Tag Archives: Amazon QuickSight

AWS Week in Review – Automate DLQ Redrive for SQS, Lambda Supports Ruby 3.2, and More – June 12, 2023

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/aws-week-in-review-automate-dlq-redrive-for-sqs-lambda-supports-ruby-3-2-and-more-june-12-2023/

Today I’m boarding a plane for Madrid. I will attend the AWS Summit Madrid this Thursday, and I will take Serverlesspresso with me. Serverlesspresso is a demo that we take to events, in where you can learn how to build event-driven architectures with serverless. If you are visiting an AWS Summit, most probably you will find one of our booths.

Serverlesspresso at Madrid

Last Week’s Launches
Here are some launches that got my attention during the previous week.

Amazon SQS – Customers were very excited when we announced the DLQ redrive for Amazon SQS as that feature helped them to easily redirect the failed messages. This week we added support for AWS SDK and CLI for this feature, allowing you to redrive the messages on the DLQ automatically, making it even easier to use this feature. You can read Seb’s blog post about this new feature to learn how to get started.

AWS Lambda – AWS Lambda now supports Ruby 3.2. Ruby 3.2 has many new improvements, for example, passing anonymous arguments to functions or having endless methods. Check out this blog post that goes in depth into each of the new features.

Amazon Fraud Detector – Amazon Fraud Detector supports event orchestration with Amazon EventBridge. This is a very important feature because now you can act on the different events that Fraud Detector emits, for example, send notifications to different stakeholders.

AWS Glue – This week, AWS Glue made two important announcements. First, it announced the general availability of AWS Glue for Ray, a new data integration engine option for AWS Glue. Ray is a popular new open-source compute framework that helps developers to scale their Python workloads. In addition, AWS Glue announced AWS Glue Data Quality, a new capability that automatically measures and monitors data lake and data pipeline quality.

Amazon Elastic Container Registry (Amazon ECR) – AWS Signer and Amazon ECR announced a new feature that allows you to sign and verify container images. You can use Signer to validate that only container images you have approved are deployed in your Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

Amazon QuickSight – Amazon QuickSight now supports APIs to automate asset deployment, so you can replicate the same QuickSight assets in multiple Regions and account easily. You can read more on how to use those APIs in this blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

  • AWS Silicon Innovation Day (June 21) – A one-day virtual event that focuses on AWS Silicon and how you can take advantage of AWS’s unique offerings. Learn more and register here.
  • AWS Global Summits – There are many summits going on right now around the world: Toronto (June 14), Madrid (June 15), and Milano (June 22).
  • AWS Community Day – Join a community-led conference run by AWS user group leaders in your region: Chicago (June 15), Manila (June 29–30), Chile (July 1), and Munich (September 14).
  • CDK Day – CDK Day is happening again this year on September 29. The call for papers for this event is open, and this year we are also accepting talks in Spanish. Submit your talk here.

That’s all for this week. Check back next Monday for another Week in Review!

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

— Marcia

Improve table readability and identify outliers with data bars in Amazon QuickSight

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/improve-table-readability-and-identify-outliers-with-data-bars-in-amazon-quicksight/

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

Recently, we launched some new features for tables and pivot tables in QuickSight centered around interactivity and performance. These new features enabled users to alter field visibility, load tables faster, and build consistency across different interactions. In the continuous streak of providing rich user experiences and readability, QuickSight is now introducing data bars for table visual.

In this post, we demonstrate how to use data bars to improve table readability and identify outliers.

Introduction to data bars

Tables are a popular way of organizing and presenting data, but it could be difficult for reading and understanding data, especially in large datasets. One way to make table presentation effective is to provide a visual representation with data bars.

Data bars are essentially bar charts displayed for a given column, where the length of the bar represents each cell value relative to the range of values within the same column. Data bars are very efficient in enabling user focus on outliers and emerging data patterns or trends, especially when dealing with large volumes of data. Data bars improve the readability and navigation of complex tables by integrating tabular data with visualizations. Their visual nature enables quick comprehension and understanding, making them a popular choice for displaying and analyzing data. With QuickSight, you can now use data bars on numeric fields and adjust your color scheme for both positive and negative values individually.

Solution overview

Our use case focuses on AnyHealth Inc., a large hospital corporation in the US. They manage different hospitals across different regions of the country. As part of their analytics requirements, they want to be able to quickly find outliers and determine health economics outcomes. They use QuickSight for their visualizations. With the recent addition of data bars to the available table visuals, AnyHealth can get these insights with ease. Not only that, they can also get the information by reading through the cells. With data bars, they are instantly able to identify the outliers visually, identify values that significantly deviate from rest of the data, and monitor emerging trends. With data bars, understanding and reading the tables has been a breeze.

In the following sections, we examine two use cases using data bars in QuickSight.

Identify outliers with data bars visually

To add a table visual to the analysis with data bars, we create a table visual with at least one metric in the Values field well. In this example, we create a table to load profits across various hospitals and categories. The following screenshot shows our initial data.

Complete the following steps to configure a visualization:

  1. On the table visual, choose the pencil icon to open the Format visual navigation pane.
  2. In the navigation pane, expand the Visuals drop-down menu and choose ADD DATA BARS.
  3. For Value field, choose Profit. By default, data bars are configured for two colors: green for positive values and red for negative values.

Note: Data bars are applicable only on the Values field of the visual.

  1. To further configure these colors, choose the paint bucket icon and choose your preferred color.
  2. Close the Data bars menu.

The data bars visualization now appears in the table and an instant outlier can be identified at South Hospital in Ante/Post Partum category.

Display various metrics on the same scale

AnyHealth often has several metrics that they want to visualize and compare side by side, sliced by a single dimension on a same metric scale. For this use case, they want to visualize revenue, profit, and price sliced by the Hospital dimension. Having all these metrics on the same scale is challenging because the numbers vary greatly. With data bars, AnyHealth was able to achieve this in a very simple and clean way, which enabled them to show their data without additional calculations.

The following screenshot shows the example implementation.

Conclusion

In this post, we looked at the data bars feature in QuickSight, its various use cases, and how to configure them. With data bars, you can analyze and quickly scan a table to see the values of a cell. Furthermore, you can use data bars to identify outliers visually that deviate from the rest of the data. Data bars can be very powerful when it comes to understanding and reading data in tables. Start using data bars to enrich your dashboards’ current visualization and unlock new business use cases today!

If you have any questions or feedback, please leave a comment.

For additional discussions and help getting answers to your questions, check out the QuickSight Community.

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.

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.

Srikanth Baheti is a Specialized World Wide Principal Solution 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.

Joulica unifies real-time and historical customer experience analytics with Amazon QuickSight

Post Syndicated from Tony McCormack original https://aws.amazon.com/blogs/big-data/joulica-unifies-real-time-and-historical-customer-experience-analytics-with-amazon-quicksight/

This is a guest post by Tony McCormack from Joulica.

Joulica is an Ireland-based startup in the contact center industry. Our founders previously led contact center research and development for a global contact center technology provider, and we founded Joulica because we saw that the shift to the cloud and growing demand for data and analytics would transform the customer service industry. Our platform delivers real-time and historical analytics across the wide variety of data sources and customer interaction channels that are now common in most organizations.

It is our mission to make customer experience analytics accessible to all users in an organization. Our customers need to have instant access to the current state of ongoing customer engagements as well trends and historical data patterns. They find it difficult to harness all of the data sources needed to achieve this, particularly in real time. To help solve this problem, we chose Amazon QuickSight, a cloud-native business intelligence tool that allows you to embed insightful analytics into any application with customized, interactive visuals and dashboards.

In this post, we discuss how QuickSight has allowed us to achieve our goal of unifying real-time and historical analytics for our customers.

Embedding QuickSight to provide a 360-degree view

At Joulica, our specialty is real-time analytics. Real-time analytics means that as soon as new data is available, analytics are updated, including any visualizations that are currently being viewed by end-users. The rich embedding capability of QuickSight allowed us to use QuickSight for historical analytics and our technology for real-time analytics, all in one place. Visual embedding with QuickSight has saved us greater than two resource years of research and development time, enabling us to serve customers sooner. We have also been able integrate Joulica real-time analytics into QuickSight dashboards—so within QuickSight, our users can use both standard QuickSight visualizations and Joulica widgets, as shown in the following example.

Our customer journey analytics go beyond typical contact center reporting, and stitches together how customers interact with organizations across channels. By pushing our analytics to QuickSight, we allow users to dive deep into patterns as well as individual customer journeys.

Integration with Amazon Connect and other AWS services

One of the main reasons we chose QuickSight was because of its alignment with Amazon Connect and the extended set of Contact Center services that AWS provides. As the leading provider of analytics for Amazon Connect, this was essential. We are deeply integrated with the full capabilities of Amazon Connect, including Amazon Lex and Contact Lens. In the preceding example of a QuickSight dashboard, we see that as soon as a KPI is updated, the visualization automatically updates. Behind the scenes, Joulica is ingesting Agent Event Streams, Contact Events, and a host of real-time data feeds from Amazon Connect, analyzing them and pushing a stream of real-time analytics to these widgets. This enables our customers to have up-to-date information, in addition to the historical context, empowering them to improve contact center performance and make better decisions. This is all visualized within QuickSight.

We can also ingest data from Amazon Kinesis and Apache Kafka, allowing live analytics from additional data sources to be visualized.

Empowering our customers with insights in QuickSight

The key goals driving the need for analytics in the contact center industry are customer experience optimization as well as efficiency gains. This is relevant across all customer touchpoints, including digital channels, traditional contact center channels, and emerging areas such as social and smart devices. In addition to KPIs related to service levels and agent performance, organizations need access to customer feedback and sentiment analytics, as well as an explanation of how these are related to business performance such as sales and customer retention. With QuickSight, we can offer out-of-the box analytics for all of these areas. The following example shows how easy it is to use the Quicksight visual embedding feature to extend one of our real-time visualizations to show a geographic breakdown of customer survey results.

Thanks to the QuickSight and AWS pay-as-you-go pricing model, we can provide real-time and historical embedded analytics to our customers automatically. We have saved significantly by choosing QuickSight, both in cost and development time, and the cost savings provided by Quicksight is passed down to our customers. Like AWS, our pricing is usage-based, meaning that our customers can scale as their operations grow.

We currently have customers in North America, United Kingdom, the EU, and APAC. As we continue to grow, we plan to explore other QuickSight features like Amazon QuickSight Q, as we pursue providing more ways for our customers to analyze, visualize, and interpret their contact center data.

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

About the Author

Tony McCormack is the CEO and Co-founder of Joulica. Based in Galway, Ireland, he is focused on providing enterprise-grade reporting and analytics for Amazon Connect, Salesforce Service Cloud, and other platforms in the customer experience market. He has extensive experience in the contact center domain, with a passion for real-time analytics and their integration into end-user applications.

Automate and accelerate your Amazon QuickSight asset deployments using the new APIs

Post Syndicated from Vetri Natarajan original https://aws.amazon.com/blogs/big-data/automate-and-accelerate-your-amazon-quicksight-asset-deployments-using-the-new-apis/

Business intelligence (BI) and IT operations (BIOps) teams often need to automate and accelerate the deployment of BI assets to ensure business continuity. We heard that you wanted an automated and scalable way to deploy, back up, and replicate Amazon QuickSight assets at scale so that BIOps teams within your organization can work in an agile manner.

Today, we are releasing six new QuickSight APIs to allow programmatic access to export and import QuickSight assets—dashboards, analyses, datasets including ingestion schedules, data sources, themes, and VPC configurations—across accounts and environments. These new APIs allow you to interact with a collection of assets in a lift-and-shift manner for deployment across QuickSight accounts, enable backup and restore, and support replication so you can automate workflows and achieve your desired infrastructure setup. These new capabilities bring greater agility to your BIOps teams, allowing you to automate and seamlessly integrate QuickSight assets into existing infrastructure.

Prior to this launch, you needed to have an in-depth understanding of QuickSight asset relationships and couldn’t deploy, back up, or replicate at scale in an automated manner. In this post, we cover the capabilities of the new APIs in detail and go over common use cases.

Export APIs

You can use the following APIs to initiate, track, and describe the export jobs that produce the bundle files from the source account. A bundle file is a zip file (with the .qs extension) that contains assets specified by the caller, and optionally all dependencies of the assets. The APIs are as follows:

  • StartAssetBundleExportJob – Use this asynchronous API to export an asset bundle file.
  • DescribeAssetBundleExportJob – Use this synchronous API to get the status of your export job. When successful, this API call response will have a presigned URL to fetch the asset bundle.
  • ListAssetBundleExportJobs – Use this synchronous API to list past export jobs. The list will contain both finished and running jobs from the past 15 days.

Import APIs

These APIs initiate, track, and describe the import jobs that take the bundle file as input and create or update assets in the destination account:

Supported assets

You can export and import the following assets with these APIs:

  • Analyses
  • Dashboards
  • Data sources
  • Datasets including scheduled and incremental refreshes
  • Themes
  • VPC connections

Asset bundle output format

The output of the export job is a single zip file with the .qs extension. This zip file contains a separate folder for each asset type. Each folder contains a single JSON file for each asset with the resourceId as the file name. This folder structure makes it easy to commit the contents into a version control system like Git, so you can get the benefits of a complete version history.

The Asset-bundle API can also export QuickSight assets as AWS CloudFormation templates, one of the most popular infrastructure as code (IaC) frameworks. It makes it easy to manage your QuickSight assets at scale and automate your deployments. AWS CloudFormation also has built-in transaction and rollback capabilities, ensuring that all your environments are consistent and your assets are deployed correctly every time. Finally, you can use the CloudFormation templates to recreate your QuickSight resources in case of a disaster.

Permissions required

These APIs are available to users with AWS Identity and Access Management (IAM) permissions to run these APIs. The following IAM policy allows an IAM user to get access to these APIs:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [          
                "quicksight:StartAssetBundleImportJob",
                "quicksight:DescribeAssetBundleImportJob",
                "quicksight:ListAssetBundleImportJobs",
                "quicksight:StartAssetBundleExportJob",
                "quicksight:DescribeAssetBundleExportJob",
                "quicksight:ListAssetBundleExportJobs"
            ],
            "Resource": "*"
        }
    ]
}

Use case overview

Let’s consider a fictional company, AnyCompany, which owns healthcare facilities across the globe. They have set up a development QuickSight account for authors to create and update QuickSight assets and a separate production account. In some cases due, to data residency regulation, they have to maintain the same assets across multiple Regions. AnyCompany is scaling their business and they want to automate deployment within and across multiple QuickSight accounts and back up QuickSight assets on a schedule.

AnyCompany has the following key deployment and backup requirements:

  • Deployment – Deploy QuickSight assets across Regions and multiple accounts:
    • Deployment to the production account – AnyCompany wants to automate the deployment of QuickSight assets from their development to their production account.
    • Deployment to different Regions in the same account – AnyCompany’s central IT team needs to deploy dashboards and datasets across various Regions to meet data residency requirements.
    • Deployment to multiple accounts in different Regions – To meet their end customer requirements of separate QuickSight accounts, AnyCompany needs to deploy the dashboards and datasets across multiple accounts.
    • Deployment in the same account and same Region – AnyCompany consolidates all non-production environment into one QuickSight account. However, there has to be different dashboards and datasets for each non-production environment, such as development and testing.
  • Backup and restore – As AnyCompany rolls out critical dashboards for business, it needs to ensure high availability of the dashboards. As part of their strategy, AnyCompany wants to maintain a backup of assets to restore in case of disasters.
  • Deployment history – As part of the governed process, AnyCompany’s central IT team needs to have a history of deployments in each environment.

In the following sections, we discuss how to meet these requirements.

Deploy to a production account

The following figure shows the sequence of steps in the deployment process through the new asset deployment APIs.

For deployments, the import job API provides the capability to pass data source configurations to point to the respective test or production instances of data sources. In the preceding sequence flow, we use the AWS Command Line Interface (AWS CLI) to showcase the capability, but you can invoke the APIs through your automation pipeline using AWS SDKs.

For this use case, AnyCompany used Amazon Simple Storage Service (Amazon S3) to store their asset bundles..

On the development QuickSight account, complete the following steps:

  1. Use the StartAssetBundleExportJob API to export the dashboard and its dependencies.
  2. Place the output asset bundle in an S3 bucket in the production account.

On the production QuickSight account, complete the following steps:

  1. Use the StartAssetBundleImportJob API with the asset bundle in Amazon S3 as the source, overriding the data source details.
  2. Run the import job.

The following code shows their StartAssetBundleExportJob API call to export the dashboard and its dependencies:

aws quicksight start-asset-bundle-export-job 
--aws-account-id $AAI 
--asset-bundle-export-job-id job-1 
--resource-arns arn:aws:quicksight:$IR:$AAI:dashboard/<<dashboard-id>> 
--include-all-dependencies 
--export-format QUICKSIGHT_JSON

The following code is for the DescribeAssetBundleExportJob API:

aws quicksight describe-asset-bundle-export-job 
--aws-account-id $AAI 
--asset-bundle-export-job-id job-1

The output of the DescribeAssetBundleExportJob API call contains the presigned URL, which you use to download your respective assets and subsequently upload them to a dedicated S3 bucket in the target account.

The import job (StartAssetBundleImportJob) is initiated in the target account using S3Uri as one of the input parameters. You can also change the data source configuration while initiating the job. In the following example, the S3 manifest file location for the S3 data source is overridden:

aws quicksight start-asset-bundle-import-job
 --aws-account-id $AAI 
--asset-bundle-import-job-id job-1 
--asset-bundle-import-source "{\"S3Uri\": \"<<qsfile location\"}"
 --override-parameters '{"DataSources": 
 [{"DataSourceId": "<<DataSourceID>>", 
 "DataSourceParameters": 
 { "S3Parameters": {"ManifestFileLocation": 
 {"Bucket": "<<bucket name>>", "Key": "<<key for manifest file>>"}}}}]}' 
--region us-west-2

DescribeAssetBundleImportJob lets you to monitor the status of the import job:

aws quicksight describe-asset-bundle-import-job 
--aws-account-id $AAI 
--asset-bundle-import-job-id job-1 
--region us-west-2

The following screenshot shows the response.

Deploy to different Regions in the different accounts

To comply with data residency regulations, data can’t be moved outside a Region in certain countries. Therefore, the dashboards have to be deployed in each of these Regions. QuickSight provides the option to pass an asset bundle extracted from the source environment as a base64 encoded string for the import job (StartAssetBundleImportJob):

aws quicksight start-asset-bundle-import-job \
 --aws-account-id $AAI  \
--asset-bundle-import-job-id <<job-id>> \ 
--asset-bundle-import-source Body="$(base64 assetbundle-extract.qs)"

Deploy within a single account in the same Region

When using a centralized account approach for all the lower environments, AnyCompany wanted to have the same dashboards within a single Region to be able to connect with different data sources. To achieve this, they used the optional parameter resource-id-prefix in the import job (StartAssetBundleImportJob) to create a unique ID for each environment:

aws quicksight start-asset-bundle-import-job
--aws-account-id $AAI 
--asset-bundle-import-job-id job-1 
--asset-bundle-import-source "{\"S3Uri\": \"s3://qs file s3 location\"}"
--override-parameters '{"DataSources": 
[{"DataSourceId": "<<DataSource ID>>", 
"DataSourceParameters":{ "S3Parameters": {"ManifestFileLocation": 
{"Bucket": "ee-assets-prod-us-west-2", 
"Key": "modules/337d5d05acc64a6fa37bcba6b921071c/v1/SalesDataManifest.json"}}}}]}' 
--region us-west-2
--resource-id-prefix "test-"

Backup and version control

AnyCompany deploys business-critical dashboards, and it’s important for them to have proper backup and version control processes. They run scheduled export jobs at regular intervals along with asset deployments. Additionally, they use asset bundle APIs to meet their version control requirements.

The following screenshot shows the content of a sample bundle.

Deployment history

AnyCompany needs to track the deployment history of all the assets in all environments. They achieved this goal by using the ListAssetBundleExportJobs and ListAssetBundleImportJobs APIs to fetch the deployment history in a given account.

The following code is for ListAssetBundleExportJobs:

aws quicksight list-asset-bundle-export-jobs \
 --aws-account-id $AAI  \

The following code is for ListAssetBundleImportJobs:

aws quicksight list-asset-bundle-export-jobs \
 --aws-account-id $AAI  \

Conclusion

Asset bundle APIs provide methods for automation and acceleration in the deployment process across multiple environments. This post illustrated various use cases where you can apply these APIs for automation and scale. For more information, refer to Amazon QuickSight and What’s New in the Amazon QuickSight User Guide.

If you have any questions or feedback, please leave a comment. For additional discussions and help getting answers to your questions, check out the QuickSight Community.

About the authors

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.

Zhao Pan is a software development manager for Amazon QuickSight. He is working to provide a delightful developer experience to our customers to automate and streamline their BI operations. He has 20 years of software development experience in various tech stacks. Prior to QuickSight he was a people and technical leader at ADP building a next-gen platform for human capital management. When he is not at his desk, he can usually be found in his garage building one contraption or another.

Mayank Agarwal is a product manager for Amazon QuickSight, AWS’ cloud-native, fully managed BI service. He focuses on embedded analytics and developer experience. He started his career as an embedded software engineer developing handheld devices. Prior to QuickSight he was leading engineering teams at Credence ID, developing custom mobile embedded device and web solutions using AWS services that make biometric enrollment and identification fast, intuitive, and cost-effective for Government sector, healthcare and transaction security applications.

Enable complex row-level security in embedded dashboards for non-provisioned users in Amazon QuickSight with OR-based tags

Post Syndicated from Srikanth Baheti original https://aws.amazon.com/blogs/big-data/enable-complex-row-level-security-in-embedded-dashboards-for-non-provisioned-users-in-amazon-quicksight-with-or-based-tags/

Amazon QuickSight is a fully managed, cloud-native business intelligence (BI) service that makes it easy to connect to your data, create interactive dashboards, and share these with tens of thousands of users, both within QuickSight and embedded in your software as a service (SaaS) applications.

QuickSight Enterprise edition started supporting nested conditions within row-level security (RLS) tags where you can combine AND and OR conditions to simplify multi-tenant access patterns. Previously, QuickSight only supported the AND operator for all tags. When users are assigned multiple roles, which enables them to view data in multiple dimensions, you need both AND and OR operators to express RLS rules. QuickSight enables authors and developers to use the OR operator in the form of OR of AND, which allows you to satisfy even the most complex data security scenarios. In this post, we look at how this can be implemented.

Feature overview

When you embed QuickSight dashboards in your application for users who aren’t provisioned (registered) in QuickSight, this is called anonymous embedding. In this scenario, even though the user is anonymous to QuickSight, you can still customize the data that user sees in the dashboard using RLS tags.

You can do this in three simple steps:

  1. Add RLS tags to a dataset.
  2. Add the OR condition to RLS tags.
  3. Assign values to those tags at runtime using the GenerateEmbedUrlForAnonymousUser API operation. For more information, see Embedding QuickSight data dashboards for anonymous (unregistered) users.

To see this feature in action, see Using tag-based rules.

Use case overview

AnyHealth Inc. is a fictitious independent software vendor (ISV) in the healthcare space. They have a SaaS application for different hospitals across different regions of the country to manage their revenue. AnyHealth Inc has thousands of healthcare employees accessing their application portal. Part of their application portal has embedded operational insights related to their business within a QuickSight dashboard. AnyHealth doesn’t want to manage their users in QuickSight separately, and wants to secure data based on who the user is and the hospital the user is affiliated to. AnyHealth decided to authorize data access to their users at runtime, enabling row-level security using tags.

AnyHealth has hospitals (North Hospital, South Hospital, and Downtown Hospital) in regions Central, East, South, and West.

In this example, the following users access AnyHealth’s application with the embedded dashboard. Each user has a certain level of data restriction that define what they can access in the dashboards. PowerUser is a super user that can see the data for all hospitals and regions.

AnyHealth’s

Application Users

 Hospital Region Condition Payor State
NorthMedicaidUser North Hospital Central and East OR Medicaid New York
SouthMedicareUser South Hospital South OR Medicare All states
NorthAdmin North Hospital All regions
SouthAdmin South Hospital All regions
PowerUser All hospitals All regions

These users are only application-level users and haven’t been provisioned in QuickSight. AnyHealth wants to continue with user management and their roles at the application level as a single source of truth. This way, when the user accesses the embedded QuickSight dashboard from the application, AnyHealth must secure the data on the dashboard based on the roles and permissions that user has. AnyHealth has different combinations of user permissions; for example, all AnyHealth administrators have access to all the data that can be achieved by PowerUser permissions. A hospital admin, for example NorthAdmin, is a user who is the administrator at North Hospital and can only view all the data related to that hospital. A hospital user, for example SouthUser, is a user who has access to data at South Hospital in a specific region.

Additionally, when there are Medicaid and Medicare claims, there are special users who monitor these programs. For example, there can be a user at North Hospital who has access to all the data in North Hospital in regions Central and East. But this user also manages Medicaid for New York. In this case, to show all the relevant data, RLS rules have to be defined such that the user can see data where (Hospital = North Hospital and Region in (Central, East)) or (payor = Medicaid and State = New York). This can be achieved with the new RLS with OR tags feature in QuickSight.

Solution overview

Setup involves two steps:

  1. Create tag keys.
  2. Set SessionTags for each user.

Create tag keys

AnyHealth creates tag keys on the dataset they’re using to power the dashboard. This can be done in two ways, either through an UpdateDataset API call or through the QuickSight console.

Configuration using the API

In the UpdateDataset API call, the RowLevelPermissionTagConfiguration element is set as follows. Note that the items within an item in TagRuleConfigurations will always run a logical AND when the rules are passed, and if there is more than one item in the list, then the items are run with a logical OR. We use the following sample configuration to address our use case:

"RowLevelPermissionTagConfiguration": {
            "Status": "ENABLED",
            "TagRules": [
                {
                    "TagKey": "region",
                    "ColumnName": "Region",
                    "TagMultiValueDelimiter": ",",
                    "MatchAllValue": "*"
                },
                {
                    "TagKey": "hospital",
                    "ColumnName": "Hospital",
                    "TagMultiValueDelimiter": ",",
                    "MatchAllValue": "*"
                },
                {
                    "TagKey": "payor",
                    "ColumnName": "Payor Segment",
                    "TagMultiValueDelimiter": "*",
                    "MatchAllValue": ","
                },
                {
                    "TagKey": "state",
                    "ColumnName": "State",
                    "TagMultiValueDelimiter": ",",
                    "MatchAllValue": "*"
                }
            ],
            "TagRuleConfigurations": [
                [
                    "region",
                    "hospital"
                ],
                [
                    "payor",
                    "state"
                ]
            ]
        }

Configuration using the QuickSight console

To use the QuickSight console, complete the following steps:

  1. On the QuickSight console, choose Datasets in the navigation pane.
  2. Choose the dataset from the list to apply tag-based RLS tags (for this post, we use the patientinfo dataset).
  3. Choose Edit under Row-level security.
  4. On the Set up row-level security page, expand Tag-based rules.
  5. To begin adding rules, choose columns on the Column drop-down menu under Manage tags.
  6. Create rules as per the permissions table.

To grant access to QuickSight provisioned users, you still need to configure user-based rules.

  1. Repeat these steps to add the required tags.
  2. After all the tags are added, choose Add OR Condition under Manage rules.
  3. Choose your tags for the OR condition and choose Update.

Note that you need to explicitly update the first condition that automatically created AND for all fields added.

  1. Once the rules are created, choose Apply.

Set SessionTags

At runtime, when embedding the dashboards via the GenerateDahboardEmbedURLForAnonymousUser API, set SessionTags for each user.

SessionTags for NorthAdmin are as follows:

{
    "SessionTags": [
        {
            "Key": "hospital",
            "Value": "North Hospital"
        },
        {
            "Key": "region",
            "Value": "*"
        }
    ]
}

SessionTags for SouthAdmin are as follows:

{
    "SessionTags": [
        {
            "Key": "hospital",
            "Value": "South Hospital"
        },
        {
            "Key": "region",
            "Value": "*"
        }
    ]
}

SessionTags for PowerUser are as follows:

{
    "SessionTags": [
        {
            "Key": "hospital",
            "Value": "*"
        },
        {
            "Key": "region",
            "Value": "*"
        }
    ]
}

SessionTags for NorthMedicaidUser are as follows:

{
    "SessionTags": [
        {
            "Key": "hospital",
            "Value": "North Hospital"
        },
        {
            "Key": "region",
            "Value": "East"
        }, 
        {
            "Key": "payor",
            "Value": "Medicaid"
        },
        {
            "Key": "state",
            "Value": "New York"
        }
    ]
}

SessionTags for SouthMedicareUser are as follows:

{
    "SessionTags": [
        {
            "Key": "hospital",
            "Value": "South Hospital"
        },
        {
            "Key": "region",
            "Value": "South"
        }, 
        {
            "Key": "payor",
            "Value": "Medicare"
        },
        {
            "Key": "state",
            "Value": "*"
        }
    ]
}

The following screenshot shows what NorthMedicaidUser sees pertaining to all North hospitals in the East region and Medicaid in New York state.

The following screenshot shows what SouthMedicaidUser sees pertaining to all South hospitals in the South region or Medicare in all states.

Based on session tags with OR of AND’s support, AnyHealth has secured data on the embedded dashboards such that each user only sees specific data based on their access. You can access the dashboard as one of the users (by changing the user on the drop-down menu on the top right) and see how the data changes based on the user selected.

Overall, with row-level security using OR of AND, AnyHealth is able to provide a compelling analytics experience within their SaaS application, while making sure that each user only sees the appropriate data without having to provision and manage users in QuickSight. QuickSight provides a highly scalable, secure analytics option that you can set up and roll out to production in days, instead of weeks or months previously.

Conclusion

The combination of embedding dashboards for users not provisioned in QuickSight and row-level security using tags with OR of AND enables developers and ISVs to quickly set up sophisticated, customized analytics for their application users—all without any infrastructure setup or user management, while scaling to millions of users. For more updates from QuickSight embedded analytics, see What’s New in the Amazon QuickSight User Guide.

If you have any questions or feedback, please leave a comment. For additional discussions and help getting answers to your questions, check out the QuickSight Community.

About the Authors

Srikanth Baheti is a Specialized World Wide Principal Solution 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.

Mayank Agarwal is a product manager for Amazon QuickSight, AWS’ cloud-native, fully managed BI service. He focuses on embedded analytics and developer experience. He started his career as an embedded software engineer developing handheld devices. Prior to QuickSight he was leading engineering teams at Credence ID, developing custom mobile embedded device and web solutions using AWS services that make biometric enrollment and identification fast, intuitive, and cost-effective for Government sector, healthcare and transaction security applications.

BWH Hotels scales enterprise business intelligence adoption while reducing costs with Amazon QuickSight

Post Syndicated from Joseph W. Landucci original https://aws.amazon.com/blogs/big-data/bwh-hotels-scales-enterprise-business-intelligence-adoption-while-reducing-costs-with-amazon-quicksight/

This is a guest post by Joseph Landucci from BWH Hotels. In their own words, “BWH Hotels is a leading, global hospitality enterprise comprised of three hotel companies, including WorldHotels, Best Western Hotels & Resorts and SureStay Hotels. Our mission is to deliver trusted guest experiences, drive hotel success and foster a caring, inclusive culture that respects the environment. Technology plays a key role in meeting these standards.

My team supports analytics throughout BWH Hotels, including some affiliate and international organizations. Four years ago, we switched to Amazon QuickSight, a fully managed, cloud-native business intelligence (BI) service, and have not looked back. In March 2021, I wrote about how QuickSight had helped us cut costs by over 70%, while also improving our operations. Since then, we have moved fully onto Amazon Web Services (AWS) from IBM Cognos and Oracle, and improved our BI adoption while navigating the effects of the COVID-19 pandemic.

In this post, I discuss how we are using QuickSight today, how it helped us through the pandemic and how we are using it to build the future of our business.

Migrating to AWS and QuickSight

After a six-month proof of concept, we decided to move fully to AWS for our data and analytics needs. Our previous BI solution, IBM Cognos, was prohibitively expensive because we had to pay for a license for each user. After we made the decision to make QuickSight our BI solution, we were able to move our hotels from Cognos to QuickSight within 10 months. Our affiliate users made the migration four months later, followed by our corporate users.

Having used Cognos since 2008, this was not just a lift-and-shift migration. We determined what dashboards and data we needed to migrate from our previous solution to QuickSight. During the migration, we discovered that a significant portion of what was in Cognos wasn’t being used. With AWS Database Migration Service (AWS DMS), we were able to put our data into Amazon Redshift and then build dashboards with QuickSight.

Navigating the pandemic with consumption-based pricing

Shortly after we switched to AWS and QuickSight, the COVID-19 pandemic hit, which impacted the hospitality industry considerably. If we hadn’t made this migration, we would have been stuck with fixed licensing costs. Thanks to the AWS pay-as-you-go pricing model, we were able to only pay for what we needed when demand was lower in 2020. Then when travel increased again, we were able to seamlessly scale back up with demand.

This consumption-based pricing model was one of the reasons we chose QuickSight, which turned out to be a game changer during an unanticipated event. We didn’t have to do any of the scaling down and then jump back up ourselves.

Increased usage and functionality

Even as we have reduced costs, our enterprise BI adoption and usage has gone up almost tenfold. Our corporate users and hoteliers find QuickSight much more intuitive than Cognos. We have 24,000 registered users and 8,000 monthly active users within BWH Hotels.

With greater usage comes greater responsibility for governance and training. When we began the move to AWS, we had a weekly governance meeting, which we have now moved to biweekly. In this governance meeting, we determined how we were going to standardize data handling and set rules. These meetings also gave us the opportunity to provide training on the new tools we were using, like QuickSight. Finally, we also had learning sessions which team members were able to access virtually. Now, whenever a new team begins to use QuickSight, we have 3–4 years’ worth of training material to help them get up to speed. We have also found the training resources provided by QuickSight to be valuable.

Improving our business with QuickSight

The BWH Hotels Revenue Management team was an early adopter of QuickSight. We have around 20–30 specific revenue management dashboards used both by corporate and hoteliers worldwide. Our sales team has implemented QuickSight to perform recency, frequency and monetary (RFM) analysis to determine which customers it should prioritize. With these dashboards, our sales teams at both the corporate and property levels are able to score customer and corporate accounts so that they can optimize how they use their time. We have also begun to experiment with sentiment analysis based on call center data that is fed into Amazon Transcribe. Gathering a multitude of data and then visualizing it in QuickSight has made us a much nimbler organization.

We are also excited about the value we are able to provide to our hoteliers with QuickSight Paginated Reports for creating billing invoices. With this new feature, our hotel operators no longer need to go onto the credit card processing portal to access transactions. They can access the information they need right within QuickSight.

Building for the future

In the near future, we plan to embed QuickSight into our property management system (PMS), AutoClerk. This will provide BWH Hotels and our hoteliers with analytics within their PMS, saving them time and giving them easy access to the information they need to improve their businesses. By including analytics, AutoClerk will provide incredible value to customers.

By switching to QuickSight, BWH Hotels has provided more value to our hoteliers – increasing adoption, saving money and building for the future.

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

About the Author
Joseph W. Landucci serves as Director of Technology Management at BWH Hotels where he has spent over eight years building and evolving their data management strategy. In this role he is responsible for the management and strategic direction of six product teams: Database, Revenue Management Systems, Data Services, Loyalty Applications, Loyalty Automation and Enterprise Analytics.

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.

Summary

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.

Conclusion

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

Prerequisites

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.

Conclusion

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.

    try:
        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"]
   else:
       access_token = get_secret_value_response["SecretBinary"]

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

SECRET_KEY = get_secret()
AUTH_SIZE = 16 

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

def lambda_handler(event, context):
ret = dict()
 try:
  res = []
  for argument in event['arguments']:
   
   try:
     msg = json.dumps(argument)
     signed_key = sign(str.encode(msg))
     res.append(signed_key.decode('utf-8'))
     
   except:
   res.append(None)     
   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)
RETURNS varchar STABLE
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:
concat("https://xxxxx.execute-api.us-east-1.amazonaws.com/stage_name/apiresourcename/?cust=",customername,"&bgc=",bgcheckrequired,"&billrate=",toString(billrate),"&skilledtrades=",skilledtrades,"&shift=",shift,"&jobdutydescription=",jobdutydescription,"&hash=",hashvalue)
  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.

Conclusion

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.

Conclusion

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.

Prerequisites

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.

Troubleshooting

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.

Conclusion

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.

Conclusion

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:

{
 "Version":"1.0.0",
 "Namespace":"default",
 "AwsAccountId":"<AwsAccountId>",
 "AwsRegion":"<AwsRegion>",
 "Permission":{
  "GroupList":[
   {"GroupName":"QUICKSIGHT_DATA_EDITOR"},
   {"GroupName":"QUICKSIGHT_DATA_VIEWER"},
   {"GroupName":"QUICKSIGHT_DATA_DESIGNER"},
   {"GroupName":"QUICKSIGHT_SAC_VIEWER"},
   {"GroupName":"QUICKSIGHT_SAC_DESIGNER"},
    ...
  ],
  "UserList":[
   {"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

Conclusion

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.

Forecasting

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.

Conclusion

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.