated SSO flow:

  1. Open a new private window in your browser.
  2. Log in to the IAM Identity Center portal (https://d-xxxxxxxxxx.awsapps.com/start).

You’re redirected to the Azure AD login prompt.

  1. Enter your Azure AD credentials.

You’re redirected back to the IAM Identity Center portal.

  1. In the IAM Identity Center portal, choose Amazon QuickSight.

IAM Identity Center portal, choose Amazon QuickSight

You’re automatically redirected to your QuickSight home.
automatically redirected to your QuickSight home

Part II: Automate group membership change upon SCIM events

In this section, we configure the update flow.

Understand the SCIM event and event pattern for EventBridge

When an Azure AD administrator makes any changes to the attributes on the particular user profile, the change will be synced with the user profile in IAM Identity Center via SCIM protocol, and the activity is recorded in an AWS CloudTrail event called UpdateUser by sso-directory.amazonaws.com (IAM Identity Center) as the event source. Similarly, the CreateUser event is recorded when a user is created on Azure AD, and the DisableUser event is for when a user is disabled.

The following screenshot on the  Event history page shows two CreateUser events: one is recorded by IAM Identity Center, and the other one is by QuickSight. In this post, we use the one from IAM Identity Center.

CloudTrail console

In order for EventBridge to be able to handle the flow properly, each event must specify the fields of an event that you want the event pattern to match. The following event pattern is an example of the UpdateUser event generated in IAM Identity Center upon SCIM synchronization:

  "source": ["aws.sso-directory"],
  "detail-type": ["AWS API Call via CloudTrail"],
  "detail": {
    "eventSource": ["sso-directory.amazonaws.com"],
    "eventName": ["UpdateUser"]

In this post, we demonstrate an automatic update of group membership in QuickSight that is triggered by the UpdateUser SCIM event.

Create attribute mapping for the group name

In order for the Lambda function to manage group membership in QuickSight, it must obtain the two user attributes (department and jobTitle). To make the process simpler, we’re combining two attributes in Azure AD (department, jobTitle) into one attribute in IAM Identity Center (title), using the attribute mappings feature in Azure AD. IAM Identity Center then uses the title attribute as a designated group name for this user.

  1. Log in to the Azure AD console, navigate to Enterprise Applications, IIC-QuickSight, and Provisioning.
  2. Choose Edit attribute mappings.
  3. Under Mappings, choose Provision Azure Active Directory Users.
    Azure AD console, Under mappings
  4. Choose jobTitle from the list of Azure Active Directory Attributes.
  5. Change the following settings:
    1. Mapping TypeExpression
    2. ExpressionJoin("-", [department], [jobTitle])
    3. Target attribute title
      update settings
  6. Choose Save.
  7. You can leave the provisioning page.

The attribute is automatically updated in IAM Identity Center. The updated user profile looks like the following screenshots (Azure AD on the left, IAM Identity Center on the right).

updated user profile
Job related information

Create a Lambda function

Now we create a Lambda function to update QuickSight group membership upon the SCIM event. The core part of the function is to obtain the user’s title attribute value in IAM Identity Center based on the triggered event information, and then to ensure that the user exists in QuickSight. If the group name doesn’t exist yet, it creates the group in QuickSight and then adds the user into the group. Complete the following steps:

  1. On the Lambda console, choose Create function.
  2. For Name, enter UpdateQuickSightUserUponSCIMEvent.
  3. For Runtime, choose Python 3.9.
  4. For Time Out, set to 15 seconds.
  5. For Permissions, create and attach an IAM role that includes the following permissions (the trusted entity (principal) should be lambda.amazonaws.com):
        "Version": "2012-10-17",
        "Statement": [
                "Sid": "MinimalPrivForScimQsBlog",
                "Effect": "Allow",
                "Action": [
                "Resource": "*"

  6. Write Python code using the Boto3 SDK for IdentityStore and QuickSight. The following is the entire sample Python code:
import sys
import boto3
import json
import logging
from time import strftime
from datetime import datetime

# Set logging
logger = logging.getLogger()

def lambda_handler(event, context):
  Modify QuickSight group membership upon SCIM event from IAM Identity Center originated from Azure AD.
  It works in this way:
    Azure AD -> SCIM -> Identity Center -> CloudTrail -> EventBridge -> Lambda -> QuickSight
  Note that this is a straightforward sample to show how to update QuickSight group membership upon certain SCIM event.
  For example, it assumes that 1:1 user-to-group assigmnent, only one (combined) SAML attribute, etc. 
  For production, take customer requirements into account and develop your own code.

  # Setting variables (hard-coded. get dynamically for production code)
  qs_namespace_name = 'default'
  qs_iam_role = 'qs-reader-azure'

  # Obtain account ID and region
  account_id = boto3.client('sts').get_caller_identity()['Account']
  region = boto3.session.Session().region_name

  # Setup clients
  qs = boto3.client('quicksight')
  iic = boto3.client('identitystore')

  # Check boto3 version
  logger.debug(f"## Your boto3 version: {boto3.__version__}")

  # Get user info from event data
  event_json = json.dumps(event)
  logger.debug(f"## Event: {event_json}")
  iic_store_id = event['detail']['requestParameters']['identityStoreId']
  iic_user_id = event['detail']['requestParameters']['userId']  # For UpdateUser event, userId is provided through requestParameters
  logger.info("## Getting user info from Identity Store.")
    res_iic_describe_user = iic.describe_user(
      IdentityStoreId = iic_store_id,
      UserId = iic_user_id
  except Exception as e:
    logger.error("## Operation failed due to unknown error. Exiting.")
    logger.info(f"## User info retrieval succeeded.")
    azure_user_attribute_title = res_iic_describe_user['Title']
    azure_user_attribute_userprincipalname = res_iic_describe_user['UserName']
    qs_user_name = qs_iam_role + "/" + azure_user_attribute_userprincipalname
    logger.info(f"#### Identity Center user name: {azure_user_attribute_userprincipalname}")
    logger.info(f"#### QuickSight group name desired: {azure_user_attribute_title}")
    logger.debug(f"#### res_iic_describe_user: {json.dumps(res_iic_describe_user)}, which is {type(res_iic_describe_user)}")

  # Exit if user is not present since this function is supposed to be called by UpdateUser event
    # Get QuickSight user name
    res_qs_describe_user = qs.describe_user(
      UserName = qs_user_name,
      AwsAccountId = account_id,
      Namespace = qs_namespace_name
  except qs.exceptions.ResourceNotFoundException as e:
    logger.error(f"## User {qs_user_name} is not found in QuickSight.")
    logger.error(f"## Make sure the QuickSight user has been created in advance. Exiting.")
  except Exception as e:
    logger.error("## Operation failed due to unknown error. Exiting.")
    logger.info(f"## User {qs_user_name} is found in QuickSight.")

  # Remove current membership unless it's the desired one
  qs_new_group = azure_user_attribute_title  # Set "Title" SAML attribute as the desired QuickSight group name
  in_desired_group = False  # Set this flag True when the user is already a member of the desired group
  logger.info(f"## Starting group membership removal.")
    res_qs_list_user_groups = qs.list_user_groups(
      UserName = qs_user_name,
      AwsAccountId = account_id,
      Namespace = qs_namespace_name
  except Exception as e:
    logger.error("## Operation failed due to unknown error. Exiting.")
    # Skip if the array is empty (user is not member of any groups)
    if not res_qs_list_user_groups['GroupList']:
      logger.info(f"## User {qs_user_name} is not a member of any QuickSight group. Skipping removal.")
      for grp in res_qs_list_user_groups['GroupList']:
        qs_current_group = grp['GroupName']
        # Retain membership if the new and existing group names match
        if qs_current_group == qs_new_group:
          logger.info(f"## The user {qs_user_name} already belong to the desired group. Skipping removal.")
          in_desired_group = True
          # Remove all unnecessary memberships
          logger.info(f"## Removing user {qs_user_name} from existing group {qs_current_group}.")
            res_qs_delete_group_membership = qs.delete_group_membership(
              MemberName = qs_user_name,
              GroupName = qs_current_group,
              AwsAccountId = account_id,
              Namespace = qs_namespace_name
          except Exception as e:
            logger.error(f"## Operation failed due to unknown error. Exiting.")
            logger.info(f"## The user {qs_user_name} has removed from {qs_current_group}.")

  # Create group membership based on IIC attribute "Title"
  logger.info(f"## Starting group membership assignment.")
  if in_desired_group is True:
      logger.info(f"## The user already belongs to the desired one. Skipping assignment.")
      logger.info(f"## Checking if the desired group exists.")
      res_qs_describe_group = qs.describe_group(
        GroupName = qs_new_group,
        AwsAccountId = account_id,
        Namespace = qs_namespace_name
    except qs.exceptions.ResourceNotFoundException as e:
      # Create a QuickSight group if not present
      logger.info(f"## Group {qs_new_group} is not present. Creating.")
      today = datetime.now()
      res_qs_create_group = qs.create_group(
        GroupName = qs_new_group,
        Description = 'Automatically created at ' + today.strftime('%Y.%m.%d %H:%M:%S'),
        AwsAccountId = account_id,
        Namespace = qs_namespace_name
    except Exception as e:
      logger.error(f"## Operation failed due to unknown error. Exiting.")
      logger.info(f"## Group {qs_new_group} is found in QuickSight.")

    # Add the user to the desired group
    logger.info("## Modifying group membership based on its latest attributes.")
    logger.info(f"#### QuickSight user name: {qs_user_name}")
    logger.info(f"#### QuickSight group name: {qs_new_group}")
      res_qs_create_group_membership = qs.create_group_membership(
        MemberName = qs_user_name,
        GroupName = qs_new_group,
        AwsAccountId = account_id,
        Namespace = qs_namespace_name
    except Exception as e:
      logger.error("## Operation failed due to unknown error. Exiting.")
      logger.info("## Group membership modification succeeded.")
      qs_group_member_name = res_qs_create_group_membership['GroupMember']['MemberName']
      qs_group_member_arn = res_qs_create_group_membership['GroupMember']['Arn']
      logger.debug("## QuickSight group info:")
      logger.debug(f"#### qs_user_name: {qs_user_name}")
      logger.debug(f"#### qs_group_name: {qs_new_group}")
      logger.debug(f"#### qs_group_member_name: {qs_group_member_name}")
      logger.debug(f"#### qs_group_member_arn: {qs_group_member_arn}")
      logger.debug("## IIC info:")
      logger.debug(f"#### IIC user name: {azure_user_attribute_userprincipalname}")
      logger.debug(f"#### IIC user id: {iic_user_id}")
      logger.debug(f"#### Title: {azure_user_attribute_title}")
      logger.info(f"## User {qs_user_name} has been successfully added to the group {qs_new_group} in {qs_namespace_name} namespace.")
  # return response
  return {
    "namespaceName": qs_namespace_name,
    "userName": qs_user_name,
    "groupName": qs_new_group

Note that this Lambda function requires Boto3 1.24.64 or later. If the Boto3 included in the Lambda runtime is older than this, use a Lambda layer to use the latest version of Boto3. For more details, refer to How do I resolve “unknown service”, “parameter validation failed”, and “object has no attribute” errors from a Python (Boto 3) Lambda function.

Add an EventBridge rule to trigger the event

To create an EventBridge rule to invoke the previously created Lambda function, complete the following steps:

  1. On the EventBridge console, create a new rule.
  2. For Name, enter updateQuickSightUponSCIMEvent.
  3. For Event pattern, enter the following code:
      "source": ["aws.sso-directory"],
      "detail-type": ["AWS API Call via CloudTrail"],
      "detail": {
        "eventSource": ["sso-directory.amazonaws.com"],
        "eventName": ["UpdateUser"]

  4. For Targets, choose the Lambda function you created (UpdateQuickSightUserUponSCIMEvent).
  5. Enable the rule.

Verify the configuration by changing a user attribute value at Azure AD

Let’s modify a user’s attribute at Azure AD, and then check if the new group is created and that the user is added into the new one.

  1. Go back to the Azure AD console.
  2. From Manage, click Users.
  3. Choose one of the users you previously used to log in to QuickSight from the IAM Identity Center portal.
  4. Choose Edit properties, then edit the values for Job title and Department.
    Edit Properties
  5. Save the configuration.
  6. From Manage, choose Enterprise application, your application name, and Provisioning.
  7. Choose Stop provisioning and then Start provisioning in sequence.

In Azure AD, the SCIM provisioning interval is fixed to 40 minutes. To get immediate results, we manually stop and start the provisioning.

Provisioning status

  1. Navigate to the QuickSight console.
  2. On the drop-down user name menu, choose Manage QuickSight.
  3. Choose Manage groups.

Now you should find that the new group is created and the user is assigned to this group.

new group is created and the user is assigned to this group

Clean up

When you’re finished with the solution, clean up your environment to minimize cost impact. You may want to delete the following resources:

  • Lambda function
  • Lambda layer
  • IAM role for the Lambda function
  • CloudWatch log group for the Lambda function
  • EventBridge rule
  • QuickSight account
    • Note : There can only be one QuickSight account per AWS account. So your QuickSight account might already be used by other users in your organization. Delete the QuickSight account only if you explicitly set it up to follow this blog and are absolutely sure that it is not being used by any other users.
  • IAM Identity Center instance
  • IAM ID Provider configuration for Azure AD
  • Azure AD instance


This post provided step-by-step instructions to configure IAM Identity Center SCIM provisioning and SAML 2.0 federation from Azure AD for centralized management of QuickSight users. We also demonstrated automated group membership updates in QuickSight based on user attributes in Azure AD, by using SCIM events generated in IAM Identity Center and setting up automation with EventBridge and Lambda.

With this event-driven approach to provision users and groups in QuickSight, system administrators can have full flexibility in where the various different ways of user management could be expected depending on the organization. It also ensures the consistency of users and groups between QuickSight and Azure AD whenever a user accesses QuickSight.

We are looking forward to hearing any questions or feedback.

About the authors

Takeshi Nakatani is a Principal Bigdata Consultant on Professional Services team in Tokyo. He has 25 years of experience in IT industry, expertised in architecting data infrastructure. On his days off, he can be a rock drummer or a motorcyclyst.

Wakana Vilquin-Sakashita is Specialist Solution Architect for Amazon QuickSight. She works closely with customers to help making sense of the data through visualization. Previously Wakana worked for S&P Global  assisting customers to access data, insights and researches relevant for their business.

Amazon QuickSight helps TalentReef empower its customers to make more informed hiring decisions

Post Syndicated from Alexander Plumb original https://aws.amazon.com/blogs/big-data/amazon-quicksight-helps-talentreef-empower-its-customers-to-make-more-informed-hiring-decisions/

This post is co-written with Alexander Plumb, Product Manager at Mitratech.

TalentReef, now part of Mitratech, is a talent management platform purpose-built for location-based, high-volume hiring. TalentReef was acquired by Mitratech in August 2022 with the goal to combine TalentReef’s best-in-class systems with Mitratech’s expertise, technology, and global platform to ensure their customers’ hiring needs are serviced better and faster than anyone else in the industry.

The TalentReef team are experts in hourly recruiting, onboarding, and hiring, with the mission to help its customers engage with great candidates utilizing an intelligent, easy-to-use single platform. TalentReef differentiates itself from its competitors by not just building features, but creating an entire talent management ecosystem on the idea of eliminating friction and making the hiring and onboarding process as smooth and easy as possible for their customers and applicants.

TalentReef used Amazon QuickSight with the intent of replacing their legacy business intelligence (BI) reporting. The team found QuickSight easy to use and developed two new dashboards that replaced dozens of legacy reports. The response has been overwhelmingly positive, leading to the development of two additional analytics dashboards, Job Postings and Onboarding, both set to be released in the first half of 2023.

The following screenshot shows the Applicant dashboard, which is used internally by TalentReef Customer Solution Managers as well as externally directly by customers embedded within the talent management application. This dashboard provides quick access to their customers’ important metrics. For example, it shows the total number of applicants for all the job postings. It also shows how many applicants are present in the system by position.
full TalentReef dashboard

Providing clarity to customers for hourly workforce hiring

The war for talent is top of mind for those hiring within the hourly workforce. Hiring managers are constantly looking for top talent and trying to understand where they came from, why they are applying, and more. They want to see how their job postings are performing, if there is a drop in any posting, and opportunities to optimize their process. TalentReef’s previous solution wasn’t designed to convey this information, and required manual intervention to extract these hidden insights from multiple reports.

With the new dashboards embedded directly into TalentReef’s customer view, the development team is able to streamline their data ingestion process to ensure up-to-date data is available to their customers within the TalentReef platform. QuickSight features such as forecasts, cross-sheet filtering, and the ability to drill into underlying data allows customers to quickly see the value through different lenses.

Whenever a new feature was rolled out in the previous solution, it wasn’t possible to gauge the impact it had on applicants and new hires because it required a lot of manual work. Development teams had to provide a raw data file to internal users, upon request, to show the value of the new feature, and even then it was limited in how they could show value. With QuickSight, not only are they able to show the value of new features quickly, but they can do so without development intervention.

Data visualization helps business analysts scale client support

The sheer volume of our datasets made gathering insights a slow process. Not only that, but datasets weren’t accessible to a wide audience outside our team, such as partners, program managers, product managers, and so on. As a result, Business Intelligence Engineers (BIEs) spent a lot of time writing ad hoc queries, which then took a long time to run. When the insights were ready, BIEs were tasked with answering questions via manual processes that didn’t scale.

On September 6, 2022, TalentReef launched two new analytics dashboards, Applicant and Hire, which are embedded into their customer application. Since the launch, TalentReef has seen usage increase over 20% and has saved manual internal resources hours of their time putting together insights for their customer base during QBR calls that now can be accessed directly from the dashboards. With TalentReef’s previous tool, reports were unstable and would time out, which required development teams to troubleshoot and repair. Since implementing QuickSight, TalentReef has found efficiencies for both internal resources as well as customer hiring managers, and are confident in the ability to meet the demand of these users.

The following image demonstrates UTM parameters (Urchin Tracking Modules—a tracking device that helps get really specific with the traffic source). This dashboard enables TalentReef’s customer base to understand where their applicants are coming from, so they know where to invest their recruitment dollars (whether the applicants came from indeed.com, or google.com, and so on). This embedded dashboard even allows users to drill further into their data, understanding the name, date, location, and more that the UTM source is tied to.

UTM Parameters

QuickSight has allowed TalentReef to unlock insights that were not previously attainable, or very manual to derive, from their previous reporting tool. An example of this in the following image is the average time to review an application. In the war for talent, minutes can make a difference between finding the individuals needed to fill a position or letting them slip through the cracks. This type of information gives leadership advantage to know where to focus their attention and help win the war for talent in the hourly workforce.

Applicants over time

Unlock the power of applicant and hire data to get insights you never had before!

Our customers have been extremely impressed with our QuickSight dashboards because they provide information that was previously unavailable, without manual effort by development teams. The interactive nature of the QuickSight dashboards allows TalentReef’s customer base to dive deeper into the applicants and hired candidates, for example to understand from where an applicant came from or how an applicant applied to a job posting.

With QuickSight, not only can we visualize applicant and hire data in multiple, meaningful ways for our customer base, but also we can help them see the ROI from additional products they’ve added on to the platform. In the following example, we have a variety of filters that allow clients to see if their sponsorship dollars are returning successful hiring applications, if their add-on of chat apply brings higher application volume, if the applicant came from text to apply, and more.

dashboard controls
Applicant report

Innovating faster with intuitive UI, increasing customer satisfaction

QuickSight enables TalentReef to innovate faster in response to customer feedback. With the intuitive UI and native data lake connections of QuickSight, TalentReef’s product team is able to quickly build visualizations based off the needs and wants of all their customers.

TalentReef’s previous reporting tool required manual efforts from development teams. Enhancements and bug fixes required prioritization against other initiatives and had a higher likelihood of error. With QuickSight, TalentReef was able to set up a data lake that allows dashboards to be built and innovated on by the product team, freeing up development resources to continue on the highest priority. Developers get the data into the data lake, and then the product team pulls in the data into QuickSight and deploys it as needed. This has lead to higher customer satisfaction both internally and externally with the quick turnaround time.

The right people with the right information

In any type of HR space, the right level of data access is key to make sure you aren’t leaving yourself open to compliance issues. Our development team developed a solution that is able to be applied across all QuickSight dashboards using row-level security on the dataset.

TalentReef’s partnership with QuickSight has enabled us to unlock insights that were previously difficult or impossible to attain. We’ve allowed our customer base to know what is happening and why it is happening, and visualize data that is most impactful and important to them.

To learn more about how you can embed customized data visuals, interactive dashboards, and natural language querying into any application, visit Amazon QuickSight Embedded.

About the Authors

Alexander Plumb is a Product Manager at Mitratech. Alexander has been a product leader with over 5 years of experience leading to highly successful product launches that meet customer needs.

Bani Sharma is a Sr Solutions Architect with Amazon Web Services (AWS), based out of Denver, Colorado. As a Solutions Architect, she works with a large number of Small and Medium businesses, and provides technical guidance and solutions on AWS. She has an area of depth in Containers and Modernization. Prior to AWS, Bani worked in various technical roles for a large Telecom provider Dish Networks and worked as a Senior Developer for HSBC Bank Software development.

Brian Klein is a Sr Technical Account Manager with Amazon Web Services (AWS), helping digital native businesses utilize AWS services to bring value to their organizations. Brian has worked with AWS technologies for 9 years, designing and operating production internet-facing workloads, with a focus on security, availability, and resilience while demonstrating operational efficiency.