Tag Archives: Amazon QuickSight

Customize Amazon QuickSight dashboards with the new bookmarks functionality

Post Syndicated from Mei Qu original https://aws.amazon.com/blogs/big-data/customize-amazon-quicksight-dashboards-with-the-new-bookmarks-functionality/

Amazon QuickSight users now can add bookmarks in dashboards to save customized dashboard preferences into a list of bookmarks for easy one-click access to specific views of the dashboard without having to manually make multiple filter and parameter changes every time. Combined with the “Share this view” functionality, you can also now share your bookmark views with other readers for easy collaboration and discussion.

In this post, we introduce the bookmark functionality and its capabilities, and demonstrate typical use cases. We also discuss several scenarios extending the usage of bookmarks for sharing and presentation.

Create a bookmark

Open the published dashboard that you want to view and make changes to the filters, controls, or select the sheet that you want. For example, you can filter to the Region that interests you, or you can select a specific date range using a sheet control on the dashboard.

To create a bookmark of this view, choose the bookmark icon, then choose Add bookmark.

Enter a name, then choose Save.

The bookmark is saved, and the dashboard name on the banner updates with the bookmark name.

You can return to the original dashboard view that the author published at any time by going back to the bookmark icon and choosing Original dashboard.

Rename or delete a bookmark

To rename or delete a bookmark, on the bookmark pane, choose the context menu (three vertical dots) and choose Rename or Delete.

Although you can make any of these edits to bookmarks you have created, the Original dashboard view can’t be renamed, deleted, or updated, because it’s what the author has published.

Update a bookmark

At any time, you can change a bookmark dashboard view and update the bookmark to always reflect those changes.

After you make your edits, on the banner, you can see Modified next to the bookmark you’re currently editing.

To save your updates, on the bookmarks pane, choose the context menu and choose Update.

Make a bookmark the default view

After you create a bookmark, you can set it as the default view of the dashboard that you see when you open the dashboard in a new session. This doesn’t affect anyone else’s view of the dashboard. You can also set the Original dashboard that the author published as the default view. When a default view is set, any time that you open the dashboard, the bookmark view is presented to you, regardless of the changes you made during your last session. However, if no default bookmark has been set, QuickSight will persist your current filter selections when you leave the dashboard. This way, readers can still pick up where they left off and don’t have to re-select filters.

To do this, in the Bookmarks pane, choose the context menu (the three dots) for the bookmark that you want to set as your default view, then choose Set as default.

Share a bookmark

After you create a bookmark, you can share a URL link for the view with others who have permission to view the dashboard. They can then save that view as their own bookmark. The shared view is a snapshot of your bookmark, meaning that if you make any updates to your bookmark after generating the URL link, the shared view doesn’t change.

To do this, choose the bookmark that you want to share so that the dashboard updates to that view. Choose the share icon, then choose Share this view. You can then copy the generated URL to share it with others.

Presentation with bookmarks

One extended use case of the new bookmarks feature is the ability to create a presentation through visuals in a much more elegant fashion. Previously, you may have had to change multiple filters and controls before arriving at your next presentation. Now you can save each presentation as a bookmark beforehand and just go through each bookmark like a slideshow. By creating a snapshot for each of the configurations you want to show, you create a smoother and cleaner experience for your audience.

For example, let’s prepare a presentation through bookmarks using a Restaurant Operations dashboard. We can start with the global view of all the states and their related KPIs.

Best vs. worst performing restaurant

We’re interested in analyzing the difference between the best performing and worst performing store based on revenue. If we had to filter this dashboard on the fly, it would be much more time-consuming because we would have to manually enter both store names into the filter. However, with bookmarks, we can pre-filter to the two addresses, save the bookmark, and be automatically directed to the desired stores when we select it.

Worst performing restaurant analysis

When comparing the best and worst performing stores, we see that the worst performing store (10 Resort Plz) had a below average revenue amount in December 2021. We’re interested in seeing how we can better boost sales around the holiday season and if there is something the owner lacked in that month. We can create a bookmark that directs us to the Owner View with the filters selected to December 2021 and the address and city pre-filtered.

Pennsylvania December 2021 analysis

After looking at this bookmark, we found that in the month of December, the call for support score in the restaurant category was high. Additionally, in the week of December 19, 2021 product sales were at the monthly low. We want to see how these KPIs compare to other stores in the same state for that week. Is it just a seasonal trend, or do we need to look even deeper and take action? Again, we can create a bookmark that gives us this comparison on a state level.

From the last bookmark, we can see that the other store in the same state (Pennsylvania) also had lower than average product sales in the week of December 19, 2021. For the purpose of this demo, we can go ahead and stop here, but we could create even more bookmarks to help answer additional questions, such as if we see this trend across other cities and states.

With these three bookmarks, we have created a top-down presentation looking at the worst performing store and its relevant metrics and comparisons. If we wanted to present this, it would be as simple as cycling through the bookmarks we’ve created to put together a cohesive presentation instead of having distracting onscreen filtering.


In this post, we discussed how we can create, modify, and delete bookmarks, along with setting it as a default view and sharing bookmarks. We also demonstrated an extended use case of presentation with bookmarks. The new bookmarks functionality is now generally available in all supported QuickSight Regions.

We’re looking forward to your feedback and stories on how you apply these calculations for your business needs.

Did you know that you can also ask questions of you data in natural language with QuickSight Q? Watch this video to learn more.

About the Authors

Mei Qu is a Business Intelligence Engineer on the AWS QuickSight ProServe Team. She helps customers and partners leverage their data to develop key business insights and monitor ongoing initiatives. She also develops QuickSight proof of concept projects, delivers technical workshops, and supports implementation projects.

Emily Zhu is a Senior Product Manager at Amazon QuickSight, AWS’s cloud-native, fully managed SaaS BI service. She leads the development of QuickSight analytics and query capabilities. Before joining AWS, she was working in the Amazon Prime Air drone delivery program and the Boeing company as senior strategist for several years. Emily is passionate about potentials of cloud-based BI solutions and looks forward to helping customers advance in their data-driven strategy-making.

Amazon Personalize customer outreach on your ecommerce platform

Post Syndicated from Sridhar Chevendra original https://aws.amazon.com/blogs/architecture/amazon-personalize-customer-outreach-on-your-ecommerce-platform/

In the past, brick-and-mortar retailers leveraged native marketing and advertisement channels to engage with consumers. They have promoted their products and services through TV commercials, and magazine and newspaper ads. Many of them have started using social media and digital advertisements. Although marketing approaches are beginning to modernize and expand to digital channels, businesses still depend on expensive marketing agencies and inefficient manual processes to measure campaign effectiveness and understand buyer behavior. The recent pandemic has forced many retailers to take their businesses online. Those who are ready to embrace these changes have embarked on a technological and digital transformation to connect to their customers. As a result, they have begun to see greater business success compared to their peers.

Digitizing a business can be a daunting task, due to lack of expertise and high infrastructure costs. By using Amazon Web Services (AWS), retailers are able to quickly deploy their products and services online with minimal overhead. They don’t have to manage their own infrastructure. With AWS, retailers have no upfront costs, have minimal operational overhead, and have access to enterprise-level capabilities that scale elastically, based on their customers’ demands. Retailers can gain a greater understanding of customers’ shopping behaviors and personal preferences. Then, they are able to conduct effective marketing and advertisement campaigns, and develop and measure customer outreach. This results in increased satisfaction, higher retention, and greater customer loyalty. With AWS you can manage your supply chain and directly influence your bottom line.

Building a personalized shopping experience

Let’s dive into the components involved in building this experience. The first step in a retailer’s digital transformation journey is to create an ecommerce platform for their customers. This platform enables the organization to capture their customers’ actions, also referred to as ‘events’. Some examples of events are clicking on the shopping site to browse product categories, searching for a particular product, adding an item to the shopping cart, and purchasing a product. Each of these events gives the organization information about their customer’s intent, which is invaluable in creating a personalized experience for that customer. For instance, if a customer is browsing the “baby products” category, it indicates their interest in that category even if a purchase is not made. These insights are typically difficult to capture in an in-store experience. Online shopping makes gaining this knowledge much more straightforward and scalable.

The proposed solution outlines the use of AWS services to create a digital experience for a retailer and consumers. The three key areas are: 1) capturing customer interactions, 2) making real-time recommendations using AWS managed Artificial Intelligence/Machine Learning (AI/ML) services, and 3) creating an analytics platform to detect patterns and adjust customer outreach campaigns. Figure 1 illustrates the solution architecture.

Digital shopping experience architecture

Figure 1. Digital shopping experience architecture

For this use case, let’s assume that you are the owner of a local pizzeria, and you manage deliveries through an ecommerce platform like Shopify or WooCommerce. We will walk you through how to best serve your customer with a personalized experience based on their preferences.

The proposed solution consists of the following components:

  1. Data collection
  2. Promotion campaigns
  3. Recommendation engine
  4. Data analytics
  5. Customer reachability

Let’s explore each of these components separately.

Data collection with Amazon Kinesis Data Streams

When a customer uses your web/mobile application to order a pizza, the application captures their activity as click-stream ‘events’. These events provide valuable insights about your customers’ behavior. You can use these insights to understand the trends and browsing pattern of prospects who visited your web/mobile app, and use the data collected for creating promotion campaigns. As your business scales, you’ll need a durable system to preserve these events against system failures, and scale based on unpredictable traffic on your platform.

Amazon Kinesis is a Multi-AZ, managed streaming service that provides resiliency, scalability, and durability to capture an unlimited number of events without any additional operational overhead. Using Kinesis producers (Kinesis Agent, Kinesis Producer Library, and the Kinesis API), you can configure applications to capture your customer activity. You can ingest these events from the frontend, and then publish them to Amazon Kinesis Data Streams.

Let us start by setting up Amazon Kinesis Data Streams to capture the real-time sales transactions from the online channels like a portal or mobile app. For this blog post, we have used the Kaggle’s public data set as a reference. Figure 2 illustrates a snapshot of sample data to build personalized recommendations for a customer.

Sample sales transaction data

Figure 2. Sample sales transaction data

Promotion campaigns with AWS Lambda

One way to increase customer conversion is by offering discounts. When the customer adds a pizza to their cart, you want to make sure they are receiving the best deal. Let’s assume that by adding an additional item, your customer will receive the best possible discount. Just by knowing the total cost of added items to the cart, you can provide these relevant promotions to this customer.

For this scenario, the AWS Lambda service polls the Amazon Kinesis Data Streams to read all the events in the stream. It then matches the events based on your criteria of items in the cart. In turn, these events will be processed by the Lambda function. The Lambda function will read your up-to-date promotions stored in Amazon DynamoDB. As an option, caching recent or most popular promotions will improve your application response time, as well as improve the customer experience on your platform. Amazon DynamoDB DAX is an integrated caching for DynamoDB that caches the most recent or popular promotions or items.

For example, when the customer added the items to their shopping cart, Lambda will send promotion details to them based on the purchase amount. This can be for free shipping or discount of a certain percentage. Figure 3 illustrates the snapshot of sample promotions.

Promotions table in DynamoDB

Figure 3. Promotions table in DynamoDB

Recommendations engine with Amazon Personalize

In addition to sharing these promotions with your customer, you may also want to share the recommended add-ons. In order to understand your customer preferences, you must gather historical datasets to determine patterns and generate relevant recommendations. Since web activity consists of millions of events, this would be a daunting task for humans to review, determine the patterns, and make recommendations. And since user preferences change, you need a system that can use all this volume of data and provide accurate predictions.

Amazon Personalize is a managed AI/ML service that will help you to train an ML model based on datasets. It provides an inference point for real-time recommendations prior to having ML experience. Based on the datasets, Amazon Personalize also provides recipes to generate recommendations. As your customers interact on the ecommerce platform, your frontend application calls Amazon Personalize inference endpoints. It then retrieves a set of personalized recommendations based on your customer preferences.

Here is the sample Python code to display the list of available recommenders, and associated recommendations.

import boto3
import json
client = boto3.client('personalize')

# Connect to the personalize runtime for the customer recommendations

recomm_endpoint = boto3.client('personalize-runtime')
response = recomm_endpoint.get_recommendations(itemId='79323P',

print(json.dumps(response['itemList'], indent=2))

    "itemId": "79323W"
    "itemId": "79323GR"
    "itemId": "79323LP"
  "itemId": "79323B"
    "itemId": "79323G"

You can use Amazon Kinesis Data Firehose to read the data near real time from the Amazon Kinesis Data Streams collected the data from the front-end applications. Then you can store this data in Amazon Simple Storage Service (S3). Amazon S3 is peta-byte scale storage help you scale and acts as a repository and single source of truth. We use S3 data as seed data to build a personalized recommendation engine using Amazon Personalize. As your customers interact on the ecommerce platform, call the Amazon Personalize inference endpoint to make personalized recommendations based on user preferences.

Customer reachability with Amazon Pinpoint

If a customer adds products to their cart but never checks out, you may want to send them a reminder. You can set up an email to suggest they re-order after a period of time after their first order. Or you may want to send them promotions based on their preferences. And as your customers’ behavior changes, you probably want to adapt your messaging accordingly.

Your customer may have a communication preference, such as phone, email, SMS, or in-app notifications. If an order has an issue, you can inform the customer as soon as possible using their preferred method of communication, and perhaps follow it up with a discount.

Amazon Pinpoint is a flexible and scalable outbound and inbound marketing communications service. You can add users to Audience Segments, create reusable content templates integrated with Amazon Personalize, and run scheduled campaigns. With Amazon Pinpoint journeys, you can send action or time-based notifications to your users.

The following workflow shown in Figure 4, illustrates customer communication workflow for promotion. A journey is created for a cohort of college students: a “Free Drink” promotion is offered with a new order. You can send this promotion over email. If the student opens the email, you can immediately send them a push notification reminding them to place an order. But if they didn’t open this email, you could wait three days, and follow up with a text message.

Promotion workflow in Amazon Pinpoint

Figure 4. Promotion workflow in Amazon Pinpoint

Data analytics with Amazon Athena and Amazon QuickSight

To understand the effectiveness of your campaigns, you can use S3 data as a source for Amazon Athena. Athena is an interactive query service that analyzes data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

There are different ways to create visualizations in Amazon QuickSight. For instance, you can use Amazon S3 as a data lake. One option is to import your data into SPICE (Super-fast, Parallel, In-memory Calculation Engine) to provide high performance and concurrency. You can also create a direct connection to the underlying data source. For this use case, we choose to import to SPICE, which provides faster visualization in a production setup. Schedule consistent refreshes to help ensure that dashboards are referring to the most current data.

Once your data is imported to your SPICE, review QuickSight’s visualization dashboard. Here, you’ll be able to choose from a wide variety of charts and tables, while adding interactive features like drill downs and filters.

The process following illustrates how to create a customer outreach strategy using ZIP codes, and allocate budgets to the marketing campaigns accordingly. First, we use this sample SQL command that we ran in Athena to query for top 10 pizza providers. The results are shown in Figure 5.

SELECT name, count(*) as total_count FROM "analyticsdemodb"."fooddatauswest2"
group by name
order by total_count desc
limit 10

Athena query results for top 10 pizza providers

Figure 5. Athena query results for top 10 pizza providers

Second, here is the sample SQL command that we ran in Athena to find Total pizza counts by postal code (ZIP code). Figure 6 shows a visualization to help create customer outreach strategy per ZIP codes and budget the marketing campaigns accordingly.

SELECT postalcode, count(*) as total_count FROM "analyticsdemodb"."fooddatauswest2"
where postalcode is not null
group by postalcode
order by total_count desc limit 50;

QuickSight visualization showing pizza orders by zip codes

Figure 6. QuickSight visualization showing pizza orders by zip codes


AWS enables you to build an ecommerce platform and scale your existing business with minimal operational overhead and no upfront costs. You can augment your ecommerce platform by building personalized recommendations and effective marketing campaigns based on your customer needs. The solution approach provided in the blog will help organizations build re-usable architecture pattern and personalization using AWS managed services.

Enable self-service visual data integration and analysis for fund performance using AWS Glue Studio and Amazon QuickSight

Post Syndicated from Rajeshkumar Karuppaswamy original https://aws.amazon.com/blogs/big-data/enable-self-service-visual-data-integration-and-analysis-for-fund-performance-using-aws-glue-studio-and-amazon-quicksight/

IMM (Institutional Money Market) is a mutual fund that invests in highly liquid instruments, cash, and cash equivalents. IMM funds are large financial intermediaries that are crucial to financial stability in the US. Due to its criticality, IMM funds are highly regulated under the security laws, notably Rule 2a-7, Which states that during market stress, fund managers can impose a liquidity fee up to 2% or redemption gates (a delay in processing redemption) if the fund’s weekly liquid assets drop below 30% of its total assets. The liquidity fees and gates allow money market funds to stop heavy redemption in times of market volatility.

Traditional banks use legacy systems and rely on monolithic architectures. Typically, data and business logic is tightly coupled on the same mainframe machines. It’s hard for analysts and fund managers to perform self-service and gather real-time analytics from these legacy systems. They work on the previous nightly report and struggle to keep up with market fluctuations. The slightest modification to the reports on these legacy systems involves vast costs, time, and significant dependency on the software development team. Due to these limitations, analysts and fund managers can’t respond effectively to market trends and face a tremendous challenge in adhering to the regulatory requirements of monitoring the market volatility.

Over the last few years, many banks have adopted the cloud. Banks have migrated their legacy workloads to reduce cost, improve their competitive advantage, and address competition from FinTech and startups. As part of the cloud strategy, many mainframe applications got re-platformed or re-architected to a more efficient database platform. However, many opportunities exist in modernizing the application. One such option is to enable self-service to run real-time analytics. AWS offers various services that help such use cases. In this post, we demonstrate how to analyze fund performance visually using AWS Glue Studio and QuickSight in a self-service fashion.

The aim of the post is to assist operations analysts and fund managers to self-service their data analysis needs without previous coding experience. This post demonstrates how AWS Glue Studio reduces the software development team’s dependency and helps analysts and fund managers perform near-real-time analytics. This post also illustrates how to build visualizations and quickly get business insights using Amazon QuickSight.

Solution overview

Most banks record their daily trading transactions activity in relational database systems. A relational database keeps the ledger of daily transactions that involves many buys and sells of IMM funds. We use the mock trades data and a simulated Morningstar data feed to demonstrate our use case.

The following sample Amazon Relational Database Service (Amazon RDS) instance records daily IMM trades, and Morningstar market data gets stored in Amazon Simple Storage Service (Amazon S3). With AWS Glue Studio, analysts and fund managers can analyze the IMM trades in near-real time and compare them with market observations from Morningstar. They can then review the data in Amazon Athena, and use QuickSight to visualize and further analyze the trade patterns and market trends.

This near-real time and self-service enables fund managers quickly respond to the market volatility and apply fees or gates on IMM funds to comply with Rule 2a-7 regulatory requirements.

The following diagram illustrates the solution architecture.

Provision resources with AWS CloudFormation

To create your resources for this use case, we deploy an AWS CloudFormation template. Complete the following steps:

  1. Choose Launch Stack (in us-east-1):
  2. Choose Next three times to reach the Review step.
  3. Select I acknowledge that AWS CloudFormation might create IAM resources.
  4. Choose Create stack.

Create an AWS Glue connection

You create an AWS Glue connection to access the MySQL database created by the CloudFormation template. An AWS Glue crawler uses the connection in the next step.

  1. On the AWS Glue console, under Databases in the navigation pane, choose Connections.
  2. Choose Add connection.
  3. For Connection name, enter Trade-Analysis.
  4. For Connection type¸ choose JDBC.
  5. Choose Next.
  6. For JDBC URL, enter your URL.
    To connect to an Amazon RDS for MySQL data store with a DBDEV database, use the following code:

    jdbc: mysql://xxx-cluster.cluster-xxx.us-east-1.rds.amazonaws.com:3306/DBDEV

    For more details, see AWS Glue connection properties. Refer to the CloudFormation fund-analysis stack Outputs tab to get the Amazon RDS ARN.

    The next step requires you to first retrieve your MySQL database user name and password via AWS Secrets Manager.

  7. On the Secrets Manager console, choose Secrets in the navigation pane.
  8. Choose the secret rds-secret-fund-analysis.
  9. Choose Retrieve secret value to get the user name and password.
  10. Return to the connection configuration and enter the user name and password.
  11. For VPC, choose the VPC ending with fund-analysis.
  12. For Subnet and Security groups, choose the values ending with fund-analysis.
  13. Choose Next and Finish to complete the connection setup.
  14. Select the connection you created and choose Test Connection.
  15. For IAM role, choose the role AWSGlueServiceRole-Studio.

For more details about using AWS Identity and Access Management (IAM), refer to Setting up for AWS Glue Studio.

Create and run AWS Glue crawlers

In this step, you create two crawlers. The crawlers connect to a data store, determine the schema for your data, and then create metadata tables in your AWS Glue Data Catalog.

Crawl MySQL data stores

The first crawler creates metadata for the MySQL data stores. Complete the following steps:

  1. On the AWS Glue console, choose Crawlers in the navigation pane.
  2. Choose Add crawler.
  3. For Crawler name, enter Trades Crawlers.
  4. Choose Next.
  5. For Crawler source type, choose Data stores.
  6. For Repeat crawls of S3 data stores, choose Crawl all folders.
  7. Choose Next.
  8. For Choose a data store, choose JDBC.
  9. For Connection, choose Trade-Analysis.
  10. For Include path, enter the MySQL database name (DBDEV).
  11. Choose Next.
  12. For Add another data store, choose No.
  13. Choose Next.
  14. For the IAM role to access the data stores, choose the role AWSGlueServiceRole-Studio.
  15. For Frequency, choose Run on demand.
  16. Choose Add database.
  17. For Database name, enter trade_analysis_db.
  18. Choose Create.
  19. Choose Next.
  20. Review all the steps and choose Finish to create your crawler.
  21. Select the Trades Crawlers crawler and choose Run crawler to get the metadata.

Crawl Amazon S3 data stores

Now you configure a crawler to create metadata for the Amazon S3 data stores.

  1. On the AWS Glue console, choose Crawlers in the navigation pane.
  2. Choose Add crawler.
  3. For Crawler name, enter Ratings.
  4. Choose Next.
  5. For Crawler source type, choose Data stores.
  6. For Repeat crawls of S3 data stores, choose Crawl all folders.
  7. Choose Next.
  8. For Choose a data store, choose S3.
  9. For Connection, choose Trade-Analysis.
  10. For Include path, enter s3://aws-bigdata-blog/artifacts/analyze_fund_performance_using_glue/Morningstar.csv.
  11. Choose Next.
  12. For Add another data store, choose No.
  13. Choose Next.
  14. For the IAM role to access the data stores, choose the role AWSGlueServiceRole-Studio.
  15. For Frequency, choose Run on demand.
  16. Choose Add database.
  17. For Database name, enter trade_analysis_db.
  18. Review all the steps and choose Finish to create your crawler.
  19. Select the Ratings crawler and choose Run crawler to get the metadata.

Review crawler output

To review the output of your two crawlers, navigate to the Databases page on the AWS Glue console.

You can review the database trade_analysis_db created in previous steps and the contents of the metadata tables.

Create a job using AWS Glue Studio

A job is the AWS Glue component that allows the implementation of business logic to transform data as part of the extract, transform, and load (ETL) process. For more information, see Adding jobs in AWS Glue.

To create an AWS Glue job using AWS Glue Studio, complete the following steps:

  1. On the AWS Glue console, in the navigation pane, choose AWS Glue Studio.
  2. Choose Create and manage jobs.
  3. Choose View jobs.
    AWS Glue Studio supports different sources. For this post, you use two AWS Glue tables as data sources and one S3 bucket as the destination.
  4. In the Create job section, select Visual with a blank canvas.
  5. Choose Create.

    This takes you to the visual editor to create an AWS Glue job.
  6. Change the job name from Untitled Job to Trade-Analysis-Job.

You now have an AWS Glue job ready to filter, join, and aggregate data from two different sources.

Add two data sources

For this post, you use two AWS Glue tables as data sources: Trades and Ratings, which you created earlier.

  1. On the AWS Glue Studio console, on the Source menu, choose MySQL.
  2. On the Node properties tab, for Name, enter Trades.
  3. For Node type, choose MySQL.
  4. On the Data Source properties – MySQL tab, for Database, choose trade_analysis_db.
  5. For Table, choose dbdev_mft_actvitity.
    Before adding the second data source to the analysis job, be sure that the node you just created isn’t selected.
  6. On the Source menu, choose Amazon S3.
  7. On the Node properties tab, for Name, enter Ratings.
  8. For Node type, choose Amazon S3.
  9. On the Data Source properties – S3 tab, for Database, choose trade_analysis_db.
  10. For Table, choose morning_star_csv.
    You now have two AWS Glue tables as the data sources for the AWS Glue job.The Data preview tab helps you sample your data without having to save or run the job. The preview runs each transform in your job so you can test and debug your transformations.
  11. Choose the Ratings node and on the Data preview tab, choose Start data preview session.
  12. Choose the AWSGlueServiceRole-Studio IAM role and choose Confirm to sample the data.

Data previews are available for each source, target, and transform node in the visual editor, so you can verify the results step by step for other nodes.

Join two tables

A transform is the AWS Glue Studio component were the data is modified. You have the option of using different transforms that are part of this service or custom code. To add transforms, complete the following steps:

  1. On the Transform menu, choose Join.
  2. On the Node properties tab, for Name, enter trades and ratings join.
  3. For Node type, choose Join.
  4. For Node parents, choose the Trades and Ratings data sources.
  5. On the Transform tab, for Join type, choose Outer join.
  6. Choose the common column between the tables to establish the connection.
  7. For Join conditions, choose symbol from the Trades table and mor_rating_fund_symbol from the Ratings table.

Add a target

Before adding the target to store the result, be sure that the node you just created isn’t selected. To add the target, complete the following steps:

  1. On the Target menu, choose Amazon S3.
  2. On the Node properties tab, for Name, enter trades ratings merged.
  3. For Node type, choose Amazon S3 for writing outputs.
  4. For Node parents, choose trades and ratings join.
  5. On the Data target properties – S3 tab, for Format, choose Parquet.
  6. For Compression type, choose None.
  7. For S3 target location, enter s3://glue-studio-blog- {Your Account ID as a 12-digit number}/.
  8. For Data catalog update options, select Create a table in the Data Catalog and on subsequent runs, update the schema and add new partitions.
  9. For Database, choose trade-analysis-db.
  10. For Table name, enter tradesratingsmerged.

Configure the job

When the logic behind the job is complete, you must set the parameters for the job run. In this section, you configure the job by selecting components such as the IAM role and the AWS Glue version you use to run the job.

  1. Choose the Job details tab.
  2. For Job bookmark, choose Disable.
  3. For Number of retries, optionally enter 0.
  4. Choose Save.
  5. When the job is saved, choose Run.

Monitor the job

AWS Glue Studio offers a job monitoring dashboard that provides comprehensive information about your jobs. You can get job statistics and see detailed information about the job and the job status when running.

  1. In the AWS Glue Studio navigation pane, choose Monitoring.
  2. Change the date range to 1 hour using the Date range selector to get the recently submitted job.
    The Job runs summary section displays the current state of the job run. The status of the job could be Running, Canceled, Success, or Failed.The Job run success rate section provides the estimated DPU usage for jobs, and gives you a summary of the performance of the job. Job type breakdown and Worker type breakdown contain additional information about the job.
  3. For get more details about the job run, choose View run details.

Review the results using Athena

To view the data in Athena, complete the following steps:

  1. Navigate to the Athena console, where you can see the database and tables created by your crawlers.

    If you haven’t used Athena in this account before, a message appears instructing you to set a query result location.
  2. Choose Settings, Manage, Browse S3, and select any bucket that you created.
  3. Choose Save and return to the editor to continue.
  4. In the Data section, expand Tables to see the tables you created with the AWS Glue crawlers.
  5. Choose the options menu (three dots) next to one of the tables and choose Preview Table.

The following screenshot shows an example of the data.

Create a QuickSight dashboard and visualizations

To set up QuickSight for the first time, sign up for a QuickSight subscription and allow connections to Athena.

To create a dashboard in QuickSight based on the AWS Glue Data Catalog tables you created, complete the following steps:

  1. On the QuickSight console, choose Datasets in the navigation pane.
  2. Choose New dataset.
  3. Create a new QuickSight dataset called Fund-Analysis with Athena as the data source.
  4. In the Choose your table section, choose AwsDataCatlog for Catalog and choose trade_analysis_db for Database.
  5. For Tables, select the tradesratingmerged table to visualize.
  6. Choose Select.
  7. Import the data into SPICE.
    SPICE is an in-memory engine that QuickSight uses to perform advanced calculations and improve performance. Importing the data into SPICE can save time and money. When using SPICE, you can refresh your datasets both fully or incrementally. As of this writing, you can schedule incremental refreshes up to every 15 minutes. For more information, refer to Refreshing SPICE data. For near-real-time analysis, select Directly query your data instead.
  8. Choose Visualize.

    After you create the dataset, you can view it and edit its properties. For this post, leave the properties unchanged.
  9. To analyze the market performance from the Morningstar file, choose the clustered bar combo chart under Visual types.
  10. Drag Fund_Symbol from Fields list to X-axis.
  11. Drag Ratings to Y-axis and Lines.
  12. Choose the default title choose Edit title to change the title to “Market Analysis.”
    The following QuickSight dashboard was created using a custom theme, which is why the colors may appear different than yours.
  13. To display the Morningstar details in tabular form, add a visual to create additional graphs.
  14. Choose the table visual under Visual types.
  15. Drag Fund Symbol and Fund Names to Group by.
  16. Drag Ratings, Historical Earnings, and LT Earnings to Value.

    In QuickSight, up until this point, you analyzed the market performance reported by Morningstar. Let’s analyze the near-real-time daily trade activities.
  17. Add a visual to create additional graphs.
  18. Choose the clustered bar combo chart under Visual types.
  19. Drag Fund_Symbol from Fields list to X-axis and Trade Amount to Y-axis.
  20. Choose the default title choose Edit title to change the title to “Daily Transactions.”
  21. To display the daily trades in tabular form, add a visual to create additional graphs.
  22. Drag Trade Date, Customer Name, Fund Name, Fund Symbol, and Buy/Sell to Group by.
  23. Drag Trade Amount to Value.

The following screenshot shows a complete dashboard. This compares the market observation reported in the street against the daily trades happening in the bank.

In the Market Analysis section of the dashboard, GMFXXD funds were performing well based on the previous night’s feed from Morningstar. However, the Daily Transactions section of the dashboard shows that customers were selling their positions from the funds. Relying only on the previous nightly batch report will mislead the fund managers or operation analyst to act.

Near-real-time analytics using AWS Glue Studio and QuickSight can enable fund managers and analysts to self-serve and impose fees or gates on those IMM funds.

Clean up

To avoid incurring future charges and to clean up unused roles and policies, delete the resources you created: the CloudFormation stack, S3 bucket, and AWS Glue job.


In this post, you learned how to use AWS Glue Studio to analyze data from different sources with no previous coding experience and how to build visualizations and get business insights using QuickSight. You can use AWS Glue Studio and QuickSight to speed up the analytics process and allow different personas to transform data with no development experience.

For more information about AWS Glue Studio, see the AWS Glue Studio User Guide. For information about QuickSight, refer to the Amazon QuickSight User Guide.

About the authors

Rajeshkumar Karuppaswamy is a Customer Solutions Manager at AWS. In this role, Rajeshkumar works with AWS Customers to drive Cloud strategy, provides thought leadership to accelerate businesses achieve speed, agility, and drive innovation. His areas of interests are AI & ML, analytics, and data engineering.

Richa Kaul is a Senior Leader in Customer Solutions serving Financial Services customers. She is based out of New York. She has extensive experience in large scale cloud transformation, employee excellence, and next generation digital solutions. She and her team focus on optimizing value of cloud by building performant, resilient and agile solutions. Richa enjoys multi sports like triathlons, music, and learning about new technologies.

Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team. He is responsible for building software artifacts to help customers. This summer, he enjoyed goldfish scooping with his children.

Talk to your data: Query your data lake with Amazon QuickSight Q

Post Syndicated from Ying Wang original https://aws.amazon.com/blogs/big-data/talk-to-your-data-query-your-data-lake-with-amazon-quicksight-q/

Amazon QuickSight Q uses machine learning (ML) and natural language technology to empower you to ask business questions about your data and get answers instantly. You can simply enter your questions (for example, “What is the year-over-year sales trend?”) and get the answer in seconds in the form of a QuickSight visual.

Some business questions can’t be answered through existing business intelligence (BI) dashboards. It can take days or weeks for the BI team to accommodate these needs and refine their solution. Because Q doesn’t depend on prebuilt dashboards or reports to answer questions, it removes the need for BI teams to create or update dashboards every time a new business question arises. You can ask questions and receive answers in the form of visuals in seconds directly from within QuickSight or from web applications and portals. Q empowers every business user to self-serve and get insights faster, regardless of their background or skillset.

In this post, we walk you through the steps to configure Q using an Olympic Games public dataset and demonstrate how an end-user can ask simple questions directly from Q in an interactive manner and receive answers in seconds.

You can interactively play with the Olympic dashboard and Q search bar in the following interactive demo.

Solution overview

We use Olympic games public datasets to configure a Q topic and discuss tips and tricks on how to make further configurations on the topic that enable Q to provide prompt answers using ML-powered, natural language query (NLQ) capabilities that empower you to ask questions about data using everyday business language.

The video from Data Con LA provides a high-level demonstration of the capabilities covered in this post.

Additionally, we discuss the following:

  • Best practices for data modeling of a Q topic
  • How to perform data cleansing using AWS Glue DataBrew, SQL, or an Amazon SageMaker Jupyter notebook on datasets to build a Q topic

We use multiple publicly available datasets from Kaggle. The datasets have historical information about athletes, including name, ID, age, weight, country, and medals.

We use the 2020 Olympic datasets and historical data. We also use the datasets Introduction of Women Olympic Sport and Women of Olympic Games to determine the participation of women athletes in Olympics and discover trends. The QuickSight datasets created using these public data files are added to a Q topic, as shown in the following screenshot. We provide details on creating QuickSight datasets later in this post.


To follow along with the solution presented in this post, you must have access to the following:

Create solution resources

The public datasets in Kaggle can’t be directly utilized to create a Q topic. We have already cleansed the raw data and have provided the cleansed datasets in the GitHub repo. If you are interested in learning more about data cleansing, we discussed three different data cleansing methods at the end of this post.

To create your resources, complete the following steps:

  1. Create an S3 bucket called olympicsdata.
  2. Create a folder for each data file, as shown in the following screenshot.
  3. Upload the data files from the GitHub repo into their respective folders.
  4. Deploy the provided CloudFormation template and provide the necessary information.

The template creates an Athena database and tables, as shown in the following screenshot.

The template also creates the QuickSight data source athena-olympics and datasets.

Create datasets in QuickSight

To build the Q topic, we need to combine the datasets, because each table contains only partial data. Joining these tables helps answer questions across all the features of the 2020 Olympics.

We create the Olympics 2021 dataset by joining the tables Medals_athletes_2021, Athletes_full_2021, Coach_full_2021, and Tech_official_2021.

The following screenshot shows the joins for our complete dataset.

Medals_athletes_2021 is the main table, with the following join conditions:

  • Left outer join athletes_full_2021 on athlete_name, discipline_code, and country_code
  • Left outer join coach_full_2021 on country, discipline, and event
  • Left outer join tech_official_2021 on discipline

Finally, we have the following datasets that we use for our Q topic:

  • Olympics 2021 Details
  • Medals 2021
  • Olympics History (created using the Olympics table)
  • Introduction of Women Olympics Sports
  • Women in the Olympic Movement

Create a Q topic

Topics are collections of one or more datasets that represent a subject area that your business users can ask questions about. In QuickSight, you can create and manage topics on the Topics page. When you create a topic, your business users can ask questions about it in the Q search bar.

When you create topics in Q, you can add multiple datasets to them and then configure all the fields in the datasets to make them natural language-friendly. This enables Q to provide your business users with the correct visualizations and answers to their questions.

The following are data modeling best practices for Q topics:

  • Reduce the number of datasets by consolidating the data. Any given question can only hit one data set, so only include multiple datasets if they are related enough to be part of the same topic, but distinct enough that you can ask a question against them independently.
  • For naming conventions, provide a meaningful name or alias (synonym) of a field to allow the end-user to easily query it.
  • If a field appears in different datasets, make sure that this field has the same name across different datasets.
  • Validate data consistency. For example, the total value of a metric that aggregates from different datasets should be consistent.
  • For fields that don’t request on-the-fly calculations, for example, metrics with distributive functions (sum, max, min, and so on), push down the calculation into a data warehouse.
  • For fields that request on-the-fly calculations, create the calculated field in the QuickSight dataset or Q topic. If other topics or dashboards might reuse the same field, create it in the datasets.

To create a topic, complete the following steps:

  1. On the QuickSight console, choose Topics in the navigation pane.
  2. Choose New topic.
  3. For Topic name, enter a name.
  4. For Description, enter a description.
  5. Choose Save.
  6. On the Add data to topic page that opens, choose Datasets, and then select the datasets that we created in the previous section.
  7. Choose Add data to create the topic.

Enhance the topic

In this section, we discuss various ways that you can enhance the topic.

Add calculated fields to a topic dataset

You can add new fields to a dataset in a topic by creating calculated fields.

For example, we have the column Age in our Olympics dataset. We can create a calculated field to group age into different ranges using the ifelse function. This calculated field can help us ask a question like “How many athletes for each age group?”

  1. Choose Add calculated field.
  2. In the calculation editor, enter the following syntax:
    Age<=20, '0-20',
    Age>20 and Age <=40, '21-40',
    Age>40 and Age<=60, '41-60',

  3. Name the calculated field Age Groups.
  4. Choose Save.

The calculated field is added to the list of fields in the topic.

Add filters to a topic dataset

Let’s say lot of analysis is expected on the dataset for the summer season. We can add a filter to allow for easy selection of this value. Furthermore, if we want to allow analysis against data for the summer season only, we can choose to always apply this filter or apply it as the default choice, but allow users to ask questions about other seasons as well.

  1. Choose Add filter.
  2. For Name, enter Summer.
  3. Choose the Women in the Olympic Movement dataset.
  4. Choose the Olympics Season field.
  5. Choose Custom filter list for Filter type and set the rule as include.
  6. Enter Summer under Values.
  7. Choose Apply always, unless a question results in an explicit filter from the dataset.
  8. Choose Save.

The filter is added to the list of fields in the topic.

Add named entities to a topic dataset

We can define named entities if we need to show users a combination of fields. For example, when someone asks for player details, it makes sense to show them player name, age, country, sport, and medal. We can make this happen by defining a named entity.

  1. Choose Add named entity.
  2. Choose the Olympics dataset.
  3. Enter Player Profile for Name.
  4. Enter Information of Player for Description.
  5. Choose Add field.
  6. Choose Player Name from the list.
  7. Choose Add field again and add the fields Age, Countries, Sport, and Medal.
    The fields listed are the order they appear in answers. To move a field, choose the six dots next to the name and drag and drop the field to the order that you want.
  8. Choose Save.

The named entity is added to the list of fields in the topic.

Make Q topics natural language-friendly

To help Q interpret your data and better answer your readers’ questions, provide as much information about your datasets and their associated fields as possible.

To make the topic more natural language-friendly, use the following procedures.

Rename fields

You can make your field names more user-friendly in your topics by renaming them and adding descriptions.

Q uses field names to understand the fields and link them to terms in your readers’ questions. When your field names are user-friendly, it’s easier for Q to draw links between the data and a reader’s question. These friendly names are also presented to readers as part of the answer to their question to provide additional context.

Let’s rename the birth date field from the athlete dataset as Athlete Birth Date. Because we have multiple birth date fields in the topics for coach, athlete, and tech roles, renaming the athletes’ birth date field helps Q easily link to the data field when we ask questions regarding athletes’ birth dates.

  1. On the Fields page, choose the down arrow at far right of the Birth Date field to expand it.
  2. Choose the pencil icon next to the field name.
  3. Rename the field to Athlete Birth Date.

Add synonyms to fields in a topic

Even if you update your field names to be user-friendly and provide a description for them, your readers might still use different names to refer to them. For example, a player name field might be referred to as player, players, or sportsman in your reader’s questions.

To help Q make sense of these terms and map them to the correct fields, you can add one or more synonyms to your fields. Doing this improves Q’s accuracy.

  1. On the Fields page, under Synonyms, choose the pencil icon for Player Name.
  2. Enter player and sportsman as synonyms.

Add synonyms to field values

Like we did for field names, we can add synonyms for category values as well.

  1. Choose the Gender field’s row to expand it.
  2. Choose Configure value synonyms, then choose Add.
  3. Choose the pencil icon next to the F value.
  4. Add the synonym Female.
  5. Repeat these steps to add the synonym Male for M.
  6. Choose Done.

Assign field roles

Every field in your dataset is either a dimension or a measure. Knowing whether a field is a dimension or a measure determines what operations Q can and can’t perform on a field.

For example, setting the field Age as a dimension means that Q doesn’t try to aggregate it as it does measures.

  1. On the Fields page, expand the Age field.
  2. For Role, choose Dimension.

Set field aggregations

Setting field aggregations tells Q which function should or shouldn’t be used when those fields are aggregated across multiple rows. You can set a default aggregation for a field, and specify aggregations that aren’t allowed.

A default aggregation is the aggregation that’s applied when there’s no explicit aggregation function mentioned or identified in a reader’s question. For example, let’s ask Q “Show total number of events.” In this case, Q uses the field Total Events, which has a default aggregation of Sum, to answer the question.

  1. On the Fields page, expand the Total Events field.
  2. For Default aggregation, choose Sum.
  3. For Not allowed aggregation, choose Average.

Specify field semantic types

Providing more details on the field context will help Q answer more natural language questions. For example, users might ask “Who won the most medals?” We haven’t set any semantic information for any fields in our dataset yet, so Q doesn’t know what fields to associate with “who.” Let’s see how we can enable Q to tackle this question.

  1. On the Fields page, expand the Player Name field.
  2. For Semantic Type, choose Person.

This enables Q to surface Player Name as an option when answering “who”-based questions.

Exclude unused or unnecessary fields

Fields from all included datasets are displayed by default. However, we have a few fields like Short name of Country, URL Coach Full 2021, and URL Tech Official 2021 that we don’t need in our topic. We can exclude unnecessary fields from the topic to prevent them from showing up in results by choosing the slider next to each field.

Ask questions with Q

After we create and configure our topic, we can now interact with Q by entering questions in the Q search bar.

For example, let’s enter show total medals by country. Q presents an answer to your question as a visual.

You can see how Q interpreted your question in the description at the visual’s upper left. Here you can see the fields, aggregations, topic filters, and datasets used to answer the question. The topic filter na is applied on the Medal attribute, which excludes na values from the aggregation. For more information on topic filters, see Adding filters to a topic dataset.

Q displays the results using the visual type best suited to convey the information. However, Q also gives you the flexibility to view results in other visual types by choosing the Visual icon.

Another example, let’s enter who is the oldest player in basketball. Q presents an answer to your question as a visual.

Sometimes Q might not interpret your question the way you wanted. When this happens, you can provide feedback on the answer or make suggestions for corrections to the answer. For more information about providing answer feedback, see Providing feedback about QuickSight Q topics. For more information about correcting answers, see Correcting wrong answers provided by Amazon QuickSight Q.


In this post, we showed you how to configure Q using an Olympic games public dataset and so end-users can ask simple questions directly from Q in an interactive manner and receive answers in seconds. If you have any feedback or questions, please leave them in the comments section.

Appendix 1: Types of questions supported by Q

Let’s look at samples of each question type that Q can answer using the topic created earlier in this post.

Try the following questions or your own questions and continue enhancing the topic to improve accuracy of responses.

Question Type Example
Dimensional Group Bys show total medals by country
Dimensional Filters (Include) show total medals for united states
Date Group Bys show yearly trend of women participants
Multi Metrics number of women events compared to total events
KPI-Based Period over Periods (PoPs) how many women participants in 2018 over 2016
Relative Date Filters show total medals for united states in the last 5 years
Time Range Filters list of women sports introduced since 2016
Top/Bottom Filter show me the top 3 player with gold medal
Sort Order show top 3 countries with maximum medals
Aggregate Metrics Filter show teams that won more than 50 medals
List Questions list the women sports by year in which they are introduced
OR filters Show player who got gold or silver medal
Percent of Total Percentage of players by country
Where Questions where are the most number of medals
When Questions when women volleyball introduced into olympic games
Who Questions who is the oldest player in basketball
Exclude Questions show countries with highest medals excluding united states

Appendix 2: Data cleansing

In this section, we provide three options for data cleansing: SQL, DataBrew, and Python.

Option 1: SQL

For our first option, we discuss how to create Athena tables on the downloaded Excel or CSV files and then perform the data cleansing using SQL. This option is suitable for those who use Athena tables as a data source for QuickSight datasets and are comfortable using SQL.

The SQL queries to create Athena tables are available in the GitHub repo. In these queries, we perform data cleansing by renaming, changing the data type of some columns, as well as removing the duplicates of rows. Proper naming conventions and accurate data types help Q efficiently link the questions to the data fields and provide accurate answers.

Use the following sample DDL query to create an Athena table for women_introduction_to_olympics:

CREATE EXTERNAL TABLE women_introduction_to_olympics(
year string,
sport string)
's3://<<s3 bucket name>>/womeninolympics/introduction_of_women_olympic_sports'

In our data files, there are few columns that are common across more than one dataset that have different column names. For example, gender is available as gender or sex, country is available as country or team or team/noc, and person names have a role prefix in one dataset but not in other datasets. We rename such columns using SQL to maintain consistent column names.

Additionally, we need to change other demographic columns like age, height, and weight to the INT data type, so that they don’t get imported as String.

The following columns from the data files have been transformed using SQL.

Data File Original Column New Column
medals Discipline
Medal_date (timestamp)
Medal_date (date)
Athletes name
Coaches name
Athlete_events (history) Team
Age (String)
Height (String)
Weight (String)
Age (Integer)
Height (Integer)
Weight (Integer)

Option 2: DataBrew

In this section, we discuss a data cleansing option using DataBrew. DataBrew is a visual data preparation tool that makes it easy to clean and prepare data with no prior coding knowledge. You can directly load the results into an S3 bucket or load the data by uploading an Excel or CSV file.

For our example, we walk you through the steps to implement data cleansing on the medals_athletes_2021 dataset. You can follow the same process to perform any necessary data cleaning on other datasets as well.

Create a new dataset in DataBrew using medals_athletes.csv and then create a DataBrew project and implement the following recipes to cleanse the data in the medals_athletes_2021 dataset.

  1. Delete empty rows in the athlete_name column.
  2. Delete empty rows in the medal_type column.
  3. Delete duplicate rows in the dataset.
  4. Rename discipline to Sport.
  5. Delete the column discipline_code.
  6. Split the column medal_type on a single delimiter.
  7. Delete the column medal_type_2, which was created as a result of step 6.
  8. Rename medal_type_1 to medal_type.
  9. Change the data type of column medal_date from timestamp to date.

After you create the recipe, publish it and create a job to output the results in your desired destination. You can create QuickSight SPICE datasets by importing the cleaned CSV file.

Option 3: Python

In this section, we discuss data cleansing using NumPy and Pandas of Python on the medals_athletes_2021 dataset. You can follow the same process to perform any necessary data cleansing on other datasets as well. The sample Python code is available on GitHub. This option is suitable for someone who is comfortable processing the data using Python.

  1. Delete the column discipline_code:

  2. Rename the column discipline to sport:
    olympic.rename(columns={'discipline': 'sport'})

You can create QuickSight SPICE datasets by importing the cleansed CSV.

Appendix 3: Data cleansing and modeling in the QuickSight data preparation layer

In this section, we discuss one more method of data cleansing that you can perform from the QuickSight data preparation layer, in addition to the methods discussed previously. Using SQL, DataBrew, or Python have advantages because you can prepare and clean the data outside QuickSight so other AWS services can use the cleansed results. Additionally, you can automate the scripts. However, Q authors have to learn other tools and programming languages to take advantage of these options.

Cleansing data in the QuickSight dataset preparation stage allows non-technical Q authors to build the application end to end in QuickSight with a codeless method.

The QuickSight dataset stores any data preparation done on the data, so that the prepared data can be reused in multiple analyses and topics.

We have provided a few examples for data cleansing in the QuickSight data preparation layer.

Change a field name

Let’s change the name data field from Athletes_full_2021 to athlete_name.

  1. In the data preview pane, choose the edit icon on the field that you want to change.
  2. For Name, enter a new name.
  3. Choose Apply.

Change a field data type

You can change the data type of any field from the data source in the QuickSight data preparation layer using the following procedure.

  1. In the data preview pane, choose the edit icon on the field you want to change (for example, birth_date).
  2. Choose Change data type and choose Date.

This converts the string field to a date field.

Appendix 4: Information about the tables

The following table illustrates the scope of each table in the dataset.

Table Name Link Table Data Scope
medals https://www.kaggle.com/piterfm/tokyo-2020-olympics?select=medals.csv Information about medals won by each athlete and the corresponding event and country details
athletes https://www.kaggle.com/piterfm/tokyo-2020-olympics?select=athletes.csv Details about each athlete, such as demographic and country
coaches https://www.kaggle.com/piterfm/tokyo-2020-olympics?select=coaches.csv Details about each coach, such as demographic and country
technical_officials https://www.kaggle.com/piterfm/tokyo-2020-olympics?select=technical_officials.csv Details about each technical official, such as demographic and country
athlete_events https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results Historical information of Olympic games
Introduction_of_Women_Olympics_Sports https://data.world/sports/women-in-the-olympic-games Information on when the women Olympic sports were introduced
womens_participation_in_the_olympic https://data.world/sports/women-in-the-olympic-games Information on participation of women in Olympic sports

About the authors

Ying Wang is a Manager of Software Development Engineer. She has 12 years experience in data analytics and data science. In her data architect life, she helped customer on enterprise data architecture solutions to scale their data analytics in the cloud. Currently, she helps customer to unlock the power of Data with QuickSight from engineering/product by delivering new features.

Ginni Malik is a Data & ML Engineer with AWS Professional Services. She assists customers by architecting enterprise level data lake solutions to scale their data analytics in the cloud. She is a travel enthusiast and likes to run half-marathons.

Niharika Katnapally is a QuickSight Business Intelligence Engineer with AWS Professional Services. She assists customers by developing QuickSight dashboards to help them gain insights into their data and make data driven business decisions.

Maintain visibility over the use of cloud architecture patterns

Post Syndicated from Rostislav Markov original https://aws.amazon.com/blogs/architecture/maintain-visibility-over-the-use-of-cloud-architecture-patterns/

Cloud platform and enterprise architecture teams use architecture patterns to provide guidance for different use cases. Cloud architecture patterns are typically aggregates of multiple Amazon Web Services (AWS) resources, such as Elastic Load Balancing with Amazon Elastic Compute Cloud, or Amazon Relational Database Service with Amazon ElastiCache. In a large organization, cloud platform teams often have limited governance over cloud deployments, and, therefore, lack control or visibility over the actual cloud pattern adoption in their organization.

While having decentralized responsibility for cloud deployments is essential to scale, a lack of visibility or controls leads to inefficiencies, such as proliferation of infrastructure templates, misconfigurations, and insufficient feedback loops to inform cloud platform roadmap.

To address this, we present an integrated approach that allows cloud platform engineers to share and track use of cloud architecture patterns with:

  1. AWS Service Catalog to publish an IT service catalog of codified cloud architecture patterns that are pre-approved for use in the organization.
  2. Amazon QuickSight to track and visualize actual use of service catalog products across the organization.

This solution enables cloud platform teams to maintain visibility into the adoption of cloud architecture patterns in their organization and build a release management process around them.

Publish architectural patterns in your IT service catalog

We use AWS Service Catalog to create portfolios of pre-approved cloud architecture patterns and expose them as self-service to end users. This is accomplished in a shared services AWS account where cloud platform engineers manage the lifecycle of portfolios and publish new products (Figure 1). Cloud platform engineers can publish new versions of products within a portfolio and deprecate older versions, without affecting already-launched resources in end-user AWS accounts. We recommend using organizational sharing to share portfolios with multiple AWS accounts.

Application engineers launch products by referencing the AWS Service Catalog API. Access can be via infrastructure code, like AWS CloudFormation and TerraForm, or an IT service management tool, such as ServiceNow. We recommend using a multi-account setup for application deployments, with an application deployment account hosting the deployment toolchain: in our case, using AWS developer tools.

Although not explicitly depicted, the toolchain can be launched as an AWS Service Catalog product and include pre-populated infrastructure code to bootstrap initial product deployments, as described in the blog post Accelerate deployments on AWS with effective governance.

Launching cloud architecture patterns as AWS Service Catalog products

Figure 1. Launching cloud architecture patterns as AWS Service Catalog products

Track the adoption of cloud architecture patterns

Track the usage of AWS Service Catalog products by analyzing the corresponding AWS CloudTrail logs. The latter can be forwarded to an Amazon EventBridge rule with a filter on the following events: CreateProduct, UpdateProduct, DeleteProduct, ProvisionProduct and TerminateProvisionedProduct.

The logs are generated no matter how you interact with the AWS Service Catalog API, such as through ServiceNow or TerraForm. Once in EventBridge, Amazon Kinesis Data Firehose delivers the events to Amazon Simple Storage Service (Amazon S3) from where QuickSight can access them. Figure 2 depicts the end-to-end flow.

Tracking adoption of AWS Service Catalog products with Amazon QuickSight

Figure 2. Tracking adoption of AWS Service Catalog products with Amazon QuickSight

Depending on your AWS landing zone setup, CloudTrail logs from all relevant AWS accounts and regions need to be forwarded to a central S3 bucket in your shared services account or, otherwise, centralized logging account. Figure 3 provides an overview of this cross-account log aggregation.

Aggregating AWS Service Catalog product logs across AWS accounts

Figure 3. Aggregating AWS Service Catalog product logs across AWS accounts

If your landing zone allows, consider giving permissions to EventBridge in all accounts to write to a central event bus in your shared services AWS account. This avoids having to set up Kinesis Data Firehose delivery streams in all participating AWS accounts and further simplifies the solution (Figure 4).

Aggregating AWS Service Catalog product logs across AWS accounts to a central event bus

Figure 4. Aggregating AWS Service Catalog product logs across AWS accounts to a central event bus

If you are already using an organization trail, you can use Amazon Athena or AWS Lambda to discover the relevant logs in your QuickSight dashboard, without the need to integrate with EventBridge and Kinesis Data Firehose.

Reporting on product adoption can be customized in QuickSight. The S3 bucket storing AWS Service Catalog logs can be defined in QuickSight as datasets, for which you can create an analysis and publish as a dashboard.

In the past, we have reported on the top ten products used in the organization (if relevant, also filtered by product version or time period) and the top accounts in terms of product usage. The following figure offers an example dashboard visualizing product usage by product type and number of times they were provisioned. Note: the counts of provisioned and terminated products differ slightly, as logging was activated after the first products were created and provisioned for demonstration purposes.

Example Amazon QuickSight dashboard tracking AWS Service Catalog product adoption

Figure 5. Example Amazon QuickSight dashboard tracking AWS Service Catalog product adoption


In this blog, we described an integrated approach to track adoption of cloud architecture patterns using AWS Service Catalog and QuickSight. The solution has a number of benefits, including:

  • Building an IT service catalog based on pre-approved architectural patterns
  • Maintaining visibility into the actual use of patterns, including which patterns and versions were deployed in the organizational units’ AWS accounts
  • Compliance with organizational standards, as architectural patterns are codified in the catalog

In our experience, the model may compromise on agility if you enforce a high level of standardization and only allow the use of a few patterns. However, there is the potential for proliferation of products, with many templates differing slightly without a central governance over the catalog. Ideally, cloud platform engineers assume responsibility for the roadmap of service catalog products, with formal intake mechanisms and feedback loops to account for builders’ localization requests.

Amazon migrates financial reporting to Amazon QuickSight

Post Syndicated from Chitradeep Barman original https://aws.amazon.com/blogs/big-data/amazon-migrates-financial-reporting-to-amazon-quicksight/

This is a guest post by from Chitradeep Barman and Yaniv Ackerman  from Amazon Finance Technology (FinTech).

Amazon Finance Technology (FinTech) is responsible for financial reporting on Earth’s largest transaction dataset, as the central organization supporting accounting and tax operations across Amazon. Amazon FinTech’s accounting, tax, and business finance teams close books and file taxes in different regions.

Amazon FinTech had been using a legacy business intelligence (BI) tool for over 10 years, and with its dataset growing at 20% year over year, it was beginning to face operational and performance challenges.

In 2019, Amazon FinTech decided to migrate its data visualization and BI layer to AWS to improve data analysis capabilities, reduce costs, and improve its use of AWS Cloud–native services, which reduces risk and technical complexity. By the end of 2021, Amazon FinTech had migrated to Amazon QuickSight, which organizations use to understand data by asking questions in natural language, exploring through interactive dashboards, or automatically looking for patterns and outliers powered by machine learning (ML).

In this post, we share the challenges and benefits of this migration.

Improving reporting and BI capabilities on AWS

Amazon FinTech’s customers are in accounting, tax, and business finance teams across Amazon Finance and Global Business Services, AWS, and Amazon subsidiaries. It provides these teams with authoritative data to do financial reporting and close Amazon’s books, as well as file taxes in jurisdictions and countries around the world. Amazon FinTech also provides data and tools for analysis and BI.

“Over time, with data growth, we started facing operational and maintenance challenges with the legacy BI tool, resulting in a multifold increase in engineering overhead,” said Chitradeep Barman, a senior technical program manager with Amazon FinTech who drove the technical implementation of the migration to QuickSight.

To improve security, increase scalability, and reduce costs, Amazon FinTech decided to migrate to QuickSight on AWS. This transition aligned with the organization’s goal to rely on native AWS technology and reduce dependency on other third-party tools.

Amazon FinTech was already using Amazon Redshift, which can analyze exabytes of data and run complex analytical queries. It can run and scale analytics on data in seconds without the need to manage the data warehouse infrastructure for its cloud data warehouse. As an AWS-native data visualization and BI tool, QuickSight seamlessly connects with AWS services, including Amazon Redshift. The migration was sizable: after consolidating existing reports, there were about 2,000 financial reports in the legacy tool that were used by over 2,500 users. The reports pulled data from millions of records.

Innovating while migrating

Amazon FinTech migrated complex reports and simultaneously started multiple training sessions. Additional training modules were built to complement existing QuickSight trainings and calibrated to meet the specific needs of Amazon FinTech’s customers.

Amazon FinTech deals with petabytes of data and had built up a repository of 10,000 reports used by 2,500 employees across Amazon. Collaborating with the QuickSight team, they consolidated their reports to reduce redundancy and focus on what their finance customers needed. Amazon FinTech built 450 canned and over 1,800 ad hoc reports in QuickSight, developing a reusable solution with the QuickSight API. As shown in the following figure, on average per month, Amazon FinTech has over 1,300 unique QuickSight users run almost 2,500 unique QuickSight reports, with more than 4,600 total runs.

Amazon FinTech has been able to scale to meet customer requirements using QuickSight.

“AWS services come along with scalability. The whole point of migrating to AWS is that we do not need to think about scaling our infrastructure, and we can focus on the functional part of it,” says Barman.

QuickSight is cloud based, fully managed, and serverless, meaning you don’t have to build your own infrastructure to handle peak usage. It auto scales across tens of thousands of users who work independently and simultaneously.

As of May 2022, more than 2,500 Amazon Finance employees are using QuickSight for financial and operational reporting and to prepare Amazon’s tax statements.

“The advantage of Amazon QuickSight is that it empowers nontechnical users, including accountants and tax and financial analysts. It gives them more capability to run their reporting and build their own analyses,” says Keith Weiss, principal program manager at Amazon FinTech. According to Weiss, “QuickSight has much richer data visualization than competing BI tools.”

QuickSight is constantly innovating for customers, adding new features, and recently released the AI/ML service Amazon QuickSight Q, which lets users ask questions in natural language and receive accurate answers with relevant visualizations to help gain insights from the underlying data. Barman, Weiss, and the rest of the Amazon FinTech team are excited to implement Q in the near future.

By switching to QuickSight, which uses pay-as-you-go pricing, Amazon FinTech saved 40% without sacrificing the security, governance, and compliance requirements their account needed to comply with internal and external auditors. The AWS pricing structure makes QuickSight much more cost-effective than other BI tools on the market.

Overall, Amazon FinTech saw the following benefits:

  • Performance improvements – Latency of consumer-facing reports was reduced by 30%
  • Cost reduction – FinTech reduced licensing, database, and support costs by over 40%, and with the AWS pay-as-you-go model, it’s much more cost-effective to be on QuickSight
  • Controllership – FinTech reports are global, and controlled accessibility to reporting data is a key aspect to ensure only relevant data is visible to specific teams
  • Improved governance – QuickSight APIs to track and promote changes within different environments reduced manual overhead and improved change trackability

Seamless and reliable

At the end of each month, Amazon FinTech teams must close books in 5 days, and since implementing QuickSight for this purpose, Barman says that “reports have run seamlessly, and there have been no critical situations.”

Amazon FinTech’s account on QuickSight is now the source of truth for Amazon’s financial reporting, including tax filings and preparing financial statements. It enables Amazon’s own finance team to close its books and file taxes at the unparalleled scale at which Amazon operates, with all its complexity. Most importantly, despite initial skepticism, according to Weiss, “Our finance users love it.”

Learn more about Amazon QuickSight and get started diving deeper into your data today!

About the authors

Chitradeep Barman is a Sr. Technical Program Manager at Amazon Finance Technology (FinTech). He led the Amazon wide migration of BI reporting from Oracle BI (OBIEE) to AWS QuickSight. Chitradeep started his career as a data engineer and over time grew as a data architect. Before joining Amazon, he lead the design and implementation to launch the BI analytics and reporting platform for Cisco Capital (a fully owned subsidiary of Cisco Systems).

Yaniv Ackerman is a senior software development manager in Fintech org. He has over 20 years of experience building business critical, scalable and high-performance software. Yaniv’s team build data lakes, analytics and automation solutions for financial usage.

New additions to line charts in Amazon QuickSight

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/new-additions-to-line-charts-in-amazon-quicksight/

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to create and deliver insights to everyone in your organization or even with your customers and partners. You can make your data come to life with rich interactive charts and create beautiful dashboards to be shared with thousands of users, either directly within the QuickSight application, or embedded in web apps and portals.

Line charts are ubiquitous to the world of data visualization and are used to visualize change in data over a dimension. They are a great way to analyze trends and patterns where data points are connected with a straight line to visualize the overall trend. In this post, we look at some of the new improvements to our line charts:

  • Support for missing data points for line and area charts
  • Improved performance and increased data limit to 10,000 data points

Missing data points

Line charts in QuickSight expect you to have data for each X axis item. If data is missing for any X axis item, it can lead to broken lines (default behavior) because there is no line drawn connecting the missing data points.

Drawing lines with points of missing data could be misleading because it would represent incorrect data, and there are valid use cases to do so. For example, imagine a scenario of a retail sales report for a given time period where data is recorded during days of operation (Monday through Saturday). In such cases, instead of displaying a broken line chart that skips Sunday, you may want to show a continuous trend by directly connecting Saturday to Monday, hiding the fact that Sunday isn’t operational. Alternatively, you may want to view store traffic for Sunday as 0 instead of displaying a broken line.

Previously, line charts only supported treating missing data for date/time fields. Now, we have added support for categorical data for both line and area charts. The following are the different line treatment options:

  1. Continuous line – Display continuous lines by directly connecting the line to the next available data point in the series
  2. Show as zero – Interpolate the missing values with zero and display a continuous line
  3. Broken line – Retain the default experience to display disjointed lines over missing values

The following diagram illustrates a line chart using each option.

This new feature applies for both categorical and time series data on area charts as well, as shown in the following graphs.

Authors can also configure different data treatments for the left and right Y axis for dual axis charts, as shown in the following example.

Increased data limit for line charts

With the recent update, we have improved line chart performance to support a maximum of 10,000 data points instead of the previous 2,500 data point limit. This also increases the limit for more line series created by the Color by field, which is also bound by the total data limit. For example, if the line chart has 1,000 data points for each series, you could display up to 10 unique colored series.

This update enables use cases where authors want to show a higher number of data points, such as hourly trends or daily trends for a year (365 data points) for multiple groups. This update doesn’t change the default limits of the Color by field (25) and X axis data point limit (100) that exist today to be compatible with existing dashboards and analysis, until authors choose to customize the limits.


In this post, we looked at how to treat missing data for line charts, where instead of viewing broken lines, you can display continuous lines. This helps you customize how you want to visualize overall trends and variations depending on the business context. Additionally, we looked at the new data handling limit for line charts, which supports 10,000 data points—four times more data than before. To learn more refer customizing missing data control.

Try out the new feature and share your feedback and questions in the comments section.

About the author

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.

AWS Week in Review – September 5, 2022

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-week-in-review-september-5-2022/

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!

As a new week begins, let’s quickly look back at the most significant AWS news from the previous seven days.

Last Week’s Launches
Here are the launches that got my attention last week:

AWS announces open-sourced credentials-fetcher to simplify Microsoft AD access from Linux containers. You can find more in the What’s New post.

AWS Step Functions now has 14 new intrinsic functions that help you process data more efficiently and make it easier to perform data processing tasks such as array manipulation, JSON object manipulation, and math functions within your workflows without having to invoke downstream services or add Task states.

AWS SAM CLI esbuild support is now generally available. You can now use esbuild in the SAM CLI build workflow for your JavaScript applications.

Amazon QuickSight launches a new user interface for dataset management that replaces the existing popup dialog modal with a full-page experience, providing a clearer breakdown of dataset management categories.

AWS GameKit adds Unity support. With this release for Unity, you can integrate cloud-based game features into Win64, MacOS, Android, or iOS games from both the Unreal and Unity engines with just a few clicks.

AWS and VMware announce VMware Cloud on AWS integration with Amazon FSx for NetApp ONTAP. Read more in Veliswa‘s blog post.

The AWS Region in the United Arab Emirates (UAE) is now open. More info in Marcia‘s blog post.

View of Abu Dhabi in the United Arab Emirates

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

Other AWS News
A few more blog posts you might have missed:

Easy analytics and cost-optimization with Amazon Redshift Serverless – Four different use cases of Redshift Serverless are discussed in this post.

Building cost-effective AWS Step Functions workflows – In this blog post, Ben explains the difference between Standard and Express Workflows, including costs, migrating from Standard to Express, and some interesting ways of using both together.

How to subscribe to the new Security Hub Announcements topic for Amazon SNS – You can now receive updates about new Security Hub services and features, newly supported standards and controls, and other Security Hub changes.

Deploying AWS Lambda functions using AWS Controllers for Kubernetes (ACK) – With the ACK service controller for AWS Lambda, you can provision and manage Lambda functions with kubectl and custom resources.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
Depending on where you are on this planet, there are many opportunities to meet and learn:

AWS Summits – Come together to connect, collaborate, and learn about AWS. Registration is open for the following in-person AWS Summits: Ottawa (September 8), New Delhi (September 9), Mexico City (September 21–22), Bogotá (October 4), and Singapore (October 6).

AWS Community DaysAWS Community Day events are community-led conferences to share and learn with one another. In September, the AWS community in the US will run events in the Bay Area, California (September 9) and Arlington, Virginia (September 30). In Europe, Community Day events will be held in October. Join us in Amersfoort, Netherlands (October 3), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

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


New row and column interactivity options for tables and pivot tables in Amazon QuickSight – Part 2

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/part-2-new-row-and-column-interactivity-options-for-tables-and-pivot-tables-in-amazon-quicksight/

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to create and deliver insights to everyone in your organization or even with your customers and partners. You can make your data come to life with rich interactive charts and create beautiful dashboards to share with thousands of users, either directly within a QuickSight application or embedded in web apps and portals.

In the previous post in this two-part series, we discussed drag handlers to alter height and width for rows, columns, and table headers. Now, let’s look at some of the new interactivity options for rows and columns for tables and pivot tables.

Hide or show fields for authors

Previously, authors could only hide fields in tables. Now we’re extending this feature to pivot tables as well. Authors can hide rows, columns, and values from either the field wells or from the column or row field headers in pivot tables. For easier identification, hidden fields are indicated with a cross eye icon; you can revert them back to visible using the Show all hidden fields option.

Let’s look at some of the use cases where this could be helpful:

  • Define actions on a pivot table and hide fields to save real estate – Sometimes, you may want to hide fields in a pivot table whose sole purpose is to enable actions, like opening another webpage and pass this hidden field as a parameter.
  • Use hidden fields to define a custom sort order – You can define a custom sort order for your pivot table using hidden fields, for example, defining a specific order for your PNL reports.
  • Display two tables side by side as a single visual – In the following example, we show sales by country, where table 1 displays the last 4 weeks of data and table 2 displays monthly data from the last 4 weeks.
  • Create butterfly tables – Another variation of displaying tables side by side is to create butterfly tables where values are displayed on both sides of the dimension. This is a great way to compare two sets of values. For example, you can compare the current month vs. a full year of data.

Export hidden fields for authors and readers

Not only can authors hide fields, they can also control the ability for readers to export data including the hidden fields or without them. When publishing the analysis, authors have the new option Enable export of hidden fields on supported visuals. When you select this option, readers are able to include hidden fields when exporting their data. The default setting is to keep this disabled and only allow readers to export visible data.

Based on the different scenarios, the following options show up for exporting data to CSV and Excel from tables and pivot tables.


In this post, we looked at the new capability of toggling row, column, and value field visibility on tables and pivot tables. We also discussed the various use cases for hiding fields and the new exporting options associated with field visibility, which can be controlled by authors. To learn more about table and pivot table formatting options, refer to Formatting tables and pivot tables in Amazon QuickSight.

Try out the new feature and share your feedback and questions in the comments section below.

About the author

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.

AWS Week in Review – August 29, 2022

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-week-in-review-august-29-2022/

I’ve just returned from data and machine learning (ML) conferences in Los Angeles and San Francisco, California. It’s been great to chat with customers and developers about the latest technology trends and use cases. This past week has also been packed with launches at AWS.

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

Amazon QuickSight announces fine-grained visual embedding. You can now embed individual visuals from QuickSight dashboards in applications and portals to provide key insights to users where they’re needed most. Check out Donnie’s blog post to learn more, and tune into this week’s The Official AWS Podcast episode.

Sample Web App with a Visual

Sample Web App with a Visual

Amazon SageMaker Automatic Model Tuning is now available in the Europe (Milan), Africa (Cape Town), Asia Pacific (Osaka), and Asia Pacific (Jakarta) Regions. In addition, SageMaker Automatic Model Tuning now reuses SageMaker Training instances to reduce start-up overheads by 20x. In scenarios where you have a large number of hyperparameter evaluations, the reuse of training instances can cumulatively save 2 hours for every 50 sequential evaluations.

Amazon RDS now supports setting up connectivity between your RDS database and EC2 compute instance in one click. Amazon RDS automatically sets up your VPC and related network settings during database creation to enable a secure connection between the EC2 instance and the RDS database.

In addition, Amazon RDS for Oracle now supports managed Oracle Data Guard Switchover and Automated Backups for replicas. With the Oracle Data Guard Switchover feature, you can reverse the roles between the primary database and one of its standby databases (replicas) with no data loss and a brief outage. You can also now create Automated Backups and manual DB snapshots of an RDS for Oracle replica, which reduces the time spent taking backups following a role transition.

Amazon Forecast now supports what-if analyses. Amazon Forecast is a fully managed service that uses ML algorithms to deliver highly accurate time series forecasts.  You can now use what-if analyses to quantify the potential impact of business scenarios on your demand forecasts.

AWS Asia Pacific (Jakarta) Region now supports additional AWS services and EC2 instance types – Amazon SageMaker, AWS Application Migration Service, AWS Glue, Red Hat OpenShift Service on AWS (ROSA), and Amazon EC2 X2idn and X2iedn instances are now available in the Asia Pacific (Jakarta) Region.

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

Other AWS News
Here are some additional news, blog posts, and fun code competitions you may find interesting:

Scaling AI and Machine Learning Workloads with Ray on AWS – This past week, I attended Ray Summit in San Francisco, California, and had great conversations with the community. Check out this blog post to learn more about AWS contributions to the scalability and operational efficiency of Ray on AWS.

Ray on AWS

New AWS Heroes – It’s great to see both new and familiar faces joining the AWS Heroes program, a worldwide initiative that acknowledges individuals who have truly gone above and beyond to share knowledge in technical communities. Get to know them in the blog post!

DFL Bundesliga Data ShootoutDFL Deutsche Fußball Liga launched a code competition, powered by AWS: the Bundesliga Data Shootout. The task: Develop a computer vision model to classify events on the pitch. Join the competition as an individual or in a team and win prizes.

Become an AWS GameDay World Champion – AWS GameDay is an interactive, team-based learning experience designed to put your AWS skills to the test by solving real-world problems in a gamified, risk-free environment. Developers of all skill levels can get in on the action, to compete for worldwide glory, as well as a chance to claim the top prize: an all-expenses-paid trip to AWS re:Invent Las Vegas 2022!

Learn more about the AWS Impact Accelerator for Black Founders from one of the inaugural members of the program in this blog post. The AWS Impact Accelerator is a series of programs designed to help high-potential, pre-seed start-ups led by underrepresented founders succeed.

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

AWS SummitAWS Global Summits – AWS Global Summits are free events that bring the cloud computing community together to connect, collaborate, and learn about AWS.

Registration is open for the following in-person AWS Summits that might be close to you in August and September: Canberra (August 31), Ottawa (September 8), New Delhi (September 9), and Mexico City (September 21–22), Bogotá (October 4), and Singapore (October 6).

AWS Community DayAWS Community DaysAWS Community Day events are community-led conferences that deliver a peer-to-peer learning experience, providing developers with a venue for them to acquire AWS knowledge in their preferred way: from one another.

In September, the AWS community will host events in the Bay Area, California (September 9) and in Arlington, Virginia (September 30). In October, you can join Community Days in Amersfoort, Netherlands (October 3), in Warsaw, Poland (October 14), and in Dresden, Germany (October 19).

That’s all for this week. Check back next Monday for another Week in Review! And maybe I’ll see you at the AWS Community Day here in the Bay Area!


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!

Enable federation to Amazon QuickSight accounts with Ping One

Post Syndicated from Srikanth Baheti original https://aws.amazon.com/blogs/big-data/enable-federation-to-amazon-quicksight-accounts-with-ping-one/

Amazon QuickSight is a scalable, serverless, embeddable, machine learning (ML)-powered business intelligence (BI) service built for the cloud that supports identity federation in both Standard and Enterprise editions. Organizations are working towards centralizing their identity and access strategy across all of their applications, including on-premises, third-party, and applications on AWS. Many organizations use Ping One to control and manage user authentication and authorization centrally. If your organization uses Ping One for cloud applications, you can enable federation to all of your QuickSight accounts without needing to create and manage users in QuickSight. This authorizes users to access QuickSight assets—analyses, dashboards, folders, and datasets—through centrally managed Ping One.

In this post, we go through the steps to configure federated single sign-on (SSO) between a Ping One instance and a QuickSight account. We demonstrate registering an SSO application in Ping One, creating groups, and mapping to an AWS Identity and Access Management (IAM) role that translates to QuickSight user license types (admin, author, and reader). These QuickSight roles represent three different personas supported in QuickSight. Administrators can publish the QuickSight app in Ping One to enable users to perform SSO to QuickSight using their Ping credentials.


To complete this walkthrough, you must have the following prerequisites:

  • A Ping One subscription
  • One or more QuickSight account subscriptions

Solution overview

The walkthrough includes the following steps:

  1. Create groups in Ping One for each of the QuickSight user license types.
  2. Register an AWS application in Ping One.
  3. Add Ping One as your SAML identity provider (IdP) in AWS.
  4. Configure an IAM policy.
  5. Configure an IAM role.
  6. Configure your AWS application in Ping One.
  7. Test the application from Ping One.

Create groups in Ping One for each of the QuickSight roles

To create groups in Ping One, complete the following steps:

  1. Sign in to the Ping One portal using an administrator account.
  2. Under Identities, choose Groups.
  3. Choose the plus sign to add a group.
  4. For Group Name, enter QuickSightReaders.
  5. Choose Save.
  6. Repeat these steps to create the groups QuickSightAdmins and QuickSightAuthors.

Register an AWS application in Ping One

To configure the integration of an AWS application in Ping One, you need to add AWS to your list of managed software as a service (SaaS) apps.

  1. Sign in to the Ping One portal using an administrator account.
  2. Under Connections, choose Application Catalog.
  3. In the search box, enter amazon web services.
  4. Choose Amazon Web Services – AWS from the results to add the application.  BDB-2210-Ping-AWS-APP
  5. For Name, enter Amazon QuickSight.
  6. Choose Next.
    BDB-2210-Ping-AWS-SAVEUnder Map Attributes, there should be four attributes.
  7. Delete the attribute related to SessionDuration.
  8. Choose Username as the value for all the remaining attributes for now.
    We update these values in later steps.
  9. Choose Next.
  10. In the Select Groups section, add the QuickSightAdmins, QuickSightAuthors, and QuickSightReaders groups you created.
  11. Choose Save.
  12. After the application is created, choose the application again and download the federation metadata XML.

You use this in the next step.

Add Ping One as your SAML IdP in AWS

To configure Ping One as your SAML IdP, complete the following steps:

  1. Open a new tab in your browser.
  2. Sign in to the IAM console in your AWS account with admin permissions.
  3. On the IAM console, under Access Management in the navigation pane, choose Identity providers.
  4. Choose Add provider.
  5. For Provider name, enter PingOne.
  6. Choose file to upload the metadata document you downloaded earlier.
  7. Choose Add provider.
  8. In the banner message that appears, choose View provider.
  9. Copy the IdP ARN to use in a later step.

Configure an IAM policy

In this step, you create an IAM policy to map three different roles with permissions in QuickSight.

Use the following steps to set up QuickSightUserCreationPolicy. This policy grants privileges in QuickSight to the federated user based on the assigned groups in Ping One.

  1. On the IAM console, choose Policies.
  2. Choose Create policy.
  3. On the JSON tab, replace the existing text with the following code:
       "Version": "2012-10-17",
        "Statement": [ 
                "Sid": "VisualEditor0", 
                 "Effect": "Allow", 
                 "Action": "quicksight:CreateAdmin", 
                 "Resource": "*", 
                 "Condition": { 
                     "StringEquals": { 
                         "aws:PrincipalTag/user-role": "QuickSightAdmins" 
                 "Sid": "VisualEditor1", 
                 "Effect": "Allow", 
                 "Action": "quicksight:CreateUser", 
                 "Resource": "*", 
                 "Condition": { 
                     "StringEquals": { 
                         "aws:PrincipalTag/user-role": "QuickSightAuthors" 
                 "Sid": "VisualEditor2", 
                 "Effect": "Allow", 
                 "Action": "quicksight:CreateReader", 
                 "Resource": "*", 
                 "Condition": { 
                     "StringEquals": { 
                         "aws:PrincipalTag/user-role": "QuickSightReaders" 
  4. Choose Review policy.
  5. For Name, enter QuickSightUserCreationPolicy.
  6. Choose Create policy.

Configure an IAM role

Next, create the role that Ping One users assume when federating into QuickSight. Use the following steps to set up the federated role:

  1. On the IAM console, choose Roles.
  2. Choose Create role.
  3. For Trusted entity type, select SAML 2.0 federation.
  4. For SAML 2.0-based provider, choose the provider you created earlier (PingOne).
  5. Select Allow programmatic and AWS Management Console access.
  6. For Attribute, choose SAML:aud.
  7. For Value, enter https://signin.aws.amazon.com/saml.
  8. Choose Next.
  9. Under Permissions policies, select the QuickSightUserCreationPolicy IAM policy you created in the previous step.
  10. Choose Next.
  11. For Role name, enter QSPingOneFederationRole.
  12. Choose Create role.
  13. On the IAM console, in the navigation pane, choose Roles.
  14. Choose the QSPingOneFederationRole role you created to open the role’s properties.
  15. Copy the role ARN to use in later steps.
  16. On the Trust relationships tab, under Trusted entities, verify that the IdP you created is listed.
  17. Under Condition in the policy code, verify that SAML:aud with a value of https://signin.aws.amazon.com/saml is present.
  18. Choose Edit trust policy to add an additional condition.
  19. Under Condition, add the following code:
    "StringLike": {
    "aws:RequestTag/user-role": "*"

  20. Under Action, add the following code:


  21. Choose Update policy to save changes.

Configure an AWS application in Ping One

To configure your AWS application, complete the following steps:

  1. Sign in to the Ping One portal using a Ping One administrator account.
  2. Under Connections, choose Application.
  3. Choose the Amazon QuickSight application you created earlier.
  4. On the Profile tab, choose Enable Advanced ConfigurationBDB-2210-Ping-AdvancedConfig
  5. Choose Enable in the pop-up window.
  6. On the Configuration tab, choose the pencil icon to edit the configuration.
  7. Under SIGNING KEY, select Sign Assertion & Response.
  8. Under SLO BINDING, for Assertion Validity Duration In Seconds, enter a duration, such as 900.
  9. For Target Application URL, enter https://quicksight.aws.amazon.com/.
  10. Choose Save.
    BDB-2210-Ping-AdvancedConfig5On the Attribute Mappings tab, you now add or update the attributes as in the following table.
Attribute Name Value
saml_subject Username
https://aws.amazon.com/SAML/Attributes/RoleSessionName Username
https://aws.amazon.com/SAML/Attributes/Role ‘arn:aws:iam::xxxxxxxxxx:role/QSPingOneFederationRole,
https://aws.amazon.com/SAML/Attributes/PrincipalTag:user-role user.memberOfGroupNames[0]
  1. Enter https://aws.amazon.com/SAML/Attributes/PrincipalTag:user-role for the attribute name and use the corresponding value from the table for the expression.
  2. Choose Save.
  3. If you have more than one QuickSight user role (for this post, QuickSightAdmins, QuicksightAuthors, and QuickSightReaders), you can add all the appropriate role names as follows:
    #data.containsAny(user.memberOfGroupNames,{'QuickSightAdmins'})? 'QuickSightAdmins' : 
    #data.containsAny(user.memberOfGroupNames,{'QuickSightAuthorss'}) ? 'QuickSightAuthors' : 
    #data.containsAny(user.memberOfGroupNames,{'QuickSightReaders'}) ?'QuickSightReaders' : null

  4. To edit the role attribute, choose the gear icon next to the role.
  5. Populate the corresponding expression from the table and choose Save.

The format of the expression is the role ARN (copied in the role creation step) followed by the IdP ARN (copied in the IdP creation step) separated by a comma.

Test the application

In this section, you test your Ping One SSO configuration by using a Microsoft application.

  1. In the Ping One portal, under Identities, choose Groups.
  2. Choose a group and choose Add Users Individually.
  3. From the list of users, add the appropriate users to the group by choosing the plus sign.
  4. Choose Save.
  5. To test the connectivity, under Environment, choose Properties, then copy the URL under APPLICATION PORTAL URL.
  6. Browse to the URL in a private browsing window.
  7. Enter your user credentials and choose Sign On.
    Upon a successful sign-in, you’re redirected to the All Applications page with a new application called Amazon QuickSight.
  8. Choose the Amazon QuickSight application to be redirected to the QuickSight console.

Note in the following screenshot that the user name at the top of the page shows as the Ping One federated user.


This post provided step-by-step instructions to configure federated SSO between Ping One and the QuickSight console. We also discussed how to create policies and roles in IAM and map groups in Ping One to IAM roles for secure access to the QuickSight console.

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 Sr. 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.

Raj Jayaraman is a Senior Specialist Solutions Architect for Amazon QuickSight. Raj focuses on helping customers develop sample dashboards, embed analytics and adopt BI design patterns and best practices.

Top Amazon QuickSight features launched in Q2 2022

Post Syndicated from Sindhu Chandra original https://aws.amazon.com/blogs/big-data/top-amazon-quicksight-features-launched-in-q2-2022/

Amazon QuickSight is a serverless, cloud-based business intelligence (BI) service that brings data insights to your teams and end-users through machine learning (ML)-powered dashboards and data visualizations, which can be accessed via QuickSight or embedded in apps and portals that your users access. This post shares the top QuickSight features and updates launched in Q2 2022 categorized into embedding, Amazon QuickSight Q, BI, and admin features.


QuickSight offers a new embedding feature:

  • 1-click public embedding – QuickSight now allows you to embed your dashboards into public applications, wikis, and portals without any coding or development. Once enabled, anyone on the internet can start accessing these embedded dashboards with up-to-date information instantly, without server deployments or infrastructure licensing needed! To learn how to empower your end-users with access to insights, visit Amazon QuickSight 1-click public embedding.

An embedded dashboard example showing metrics for a contact center

QuickSight Q

You can take advantage of the following updates in Q:

  • Programmatic question submission – Q can now accept full questions as input without requiring users to enter them when used in embedded mode. This new feature allows developers to create questions as widgets at appropriate placements on their web applications, making it easy for users to discover the capability to ask questions about data within the current context of their user journey. To learn more, see Amazon QuickSight Embedding SDK.
  • Experience Q before signing up – QuickSight authors can now try, learn, and experience Q before signing up. You can choose from six different sample topics to explore relevant dashboard visualizations and ask questions about data in the context of exploration to fully explore Q’s capability before signing up. Get started with a free trial for QuickSight Q.

User inputs a question in natural language about sales numbers for the month by segment and gets answers on the embedded dashboard.

Business intelligence

QuickSight now offers the following BI features:

  • Table row and column size control – QuickSight now provides the flexibility for both authors and readers to use drag controller to resize rows and columns in a table or pivot table visual. You can adjust both row height and column width. To learn more, see Resizing rows and columns in tables and pivot tables.

Animation showing how to use drag controllers to resize rows and columns in a table

  • Level-aware calculations – QuickSight now supports a suite of functions called level-aware calculations (LAC). The new calculation capability brings flexibility and simplification for users to build advanced calculations and powerful analyses. LAC enables you to specify the level of granularity you want the window functions or aggregate functions to be conducted at. For more information, refer to Using level-aware calculations in Amazon QuickSight.
  • Show or hide fields on pivot tables – QuickSight now provides authors the ability to show or hide any column, row, or value fields from the field well context menu on pivot table visuals. With the show/hide column feature, you can hide unwanted columns that are often used for custom actions for interactivity and provide a better visual presentation. For further details, visit Showing and hiding pivot table columns in Amazon QuickSight.
  • Rolling date functionality – QuickSight now enables authors to set up rolling dates to dynamically generate dashboards for end-users. You can set up rolling rules to fetch a date, such as today, yesterday, or different combinations of (start/end) of (this/previous/next) (year/quarter/month/week/day), and dynamically update the dashboard content To learn how to create date filters, visit Creating date filters in analyses.
  • Bookmarks in dashboards – QuickSight now supports bookmarks in dashboards. Bookmarks allow QuickSight readers to save customized dashboard preferences into a list of bookmarks for easy one-click access to specific views of the dashboard without having to manually make multiple filter and parameter changes every time you want to access your dashboard. For further details, visit Bookmarking views of a dashboard.
  • Custom subtotals at all levels – QuickSight now enables custom subtotals at all levels on pivot tables. QuickSight authors can now customize how subtotals are displayed in a pivot table, with options to display subtotals for last level, all levels, or selected level. This customization is available for both rows and columns. To learn more about custom subtotals, refer to Displaying Totals and Subtotals.


QuickSight offers the following new admin features:

  • Monitor deployments in real time – QuickSight now supports monitoring of QuickSight assets by sending metrics to Amazon CloudWatch. QuickSight developers and administrators can use these metrics to observe and respond to the availability and performance of their QuickSight ecosystem in near-real time. To learn how to monitor your QuickSight deployments in real time, visit Monitor your Amazon QuickSight deployments using the new Amazon CloudWatch integration.
  • Public API for account provisioning – QuickSight now supports APIs for QuickSight account creation. Administrators and developers can automate deployment of QuickSight accounts in their organization at scale. You can now programmatically create accounts with QuickSight Enterprise and Enterprise + Q editions. For more information on account creation, visit CreateAccountSubscription.
  • API for account creation – QuickSight now supports API-based allow listing of domains where QuickSight data visualizations can be embedded. With this new capability, developers can easily scale their embedded analytics offerings across different applications for different customers quickly without any infrastructure setup or management. To learn more, visit Scale Amazon QuickSight embedded analytics with new API-based domain allow listing.


QuickSight serves millions of dashboard views weekly, enabling data-driven decision-making in organizations of all sizes, including customers like the NFL, 3M, Accenture, and more.

To stay up to date on all things new with QuickSight, visit What’s New with Analytics!

About the Author

Sindhu Chandra is a Senior Product Marketing Manager for Amazon QuickSight, AWS’ cloud-native, business intelligence (BI) service that delivers easy-to-understand insights to anyone, wherever they are.

New Powered by QuickSight program helps AWS partners embed interactive analytics in applications to enable data-driven experiences

Post Syndicated from Rich Russell original https://aws.amazon.com/blogs/big-data/new-powered-by-quicksight-program-helps-aws-partners-embed-interactive-analytics-in-applications-to-enable-data-driven-experiences/

Applications today generate enormous amounts of data. This provides an incredible opportunity for independent software vendors (ISV) to deliver business intelligence (BI) offerings as a part of their application, providing visuals, dashboards, and self-service capabilities to customers. These insights are crucial for data-driven decision-making for end-users of these apps, especially if surfaced in the moments that matter and in formats that are easily understood.

Amazon QuickSight provides the keys for ISVs in the software as a service (SaaS) space to unlock the potential of your data by offering the technology to deliver out-of-the-box analytics, driving stickier experiences for customers that lead to higher revenues. To support our partners further, we are excited to announce the Powered by QuickSight program, which includes technical build, go-to-market (GTM), and sales support for ISVs to embed and monetize their data.

About the Powered by QuickSight program

Building upon the Powered by Amazon Redshift program announced in July 2022, the Powered by QuickSight program extends the available benefits and funding to partners using more of the AWS Data and Analytics stack to include Amazon’s BI offering QuickSight. ISVs can embed the powerful analytics and BI capabilities of QuickSight, such as visuals, dashboards, natural language querying, and authoring insights, into applications to enable data-driven experiences for millions of users, joining customers such as the NFL, 3M, Bolt, Tealium, and Extensiv, whose apps are powered by QuickSight.

The benefits available from the Powered by QuickSight program include:

  • Private strategy and development support – Connect with AWS industry experts in one-on-one sessions focused on a customized evaluation of revenue-generating offerings. Analyze your offering relevance by industry, location, and size.
  • Rapid proof of concept – Take advantage of QuickSight product managers, solution architects, specialists, and AWS Data Labs to develop a minimum viable product (MVP) for your selected use case. Where appropriate, AWS will lead a Data-Driven Everything (D2E) workshop to quickly identify proofs of concept for priority use cases.
  • Product technical assistance – Have access to product engineering, architecture, specialists, and support teams for integration, product development, best practices, roadmaps, and post-sales support.
  • Joint Powered by QuickSight launch package – Take advantage of joint PR, AWS website listing, and featured blog posts and social media. You also have access to a joint webinar deck, solution brief, case study, landing page, and social media banners.

Additional benefits:

  • Listed internally as a qualified partner to engage with field teams in respective regions
  • Annual planning session to identify AWS Marketplace solutions to be listed
  • Annual funding for AWS Partner Marketing funds to support development of lead generation or thought leadership highlighting your solution (incremental to existing Marketing Development Funds)

“AWS has been a natural home for builders, with the widest range of services available to support applications across use cases. We see an increasing trend of builders needing more powerful analytical capabilities within their applications,” says Tracy Daugherty, General Manager of Amazon QuickSight.

“With QuickSight, we have a powerful set of analytics capabilities that are easy to embed in applications. The Powered by QuickSight program combines these capabilities with training, enablement, and go-to-market support activities to help developers and ISVs provide differentiated data-driven experiences that scale to all of their users.”

ISVs that are registered with the AWS Partner Network (APN) and interested in joining the program can complete the registration form for an exploratory session to deep dive into their data monetization strategy.

To get started, register now. Once registered, a member of the AWS team will contact you with next steps.

About QuickSight

With QuickSight, ISVs can easily embed interactive visuals and dashboards, natural language querying (NLQ), or the complete BI-authoring experience seamlessly in your applications, enabling end-users to gain insights right when and where they matter. Additionally, QuickSight capabilities such as custom branded email reports and advanced machine learning (ML) capabilities (such as forecasting and anomaly detection) enable you to differentiate your applications and enhance your end-user experience, without the heavy lifting involved in building such capabilities from scratch.

Application users can now dive deep into data insights without leaving their application, with interactive dashboards that offer drill-down, interactive filtering, and export options. Embedded visuals and dashboards are refreshed automatically when the data is updated, with options of direct query to data sources such as (but not limited to) Amazon Redshift or Snowflake, or fast performance with the QuickSight SPICE in-memory data store. You can now quickly bring value to your applications by using the Powered by QuickSight program, creating data-driven experiences for users, and using joint GTM channels to grow their business.

The following screenshots are just a few examples of the interactive dashboards you can create with QuickSight.

Success stories from ISV partners in the program

ISV partners in the Powered by QuickSight program have the following to say.


“Amazon QuickSight enabled us to delight our customers by replacing a non-interactive native dashboard with an engaging QuickSight embedded dashboard. We quickly scaled to 4,600 daily viewers with strong engagement and extremely positive feedback. Our user sentiment score went from 14% on the old dashboard to 83% on the QuickSight dashboard, over the same period,” says Brian Sevy, Senior Product Manager at Extensiv.

“Extensiv has seen great value in the technical support provided through the Powered by QuickSight program. We look forward to leveraging the GTM benefits offered in the Powered by QuickSight program as we as we continue to expand our solution offerings.”


“Visibility and transparency are critical to solving modern supply chain challenges. Convoy collects over 1,000 data points on every shipment; however, the complexity of this data can be difficult to curate and present in a way that freight shippers can act on,” says Dorothy Li, CTO of Convoy.

“QuickSight allowed our team to build an interactive shipment and facility insights dashboard in weeks to visualize more than 40 key data points—all on demand and in real time. This has helped us strengthen our partnership with shippers by giving them visibility into opportunities that save both time and costs.”


“Companies struggle with trying to organize and visualize their HR data in meaningful ways. Trakstar equips HR leaders who now have a ‘seat at the table’ with board-ready reporting and expertly designed people analytics dashboards embedded directly into the product experience that help guide future business decisions. By partnering with AWS and leveraging QuickSight, we can focus on what HR leaders need next and trust QuickSight to deliver interactive, ML-driven visualizations with high performance at scale,” says Brian Kasen, Dir. Business Intelligence at Trakstar.

“Trakstar is excited to be Powered by QuickSight and empower business and HR leaders with state-of-the-art capabilities to make smarter decisions to attract, retain, and engage their workforce.”


“At ConexEd, we strive to make students successful by providing tools and accessibility to school staff. School administrators usually struggle to get meaningful data that help them make informed decisions to further support students. To close this gap, we had to invest more than half of our development team’s time to create initial reports and keep building on these as we launch new features instead of focusing on building critical features for the business,” says Michael Gorham, co-founder and CTO of ConexEd.

“Now with embedded dashboards powered by QuickSight, not only can ConexEd’s development team focus all its energies on creating competitive features, but also the reporting and data visualization are now features our customers can control and customize. QuickSight features such as drill-down filtering, predictive forecasting, and aggregation insights have given us the competitive edge that our customers expect from a modern, cloud-based solution.”


“Embedding Amazon QuickSight dashboards into our applications was a no-brainer to meet our goal. With QuickSight, we are able to build data rich dashboards easily and embed these in our applications quickly. Different personas from the same enterprise get insights that matter to them, cutting out any noise,” says Tyler Warden, SVP of Product and Engineering at Syniti.

“Partnering with QuickSight has made it easy for us to bring rich insights to our enterprise customers and help us grow.”

Next steps

To learn more about the Powered by QuickSight program, visit QuickSight. If you’re interested in learning more about how your company can use QuickSight, you can meet us in person at a Powered by QuickSight roadshow at one of four locations: New York on September 7, Chicago on September 13, San Francisco on September 15 , and London on October 4.

About the authors

Rich Russell is the global Head for QuickSight GTM at Amazon Web Services (AWS) and senior global sales and partner leader with extensive experience in establishing and building revenue generating Go-To-Market (GTM) strategies through sales, strategic partnerships, ISVs and global alliance programs. Specializing in helping companies drive growth through leveraging sales and partners while focusing on what’s most important, the customer. Rich resides in Utah, with his wife and 5 boys where they love to spend time in the great outdoors.

Kareem Syed-Mohammed is a Product Manager at Amazon QuickSight. He focuses on embedded analytics, APIs, and developer experience. Prior to QuickSight he has been with AWS Marketplace and Amazon retail as a PM. Kareem started his career as a developer and then PM for call center technologies, Local Expert and Ads for Expedia. He worked as a consultant with McKinsey and Company for a short while.

New — Fine-Grained Visual Embedding Powered by Amazon QuickSight

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/new-fine-grained-visual-embedding-powered-by-amazon-quicksight/

Today, we are announcing a new feature, Fine-Grained Visual Embedding Powered by Amazon QuickSight. With this feature, individual visualizations from Amazon QuickSight dashboards can now be embedded in high-traffic webpages and applications. Additionally, this feature enables you to provide rich insights for your end-users where they need them the most, without server or software setup or infrastructure management.

This is a quick preview of this new feature:

Quick Preview of Fine-Grained Visual Embedding Powered by Amazon QuickSight

Quick Preview: Fine-Grained Visual Embedding Powered by Amazon QuickSight

New Feature: Fine-Grained Visual Embedding

Amazon QuickSight is a cloud-based embeddable and ML-powered business intelligence (BI) service that delivers interactive data visualizations, analysis, and reporting to enable data-driven decision-making within the organization and with the end user, without servers to manage.

Amazon QuickSight supports embedded analytics, a feature that enables you to incorporate branded analytics into internal portals or public sites. Customers can easily embed interactive dashboards, natural language querying (NLQ), or the complete BI-authoring experience seamlessly in their applications. This provides convenience for your end users to simplify the process of data-informed decisions.

Our customers want to be able to embed visuals from various dashboards into their applications and websites in order to bring forth deeply integrated data-driven experiences to enhance end user experiences. Previously, customers needed to build, scale, and maintain generation layer and charting libraries to embed individual visualizations.

With Fine-Grained Visual Embedding Powered by Amazon QuickSight, developers and ISVs now have the ability to embed any visuals from dashboards into their applications using APIs. As for enterprises, they can embed visuals into their internal sites using 1-Click Embedding. For end-users, Fine-Grained Visual Embedding provides a seamless and integrated experience to access a variety of key data visuals to get insights.

Here’s an example view where we can embed a visual using this feature in a sample web application page:

Sample Web App with a Visual

Sample Web App with a Visual

The embedded visuals are automatically updated when the source data changes or when the visual is updated. Embedded visuals scale automatically without the need to manage servers from your end and are optimized for high performance on high-traffic pages.

Get Started with Fine-Grained Visual Embedding

There are two ways to use Fine-Grained Visual Embedding, with 1-Click Embedding or using QuickSight APIs to generate the embed URL. The 1-Click Embedding feature makes it easy for nontechnical users to generate embed code that can be inserted directly into internal portals or public sites. Using APIs, ISVs and developers can embed rich visuals in their applications. Furthermore, with row-level security, data access is secured enabling users to access only their data.

To start using this feature, let’s turn to the Amazon QuickSight dashboard. Here, I already have a dashboard using a dataset that you can follow from the Create an Amazon QuickSight dashboard using sample data documentation.

Amazon QuickSight Dashboard Using Sample Data

Amazon QuickSight Dashboard Using Sample Data

Using 1-Click Embedding to Generate Embed Code

Amazon QuickSight supports 1-Click Embedding—a feature that allows you to get the embed code without any development efforts. There are two types of 1-Click Embedding: 1) 1-Click Enterprise Embedding and 2) 1-Click Public Embedding. With enterprise embedding, it allows you to enable access to the dashboard with registered users in your account. In public embedding, you can enable access to the dashboards for anyone.

To get the embed code via 1-Click Embedding, you can select the visual you want to embed, then select Menu Options and choose Embed visual.

Select "Embed visual" from Menu Options

Select Embed visual from Menu Options

Once you select Embed visual, you will get a new menu on the right side, which contains the details of the visual you selected.

Copy "Embed code"

Copy the Embed code

The Embed code section contains iframe code that you can insert into your application, portal, or website. Domains hosting these embedded visuals must be on an allow list, which you can learn more about on the Allow listing static domains page. This is a sample display of how the embed code is rendered:

Sample Display of Fine-Grained Visual Embedding Powered by Amazon QuickSight

Sample Display of Fine-Grained Visual Embedding Powered by Amazon QuickSight

When there is a change in the visual source within Amazon QuickSight, it will also be reflected within the web app or app where you embed your visuals. In addition, embedded visuals from QuickSight will automatically scale as traffic on the website grows.

From a customer’s perspective, 1-Click Embedding will help customers provide key data visuals from various dashboards in Amazon QuickSight for end users anywhere on their websites without requiring technical skills.

Programmatically Generate Embed URL

In addition to the 1-Click Embedding, you can also perform visual embedding through the API. To perform visual embedding through the API, you can use AWS CLI or SDK to call the API GenerateEmbedUrlForAnonymousUser or GenerateEmbedUrlForRegisteredUser.

You can use the GenerateEmbedUrlForAnonymousUser API to embed visuals in your applications for your users without provisioning them in Amazon QuickSight.

You can also use GenerateEmbedUrlForRegisteredUser API to embed visuals in your application for your users that are provisioned in Amazon QuickSight.

The API works by passing the ExperienceConfiguration parameter in DashboardVisual with the properties below:


Then, to get the IDs for DashboardSheet, and Visual, you can find the value of these properties under IDs for Developers menu section for the visual you selected.

IDs for Developers

IDs for Developers

Using CLI to Generate Embed URL

After collecting all the required IDs, we can pass them as parameters. Here’s an example API command to generate an embed URL:

aws quicksight generate-embed-url-for-anonymous-user \  
    --aws-account-id <ACCOUNT_ID> \  
    --session-lifetime-in-minutes 15 \          
    --authorized-resource-arns “<DASHBOARD_ARN>”           
    --namespace default           
    --experience-configuration '{"DashboardVisual": \
            "InitialDashboardVisualId": \
                    "DashboardId”:”<DASHBOARD_ID>”,  \
                    "SheetId”:”<SHEET_ID>”,  \
                    "VisualId”:”<VISUAL_ID”  \

If the request is successful, you will get the following response. You can then use the EmbedUrl property within your web or application.

    "Status": 200,  
    "EmbedUrl": “<EMBED_URL>”,  
    "RequestId": “<REQUEST_ID>”,  
    "AnonymousUserArn": “<ARN>”  

Using SDK to Generate Embed URL

In addition to the AWS CLI, generating embed URLs can also be done using the AWS SDK. Here’s an example in Python:

response = client.generate_embed_url_for_anonymous_user(  
        'DashboardVisual': {  
            'InitialDashboardVisualId': {  

With API, you have the flexibility to configure allowed domains at runtime. From the example above, you can pass your domains in AllowedDomains property.

When the request is successful, the API will return a successful response, along with a URL from Visual Embedding that can be inserted into external web apps. Example response as below:

    "Status": 200,  
    "RequestId": "<REQUEST_ID>”

Using the API approach gives developers the flexibility to programmatically generate embed URLs. Developers can specify the access for visuals for nonregistered and registered users in Amazon QuickSight.


To see Fine-Grained Visual Embedding Powered by Amazon QuickSight in action, have a look at this demo:

Pricing and Availability

You can use this new feature, Fine-Grained Visual Embedding in Amazon QuickSight Enterprise Edition, in all supported Regions. For more detailed information, please visit the documentation page.

Happy building,

— Donnie

New row and column interactivity options for tables and pivot tables in Amazon QuickSight – Part 1

Post Syndicated from Bhupinder Chadha original https://aws.amazon.com/blogs/big-data/part-1-new-row-and-column-interactivity-options-for-tables-and-pivot-tables-in-amazon-quicksight/

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to create and deliver insights to everyone in your organization. You can make your data come to life with rich interactive charts and create beautiful dashboards to share with thousands of users, either directly within a QuickSight application, or embedded in web apps and portals.

In 2021, as part of table and pivot table customization, we launched the ability for authors to resize row height in tables and pivot tables. Now, we are extending this capability to readers, along with other row and column interactions, such as altering column width and header height for both tables and pivot tables using a drag handler, for consistency between row and column interactions and an improved user experience. Apart from the format pane settings, authors can make quick changes to column, row, and header width for both parent and child nodes by simply dragging the cell, column, or row to the desired setting using the drag handler.

The following are the different interactions that both readers and authors can perform, some of which were already available for tables.

Modify row height

You can modify row height using the drag handler for cells, row headers, or column headers.

The following screenshot illustrates how to alter the row height by dragging from any cell in the table or pivot table.

For row headers, you can only resize row height by dragging the last element (child node), which gets aggregated to define the row height for the parent node.

You can resize the height of the column headers at any level such that you can have different heights at each level for better aesthetics.

Modify column width

You can also use the drag handler to modify column width for row headers, column headers, or cells.

You can alter column width for any field assigned to rows, as shown in the following screenshot.

You can alter column width for column dimension members both from the parent or leaf node in the case of hierarchy, or column field or values headers in the absence of a column hierarchy.

You now have complete flexibility to resize column width from cells. Depending on which cell, the corresponding column width is adjusted.


A few things to note about this new feature:

  • Drag handlers are available for both web and embedded use cases
  • Row height is set in common for all rows and not specific to a particular row


With the introduction of a drag handler, authors and readers can now quickly alter column width, row height, and header height for tables and pivot tables. This provides a consistent behavior and improved user interaction for both the personas and visuals. To learn more about table and pivot table formatting options, refer to Formatting tables and pivot tables in Amazon QuickSight.

Try out the new feature and share your feedback and questions in the comments section.

About the author

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.

Forwood Safety uses Amazon QuickSight Q to extend life-saving safety analytics to larger audiences

Post Syndicated from Faye Crompton original https://aws.amazon.com/blogs/big-data/forwood-safety-uses-amazon-quicksight-q-to-extend-life-saving-safety-analytics-to-larger-audiences/

This is a guest post by Faye Crompton from Forwood Safety. Forwood provides fatality prevention solutions to organizations across the globe.

At Forwood Safety, we have a laser focus on saving lives. Our solutions, which provide full content and proven methodology via verification tools and analytical capabilities, have one purpose: eliminating fatalities in the workplace. We recently realized an ambition to provide interactive, dynamic data visualization tools that enable our end users to access safety data in the field, regardless of their experience with analytics and data reporting.

In this post, I’ll talk about how Amazon QuickSight Q solved these challenges by giving users fast data insights through natural language querying capabilities.

Driving data insights with QuickSight

Forwood’s Critical Risk Management (CRM) solution provides organizations with globally benchmarked and comprehensive critical control checklists and verification controls that are proven to prevent fatalities in the workplace. CRM protects frontline workers from serious harm by helping change the culture of risk management for companies. In addition, our Forwood Analytical Self-Service Tool (FAST) enables our customers to use self-service reporting to get updated dashboards that display key safety and fatality prevention metrics.

For several years, we used AWS QuickSight to provide data visualization for our CRM and FAST reporting products, with great success. Most of our technology stack was already based on AWS, so we knew QuickSight would be easy to integrate. QuickSight is agnostic in terms of data sources and types, and it’s a very flexible tool. It’s also an open data technology, so it can accept most of the data sources that we throw at it. Most importantly, it ties in seamlessly with our own architecture and data pipelines in a way that our previous BI tools couldn’t. After we implemented QuickSight, we started using it to power both CRM and FAST, visualizing risk data and serving it back to our customers.

Using QuickSight Q to help site supervisors get answers quickly

Furthering our focus on innovation and usability; we identified a common challenge that we believed QuickSight could solve through our FAST application on behalf of our clients — we needed to make risk data more accessible for those of our clients who aren’t data analysts. We recognize that not everyone is an analyst. We also have mining industry customers who are not frequently accessing our applications via desktop. For example, mining site Supervisors and Operators working deep underground typically have access only via their mobile devices. For these users, it’s easier for them to ask the questions relevant to their specific use cases as needed at point of use, rather than filter and search through a dashboard to find the answers ahead of time.

QuickSight Q was the perfect solution to this challenge. QuickSight Q is a feature within QuickSight that uses machine learning to understand questions and how they relate to business data. The feature provides data insights and visualizations within seconds. With this capability, users can simply type in questions in natural language to access data insights about risk and compliance. Mining site workers, for example, can ask if the site is safe or if the right verification processes are in place. Health and safety teams and mining site supervisors can ask questions such as “Which sites should I verify today?” or “Which risk will be highest next week?” and receive a chart with the relevant data.

Making data more accessible to everyone

QuickSight Q gives our on-site customers near-real-time risk and compliance data from their mobile devices in a way they couldn’t before. With QuickSight Q, we can give our FAST users the opportunity to quickly visualize any fatality risks at their sites based on updated fatality prevention data. All users, not just analysts, can identify worksites that have a higher fatality risk because the data can show trends in non-compliance with safety standards. Our clients no longer have to look in a dashboard for the answers to their questions; those looking at a dashboard can go beyond the dashboard and ask deeper questions.

QuickSight Q solved one of our main BI challenges: how to make risk data more accessible to more people without extensive user training and technical understanding. Soon, we hope to use QuickSight Q as part of a multidimensional predictive dataset using deep learning models to deliver even more insights to our customers.

We look forward to extending our use of QuickSight. When we first started using it, it was strictly for analytics on our existing data. More recently, we started using API deployments for QuickSight. We have many different clients, and we use the API feature to maintain master versions of all 30+ standard reports, and then deploy those dashboards to as many clients as we need to via code. Previously, we saw QuickSight as a function of our analytics products; now we see it as a powerful and flexible toolkit of analytics features that our developers can build with.

Additionally, we look forward to relying on QuickSight Q to bring life-saving safety analytics to more people. QuickSight Q bridges the gap between the data a company has and the decisions that company needs to make, and that’s very powerful for our clients. Forwood Safety is driven to eradicate workplace fatalities, and by getting data to more people and making it easy to access, we can make our solutions more effective, saving more lives.

About the author

Faye Crompton is Head of Analytics, Safety Applications and Computer Vision at Forwood Safety. She leads work on analytics and safety products that reduce fatality risk in mining and other high-risk industries.

Schedule email reports and configure threshold based-email alerts using Amazon QuickSight

Post Syndicated from Niyati Upadhyay original https://aws.amazon.com/blogs/big-data/schedule-email-reports-and-configure-threshold-based-email-alerts-using-amazon-quicksight/

Amazon QuickSight is a cloud-scale business intelligence (BI) service that you can use to deliver easy-to-understand insights to the people you work with, wherever they are.

You can build dashboards using combinations of data in the cloud, on premises, in other software as a service (SaaS) apps, and in flat files. Although users can always view and interact with dashboards on-demand in their browser or our native mobile apps, sometimes users prefer to receive notifications and report snapshots on a scheduled basis or when a certain value surpasses a user-defined threshold.

In this post, we walk you through the features and process of scheduling email reports and configuring threshold-based alerts.

Overview of solution

In QuickSight Enterprise edition, you can email a report from each dashboard on a scheduled basis or based on a threshold set for KPI and gauge visuals. Scheduled reports include settings for when to send them, the contents to include, and who receives the email.

Scheduled email reports work with row-level security so that each user receives reports containing only data that is relevant to them. Alert reports include threshold value, alert condition, and the receiver’s email. To set up or change the report sent from a dashboard, make sure that you’re an owner or co-owner of the dashboard.

To receive email reports, the users or group members must be part of your QuickSight account. They must have completed the sign-up process to activate their subscription as QuickSight readers, authors, or admins.

In this post, we configure the email settings for a QuickSight dashboard for users and construct a custom email for each user or group based on their data permissions.

The solution includes the following high-level steps:

  1. Set up scheduled email alerts for your existing reports.
  2. Set up threshold-based email alerts for the existing reports.
  3. View alert history.
  4. Set up email alerts if the dataset refresh fails.


For this walkthrough, you should have the following prerequisites:

Set up scheduled email alerts

To configure scheduled emails for your reports, complete the following steps:

  1. On the QuickSight console, choose Dashboard in the navigation pane.
  2. Open a dashboard.
  3. On the Share menu, choose Email report.

  1. For Schedule, choose the frequency for the report. For this post, we choose Repeat once a week.

  1. For Send first report on, choose a date and time.
  2. For Time zone, choose the time zone.
  3. For Report title, enter a custom title for the report.
  4. For (Optional) E-mail subject line, leave it blank to use the report title or enter a custom subject line.
  5. For (Optional) E-mail body text, leave it blank or enter a custom message to display at the beginning of the email.

  1. Select Include PDF attachment to attach a PDF snapshot of the items visible on the first sheet of the dashboard.
  2. For Optimize report for, choose a default layout option for new users.
  3. Under Recipients, select specific recipients from the list (recommended), or select Send email report to all users with access to dashboard.
  4. To send a sample of the report before you save changes, choose Send test report.

This option is displayed next to the user name of the dashboard owner.

  1. To view a list of the datasets used by this report, choose View dataset list.

  1. Choose Save report or Update report.

A “Report scheduled” message briefly appears to confirm your entries.

  1. To immediately send a report, choose Update & send a report now.

The report is sent immediately, even if your schedule’s start date is in the future.

The following screenshot shows the PDF report visible to the user AlejandroRosalez. They have access to data where the state is California or Texas, and the city is Los Angeles or Fort Worth.

The following screenshot shows the report visible to the user SaanviSarkar. They can see data for any city, but only if the state is Texas.

The following screenshot shows the report visible to the user MarthaRivera. Martha can see the data for any city or state.

The following screenshot shows that no data is visible to the workshop user, which isn’t present in the permissions.csv file.

Set up threshold-based email alerts

To create an alert based on a threshold, complete the following steps:

  1. On the QuickSight dashboard, choose Dashboards, and navigate to the dashboard that you want.

For more information about viewing dashboards as a dashboard subscriber in QuickSight, see Exploring Dashboards.

  1. In the dashboard, select the KPI or gauge visual that you want to create an alert for.
  2. On the options menu at upper-right on the visual, choose the Create alert icon.

  1. For Alert name, enter a name for the alert.
  2. For Alert value, choose a value that you want to set the threshold for.

The values that are available for this option are based on the values the dashboard author sets in the visual. For example, let’s say you have a KPI visual that shows a percent difference between two dates. Given that, you see two alert value options: percent difference and actual.

If the visual has only one value, you can’t change this option. It’s the current value and is displayed here so that you can use it as a reference while you choose a threshold. For example, if you’re setting an alert on actual, this value shows you what the current actual cost is (for example, $5). With this reference value, you can make more informed decisions while setting your threshold.

  1. For Condition, choose a condition for the threshold.
    • Is above – The alert triggers if the alert value goes above the threshold.
    • Is below – The alert triggers if the alert value goes below the threshold.
    • Is equal to – The alert triggers if the alert value is equal to the threshold.
  1. For Threshold, enter a value to prompt the alert.
  2. Choose Save.

A message appears indicating that the alert has been saved. If your data crosses the threshold you set, you get a notification by email at the address that’s associated with your QuickSight account.

View alert history

To view the history of when an alert was triggered, complete the following steps:

  1. On the QuickSight console, choose Dashboards, and navigate to the dashboard that you want to view alert history for.
  2. Choose Alerts.
  3. In the Manage dashboard alerts section, find the alert that you want to view the history for, and expand History under the alert name.

Set up email alerts if the dataset refresh fails.

To configure emails alerts, if the your dataset refresh fails, complete the following steps:

  1. On the QuickSight console, choose Dataset, and choose the dataset that you want to set an alert for.
  2. Select Email owners when a refresh fails.
  3. Close the window.

Clean up

To avoid incurring future charges, delete the QuickSight users and Enterprise account.


This post showed how to set up email scheduling of QuickSight dashboards for users and groups, as well as how end-users (readers) can configure alerts to be sent to them when a value surpasses or drops below a given threshold.

You can send dashboard snapshots as emails to groups of readers, and each reader receives custom reports as per the security configurations set on the dataset. For more information, see sending reports by email and threshold alerts.

You can try this solution for your own use cases. If you have comments or feedback, please leave them in the comments.

About the Author

Niyati Upadhyay is a Solutions Architect at AWS. She joined AWS in 2019 and specializes in building and supporting big data solutions that help customers analyze and get value out of their data.

Scale Amazon QuickSight embedded analytics with new API-based domain allow listing

Post Syndicated from Vetri Natarajan original https://aws.amazon.com/blogs/big-data/scale-amazon-quicksight-embedded-analytics-with-new-api-based-domain-allow-listing/

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, either within QuickSight itself or embedded in apps and portals.

QuickSight Enterprise Edition recently introduced the ability to dynamically allow list the domains where QuickSight content can be embedded. This allows developers to quickly embed dashboards across multiple apps, portals, or websites, without needing to make this change on the QuickSight administrative console every time. Together with QuickSight’s existing dashboard theming and templating capabilities, this new feature allows developers to rapidly develop and deploy QuickSight dashboards and visualizations for a variety of use cases across various applications with ease. Let’s take a look at how this works.

Solution overview

To embed a QuickSight dashboard using APIs, you can use one of the following embedding APIs:

In these APIs, you can now pass the domain where you want to embed your dashboard using the new parameter AllowedDomains:

POST /accounts/AwsAccountId/embed-url/registered-user HTTP/1.1
Content-type: application/json
   "AllowedDomains": [ "string" ],
   "ExperienceConfiguration": { 
      "Dashboard": { 
         "InitialDashboardId": "string"
      "QSearchBar": { 
         "InitialTopicId": "string"
      "QuickSightConsole": { 
         "InitialPath": "string"
   "SessionLifetimeInMinutes": number,
   "UserArn": "string"

You can add up to three domains in a single API call as an array list. All the domains need to be SSL enabled (using HTTPS protocol). If you want to test out the embedded dashboard on your local machine, you can allow list http://localhost via the AllowedDomains parameter. For example, if you want to embed a dashboard in your SaaS application called https://myorders.simplelogistics.com, you set AllowedDomains to be https://myorders.simplelogistics.com in the API call. You can also enable sub domains by passing *, for example, https://*.myorders.simplelogistics.com.

AllowedDomains is an optional parameter. If you don’t specify any domains via this parameter, you can still use the domains allow listed via the QuickSight console. But if you specify domains via this parameter, then the embedding URL returned as part of the API call is only embeddable in these domains (even if you have a list of static domains entered on the QuickSight console).

Prior to this capability, the Content-Service-Policy in the request header listed all the domains allow listed in QuickSight console. Now when allow listing the domains using the API, the Content-Service-Policy only shows the domains that are allow listed in the API call.

With this new capability, ISVs that have different applications for different customers can allow list specific domains at runtime, enabling them to scale easily for different customers and to hundreds of thousands of end-users.

As an added security, the AWS Identity and Access Management (IAM) admin of your QuickSight account can restrict the domains that can be allow listed. This can be done when your IAM admin sets up permissions for your application or server. As part of this step, you can specify the list of domains that can be allow listed via the embedding APIs. For example, let’s assume you want your developers to only allow list the following domains:

You can set these domains in the quicksight:AllowedEmbeddingDomain of the permissions setup. The following code is a sample for the GenerateEmbedURLForAnonymousUser API:

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": "arn:partition:quicksight:region:accountId:user/namespace/userName",
            "Condition": {
                "ForAllValues:StringEquals": {
                    "quicksight:AllowedEmbeddingDomains": [

Sample use case

In this example use case, Travel Analytics is a software as a service (SaaS) provider with travel-related solutions for various travel agencies. They have a SaaS application for these agencies to track different metrics on how their business is performing. Because Travel Analytics is scaling their business, they have different sites for different travel agencies. With the newly launched domain allow listing with APIs, they’re able to scale with ease. They allow list the specific domains, depending on the customer, via the API when generating the embedding URL.

The following code shows their sample GenerateEmbedURLForAnonymousUser API call with the domain added to the request:

The returned URL can only be embedded in the domain that was allow listed as part of the preceding request. The following is a screenshot of the embedded dashboard in this domain.

The CSP header has only the specific allow listed domain via the API when the dashboard is embedded.


Runtime domain allow listing using embedding APIs enables developers to scale their embedded offerings with QuickSight dashboards, visuals, QuickSight Q (natural language querying), or authoring experience across different domains for their different customers easily. All of this is done without any infrastructure setup or management, while scaling to millions of users. For more information, refer to Amazon QuickSight Embedded Analytics and What’s New in the Amazon QuickSight User Guide.

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.

Kareem Syed-Mohammed is a Product Manager at Amazon QuickSight. He focuses on embedded analytics, APIs, and developer experience. Prior to QuickSight he has been with AWS Marketplace and Amazon retail as a PM. Kareem started his career as a developer and then PM for call center technologies, Local Expert and Ads for Expedia. He worked as a consultant with McKinsey and Company for a short while.

Introducing Embedded Analytics Data Lab to accelerate integration of Amazon QuickSight analytics into applications

Post Syndicated from Romit Girdhar original https://aws.amazon.com/blogs/big-data/introducing-embedded-analytics-data-lab-to-accelerate-integration-of-amazon-quicksight-analytics-into-applications/

We are excited to announce Embedded Analytics Data Lab (EADL), a no-cost collaborative engagement that helps engineering and development teams cut down time required to launch applications with embedded analytics from Amazon QuickSight in production by providing hands-on guidance and architectural best practices.

Embedding rich analytics such as interactive visuals and dashboards directly into applications allows developers to create differentiated, analytics-driven experiences that enables end-users to make more informed decisions. QuickSight is a cloud-native, serverless business intelligence (BI) service that allows developers from enterprises and independent software vendors (ISVs) to incorporate powerful BI capabilities such as interactive visualizations, dashboards, and machine learning (ML)-powered natural language query (NLQ) using Amazon QuickSight Q into their applications and web portals, delivering insights to end-users where they are.

AWS Data Lab is an AWS offering that offers accelerated, joint engineering engagements between customers and AWS technical resources to create tangible deliverables that accelerate data, analytics, AI/ML, serverless, and containers modernization initiatives.

Today, with the new EADL offering, we’re bringing together the breadth of QuickSight’s embedding capabilities with proven expertise from AWS Data Lab. With EADL, AWS customers can request a hands-on session to prototype embedded analytics solutions, build custom architectures, and implement best practices with QuickSight-specialist Data Lab Solutions Architects. The output from this engagement is a customized solution that is specific to customer requirements, built using their data, in their AWS account, while providing hands-on learning to the engineering teams attending the lab. EADL engagements accelerate time from ideation to proof of concept to production by months, through tailored guidance while using resources across AWS teams to accelerate the rollout of embedded analytics features powered by QuickSight.

“We’re excited to announce the launch of the Embedded Analytics Data Lab that enables customers and ISVs to accelerate their embedded analytics offering using Amazon QuickSight. With Amazon QuickSight’s embedded analytics capabilities, AWS customers can integrate rich visuals and dashboards into their applications to scale to 100,000s of end-users, differentiating their user experiences—without any servers or infrastructure management. Embedded Analytics Data Lab helps demonstrate this business value in a matter of days by accelerating the QuickSight embedded journey for development teams.”

– Tracy Daugherty, General Manager, Amazon QuickSight.

Customers in EADL work closely with assigned AWS Data Lab Solutions Architect, solidifying the architecture design for their embedded analytics solution, including designing any data model and data pipeline components. The engagement then proceeds to the lab phase, where builders spend 2–4 days with their Solutions Architect, working backward from end goals and building a solution based on the previously defined architecture and real-time guidance from the Solutions Architect and other AWS service experts. Data Lab Solutions Architects also provide implementation guidance on data modeling, setting up multi-tenancy, enabling single sign-on with customers’ identity providers, enabling row- and column-level security, and tracking the health of the QuickSight environment. At lab completion, customers leave with a working prototype of their embedded analytics solution, built by their own builders in their AWS accounts that meet their requirements and specs.

Over the last year, we have worked closely with customers to help design and build their embedded analytics solutions. Some of these customers include BriteCore, Carbyne, and KRS.io.

BriteCore is an enterprise-level insurance processing suite that relies on dashboards to provide operational tracking and trend insights to insurance carriers on data points such as insurance claims and losses by agency, policy type, and line of business. To provide a seamless experience for their over 125,000 customers, BriteCore sought to integrate their BI offerings with their core platform and deliver dashboards to customers as embedded visuals. BriteCore’s engineering and reporting and analytics teams engaged the AWS Data Lab to design and validate the best integration approach between QuickSight and their application and to jumpstart building their interactive, embedded QuickSight dashboards.

“AWS Data Lab was pivotal in helping us build out our embedded analytics solution with the AWS suite of analytics services. Within 4 days, we built a working prototype of our multi-tenant solution with the right identity and security policies in place. Engaging with AWS Data Lab to build our solution definitely helped us reduce our time to production. Our customers now have even better insights into their business, and we will be able to deliver a much richer experience.”

– Supreet Oberoi, Senior Vice President of Engineering, BriteCore.

Carbyne is the global leader in contact center solutions, enabling emergency contact centers and selected enterprises to connect with callers on any connected devices via highly secure communication channels without downloading a consumer app. Carbyne worked with AWS Data Lab to explore options for building a low-latency, multi-tenant analytical system that would enable them to generate meaningful insights using QuickSight’s interactive dashboards for call center owners who manage 911 calls. Example insights include 911 call duration ranges, peak time of day for callers, and percentage of abandoned vs. answered calls—all data points that help Carbyne customers measure the effectiveness of their emergency response systems and then provision staff and resources accordingly. These insights were then embedded into their application, enabling a seamless experience for the 911 call center managers.

“This experience with the AWS Data Lab is what it means to be in true partnership. Data Lab’s support and efforts are much appreciated as we push innovative solutions to the public safety industry. I can say confidently that Data Lab’s support will reduce our time to production by weeks, if not months.”

– Alex Dizengof, Founder & CTO, Carbyne, Inc. 

KRS.io is a leader in coalition loyalty marketing connecting thousands of retailers with their customers on an intimate level with rewards programs and loyalty solutions. To truly democratize data, they set out to build a solution that harnesses the power of NQL. In a 1-day workshop with the AWS Data Lab team, KRS.io embedded QuickSight Q into Epiphany and successfully modeled 20 questions for their Profit Central back office accounting system, perpetual inventory, and loyalty datasets.

“In business, speed matters. Working with AWS Data Lab accelerated our timeframe from proof of concept to deployment. I had zero-tolerance for risk and the Data Lab allowed my team to meet my high bar for security and reliability”

– Brian McManus, CTO, KRS.io.

Get started with EADL

Prerequisites required to qualify for this offering are:

  • Valid embedded analytics use case.
  • Ready and accessible data to be used with QuickSight.
  • Available AWS sandbox or development environment to build the prototype. Data sources for QuickSight must be accessible through this sandbox account.
  • Available webpages or assets to be used to embed the QuickSight visuals and dashboards.
  • Full-time participation of at least two builders, including a builder that is comfortable and familiar with the web assets to be used for embedding.

To get started, register now. Once registered, a member of the AWS team will contact you with next steps.

About the Authors

Romit Girdhar manages Technical Product Management & Software Development teams for AWS Data Lab. He focuses on working backwards from customer outcomes to help accelerate their cloud journey. Romit has over a decade of experience working on engineering solutions for and with customers across two major public cloud companies – Amazon and Microsoft.

Kareem Syed-Mohammed is a Product Manager at Amazon QuickSight. He focuses on embedded analytics, APIs, and developer experience. Prior to QuickSight he has been with AWS Marketplace and Amazon retail as a PM. Kareem started his career as a developer and then PM for call center technologies, Local Expert and Ads for Expedia. He worked as a consultant with McKinsey and Company for a short while.