Posts Tagged Business Intelligence

Amazon QuickSight Identity Federation with Auth0: Managing QuickSight users with a third-party enterprise identity provider (IdP)


As a Solutions Architect working with Analytics customers, I am often asked about integrating Amazon QuickSight with Active Directory or single sign-on with third-party identity providers for user management.

Amazon QuickSight

Amazon QuickSight, according to AWS, is a scalable, serverless, embeddable, machine learning-powered business intelligence (BI) service built for the cloud. QuickSight lets you easily create and publish interactive BI dashboards that include machine learning-powered insights. QuickSight dashboards can be accessed from any device and seamlessly embedded into your applications, portals, and websites.


Auth0 is an easy-to-implement, adaptable authentication and authorization platform. Auth0’s identity and management platform, according to Auth0, provides greater control, superior security, and ease of use. Single Sign-On (SSO), whether through enterprise federation, social log-in, or username and password authentication, according to Auth0, allows users to simply log in once and use all applications they have been granted access to.

Identity Federation

According to AWS, Amazon QuickSight supports identity federation in both Standard and Enterprise editions. With federated identities, you manage users with your enterprise identity provider (IdP) and use AWS Identity and Access Management (IAM) to authenticate users when they sign in to Amazon QuickSight. You can use a third-party identity provider that supports Security Assertion Markup Language 2.0 (SAML 2.0) to provide a simple onboarding flow for your QuickSight users. Such identity providers include Microsoft Active Directory Federation Services (AD FS), Okta, Ping Identity, Duo, Azure AD, and Auth0.

With identity federation, your users get one-click access to their Amazon QuickSight applications using their existing identity credentials. You also have the security benefit of identity authentication by your identity provider. You can control which users have access to Amazon QuickSight using your existing identity provider. Authenticated users can log directly into QuickSight, bypassing the AWS Management Console.

Initiating Sign-On from Amazon QuickSight

In this post’s scenario, a user initiates the sign-on process from the Amazon QuickSight application portal without being signed on to Auth0, the identity provider. The user has an existing federated account managed by Auth0. The user may or may not already have an account on QuickSight. QuickSight sends an authentication request to the IdP, Auth0. After the user is successfully authenticated, QuickSight opens.

For this post, we will assume that you have signed up for the Enterprise Edition of Amazon QuickSight and chosen Use Role Based Federation (SSO) as opposed to Use Active Directory. The Use Role Based Federation (SSO) option will allow us to configure a third-party IdP for identity authentication.

Auth0 Users and Roles

In Auth0’s User Management interface, create three users and their associated roles representing three QuickSight personas: Admin, Author, and Reader. For demonstration purposes, I chose to name the three users based on their QuickSight personas: QuickSightAdmin1, QuickSightAuthor1, and QuickSightReader1.

Next, create three roles: QuickSight-Admin-Role, QuickSight-Author-Role, and QuickSight-Reader-Role. Role names are arbitrary as long as they are identical to the equivalent IAM roles in AWS (created later in the post).

Associate each user with their corresponding role, one user per role. For example, associate the QuickSightDemoAdmin1 user with the QuickSight-Admin-Role role.

Auth0 Application

Next, in Auth0’s Application interface, create a new Regular Web Application. Name the new application, Amazon QuickSight.

On the new application’s Addons tab, enable the SAML2 Web App option.

On the SAML2 Web App Addon’s Settings tab, set the Application Callback URL value to Change the JSON blob value for Settings to the following:

"audience": "urn:amazon:webservices"

The final configuration should match the example shown below.

Switch to the Usage tab. Download the Identity Provider Metadata XML file and note the Identity Provider Login URL value. You will need the metadata file and the URL to configure QuickSight later in the post.

Next, on the Connections tab of the new Amazon QuickSight application, ensure only Username-Password-Authentication is enabled.

Lastly, on the Settings tab, in the Application URIs configuration section, ensure the Allowed Callback URLs value is also set to

Auth Pipeline Rule

Next, in Auth0’s Auth Pipeline Rules interface, create a new Empty rule.

Name the new rule: Change QuickSight SAML configuration.

For the Script field value, use the following JavaScript code snippet.

function changeSamlConfiguration(user, context, callback) {
if (context.clientID !== '<your_web_client_id>')
return callback(null, user, context);
const assignedRoles = (context.authorization || {}).roles;
const accountId = '<your_aws_account_id>';
const provider = 'saml-provider/Auth0';
user.awsRole = 'arn:aws:iam::' + accountId + ':role/' + assignedRoles[0] +
',arn:aws:iam::' + accountId + ':' + provider;
user.quickSightUser =*/, '');
context.samlConfiguration.mappings = {
'': 'awsRole',
'': 'quickSightUser',
callback(null, user, context);

Replace the <your_web_client_id> placeholder with the Client ID of the Amazon QuickSight regular web application you created previously. The Client ID is listed in the Application interface, alongside the application’s name. Also, replace the <your_aws_account_id> placeholder with your twelve digital AWS account Id.

This rule will be used to modify the SAML assertion, as shown in the SAML assertion snippet example below, returned by Auth0 as part of the authentication process. The rule will inject the Amazon Resource Name (ARN) of the IAM role, to which the Auth0 user should be associated: QuickSight-Admin-Role, QuickSight-Author-Role, or QuickSight-Reader-Role. Note that the rule assumes one role per user. Additional logic would be required if the user is assigned to multiple roles.

AWS IAM Identity Provider

Back in the AWS Management Console, add an AWS IAM Identity Provider for Auth0. From the IAM console’s Identity providers interface, click Add provider. For Provider type choose SAML. Name the provider, Auth0. Click Choose file in the Metadata document section. Select the metadata document you downloaded earlier from Auth0. Click Add provider to finish.

The resulting Auth0 IAM Identity provider should be similar to the below example.

AWS IAM Policies and Roles

Next, create three AWS IAM roles that correspond to the three QuickSight personas of Administrator, Author, and Reader. These three roles also correspond to the three Auth0 roles we created previously. The Auth0 user will pass the Auth0 role name in the SAML document. The Auth0 role name will correspond to the IAM role. The IAM role defines the permissions the Auth0 user will have for QuickSight. We can associate many Auth0 users to one Auth0 Role and correspondingly with one IAM role.

First, create three IAM policies: QuickSight-Admin-Policy, QuickSight-Author-Policy, and QuickSight-Reader-Policy. These policies will each be associated with a corresponding IAM role. Create the policy, QuickSight-Admin-Policy. Replace the <your_aws_account_id> placeholder with your twelve digital AWS account Id.

"Version": "2012-10-17",
"Statement": [
"Effect": "Allow",
"Action": "quicksight:CreateAdmin",
"Resource": "arn:aws:quicksight::<your_aws_account_id>:user/${aws:userid}"

Then, QuickSight-Author-Policy.

"Version": "2012-10-17",
"Statement": [
"Effect": "Allow",
"Action": "quicksight:CreateUser",
"Resource": "arn:aws:quicksight::<your_aws_account_id>:user/${aws:userid}"

Finally, QuickSight-Reader-Policy.

"Version": "2012-10-17",
"Statement": [
"Effect": "Allow",
"Action": "quicksight:CreateReader",
"Resource": "arn:aws:quicksight::<your_aws_account_id>:user/${aws:userid}"

Next, create the three corresponding IAM roles and associate the corresponding IAM policy: QuickSight-Admin-Role (QuickSight-Admin-Policy), QuickSight-Author-Role (QuickSight-Author-Policy), and QuickSight-Reader-Role (QuickSight-Reader-Policy).

The role’s Trust relationships establishes a trust relationship between the role and the Auth0 IAM Identity provider, as shown below.

For each of the three roles, click on Edit trust relationship and modify the access control policy document as shown below. Replace the <your_aws_account_id> placeholder with your twelve digital AWS account Id.

"Version": "2012-10-17",
"Statement": [
"Effect": "Allow",
"Principal": {
"Federated": "arn:aws:iam::<your_aws_account_id>:saml-provider/Auth0"
"Action": "sts:AssumeRoleWithSAML",
"Condition": {
"StringEquals": {
"saml:aud": ""

The role’s access control policy document also includes a Condition policy element. According to AWS IAM documentation, for security reasons, AWS should be included as an audience in the SAML assertion your IdP sends to AWS. For the value of the Audience element, specify either or urn:amazon:webservices.

We can inspect the SAML assertion returned from Auth0 by decoding the form data in the response payload from Base64 format to XML. Below, we see the SAML assertion using Chrome’s Developer Tools to inspect network activity.

Note that the AWS IAM documentation states that the SAML AudienceRestriction value in the SAML assertion from the IdP does not map to the saml:aud context key that you can test in an IAM policy. Instead, the saml:aud context key comes from the SAML recipient attribute because it is the SAML equivalent to the OIDC audience field, for example, by These are shown in the SAML assertion XML snippet, below.

QuickSight IdP Configuration

The last step in integrating QuickSight and Auth0 is to configure QuickSight with Auth0’s specific IdP information. From the QuickSight Management interface, select Single sign-on (SSO). Switch the Status to ON.

Add the IdP URL value. As a reminder, this value came from the Auth0 Amazon QuickSight application’s SAML2 Web App Addon’s Usage tab, the Identity Provider Login URL value (see below). Make sure to copy the actual link associated with the URL shown.

Lastly, for the IdP redirect URL parameter value, use RelayState. Select Save to save the IdP configuration.

Testing Identity Federation

The easiest way to test the integration is to open a new Incognito window and point your browser to You should be prompted for your QuickSight account name. You created your account name when you signed up for QuickSight (e.g., acme-corp-sales).

Once you have entered your QuickSight account name, you should be redirected to Auth0 and presented with the Amazon QuickSight application’s log-in screen. Enter any of the three Auth0 user’s email addresses and passwords.

If the Auth0 log-in is successful, you will be redirected back and into the QuickSight application portal. If this is the first time the user has logged into QuickSight, you will be prompted for the user’s email address. Use the same email address the user is associated with in Auth0. In this scenario, the user will be self-registered with QuickSight and associated with the default namespace.

Your QuickSight experience and available features will vary, depending on the IAM role associated with the user, either Reader, Author, or Admin.

Close the Incognito browser window to end the current user session. Open a new Incognito browser window and repeat the process with the two remaining users, ensuring each can log-in successfully. Also, ensure the user’s experience in QuickSight matches the associated role, either Reader, Author, or Admin.

User Management

As a QuickSight Admin, log back into QuickSight and open the Manage users interface. All three Auth0 users should be registered with QuickSight, as shown in the example below. The users should be associated with the correct IAM role (column 1, below, left of the forward slash): QuickSight-Admin-Role, QuickSight-Author-Role, and QuickSight-Reader-Role. Users should also be associated with the correct QuickSight role (column 3, below): Reader, Author, or Admin.

Users can have custom permissions applied using the Manage permissions option.


In this post, we learned about how Amazon QuickSight supports identity federation. We learned how to manage users with a third-party enterprise identity provider (IdP), Auth0, and use AWS Identity and Access Management (IAM) to authenticate users when they sign in to Amazon QuickSight.


This blog represents my own viewpoints and not of my employer, Amazon Web Services. All product names, logos, and brands are the property of their respective owners.

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Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight: Part 2


In part one, we learned how to ingest, transform, and enrich raw, semi-structured data, in multiple formats, using Amazon S3, AWS Glue, Amazon Athena, and AWS Lambda. We built an S3-based data lake and learned how AWS leverages open-source technologies, including Presto, Apache Hive, and Apache Parquet. In part two of this post, we will use the transformed and enriched data sources, stored in the data lake, to create compelling visualizations using Amazon QuickSight.

athena-glue-architecture-v2High-level AWS architecture diagram of the demonstration.


If you recall the demonstration from part one of the post, we had adopted the persona of a large, US-based electric energy provider. The energy provider had developed and sold its next-generation Smart Electrical Monitoring Hub (Smart Hub) to residential customers. Customers can analyze their electrical usage with a fine level of granularity, per device and over time. The goal of the Smart Hub is to enable the customers, using data, to reduce their electrical costs. The provider benefits from a reduction in load on the existing electrical grid and a better distribution of daily electrical load as customers shift usage to off-peak times to save money.

Data Visualization and BI

The data analysis process in the demonstration was divided into four logical stages: 1) Raw Data Ingestion, 2) Data Transformation, 3) Data Enrichment, and 4) Data Visualization and Business Intelligence (BI).

athena-glue-0.pngFull data analysis workflow diagram (click to enlarge…)

In the final, Data Visualization and Business Intelligence (BI) stage, the enriched data is presented and analyzed. There are many enterprise-grade services available for data visualization and business intelligence, which integrate with Amazon Athena. Amazon services include Amazon QuickSight, Amazon EMR, and Amazon SageMaker. Third-party solutions from AWS Partners, many available on the AWS Marketplace, include Tableau, Looker, Sisense, and Domo.

In this demonstration, we will focus on Amazon QuickSight. Amazon QuickSight is a fully managed business intelligence (BI) service. QuickSight lets you create and publish interactive dashboards that include ML Insights. Dashboards can be accessed from any device, and embedded into your applications, portals, and websites. QuickSight serverlessly scales automatically from tens of users to tens of thousands without any infrastructure management.


Using QuickSight

QuickSight APIs

Amazon recently added a full set of aws quicksight APIs for interacting with QuickSight. For example, to preview the three QuickSight data sets created for this part of the demo, with the AWS CLI, we would use the list-data-sets comand.

aws quicksight list-data-sets –aws-account-id 123456789012

"Status": 200,
"DataSetSummaries": [
"Arn": "arn:aws:quicksight:us-east-1:123456789012:dataset/9eb88a69-20de-d8be-aefd-2c7ac4e23748",
"DataSetId": "9eb88a69-20de-d8be-aefd-2c7ac4e23748",
"Name": "etl_output_parquet",
"CreatedTime": 1578028774.897,
"LastUpdatedTime": 1578955245.02,
"ImportMode": "SPICE"
"Arn": "arn:aws:quicksight:us-east-1:123456789012:dataset/78e81193-189c-6dd0-864fb-a33244c9654",
"DataSetId": "78e81193-189c-6dd0-864fb-a33244c9654",
"Name": "electricity_rates_parquet",
"CreatedTime": 1578029224.996,
"LastUpdatedTime": 1578945179.472,
"ImportMode": "SPICE"
"Arn": "arn:aws:quicksight:us-east-1:123456789012:dataset/a474214d-c838-b384-bcca-ea1fcd2dd094",
"DataSetId": "a474214d-c838-b384-bcca-ea1fcd2dd094",
"Name": "smart_hub_locations_parquet",
"CreatedTime": 1578029124.565,
"LastUpdatedTime": 1578888788.135,
"ImportMode": "SPICE"
"RequestId": "2524e80c-7c67-7fbd-c3f1-b700c521badc"

To examine details of a single data set, with the AWS CLI, we would use the describe-data-set command.

aws quicksight describe-data-set \
–aws-account-id 123456789012 \
–data-set-id 9eb88a69-20de-d8be-aefd-2c7ac4e23748

QuickSight Console

However, for this final part of the demonstration, we will be working from the Amazon QuickSight Console, as opposed to the AWS CLI, AWS CDK, or CloudFormation templates.

Signing Up for QuickSight

To use Amazon QuickSight, you must sign up for QuickSight.


There are two Editions of Amazon QuickSight, Standard and Enterprise. For this demonstration, the Standard Edition will suffice.


QuickSight Data Sets

Amazon QuickSight uses Data Sets as the basis for all data visualizations. According to AWS, QuickSight data sets can be created from a wide variety of data sources, including Amazon RDS, Amazon Aurora, Amazon Redshift, Amazon Athena, and Amazon S3. You can also upload Excel spreadsheets or flat files (CSV, TSV, CLF, ELF, and JSON), connect to on-premises databases like SQL Server, MySQL, and PostgreSQL and import data from SaaS applications like Salesforce. Below, we see a list of the latest data sources available in the QuickSight New Data Set Console.


Demonstration Data Sets

For the demonstration, I have created three QuickSight data sets, all based on Amazon Athena. You have two options when using Amazon Athena as a data source. The first option is to select a table from an AWS Glue Data Catalog database, such as the database we created in part one of the post, ‘smart_hub_data_catalog.’ The second option is to create a custom SQL query, based on one or more tables in an AWS Glue Data Catalog database.


Of the three data sets created for part two of this demonstration, two data sets use tables directly from the Data Catalog, including ‘etl_output_parquet’ and ‘electricity_rates_parquet.’ The third data set uses a custom SQL query, based on the single Data Catalog table, ‘smart_hub_locations_parquet.’ All three tables used to create the data sets represent the enriched, highly efficient Parquet-format data sources in the S3-based Data Lake.


Data Set Features

There are a large number of features available when creating and configuring data sets. We cannot possibly cover all of them in this post. Let’s look at three features: geospatial field types, calculated fields, and custom SQL.

Geospatial Data Types

QuickSight can intelligently detect common types of geographic fields in a data source and assign QuickSight geographic data type, including Country, County, City, Postcode, and State. QuickSight can also detect geospatial data, including Latitude and Longitude. We will take advantage of this QuickSight feature for our three data set’s data sources, including the State, Postcode, Latitude, and Longitude field types.


Calculated Fields

A commonly-used QuickSight data set feature is the ‘Calculated field.’ For the ‘etl_output_parquet’ data set, I have created a new field (column), cost_dollar.


The cost field is the electrical cost of the device, over a five minute time interval, in cents (¢). The calculated cost_dollar field is the quotient of the cost field divided by 100. This value represents the electrical cost of the device, over a five minute time interval, in dollars ($). This is a straightforward example. However, a calculated field can be very complex, built from multiple arithmetic, comparison, and conditional functions, string calculations, and data set fields.


Data set calculated fields can also be created and edited from the QuickSight Analysis Console (discussed later).


Custom SQL

The third QuickSight data set is based on an Amazon Athena custom SQL query.

SELECT lon, lat, postcode, hash, tz, state
FROM smart_hub_data_catalog.smart_hub_locations_parquet;

Although you can write queries in the QuickSight Data Prep Console, I prefer to write custom Athena queries using the Athena Query Editor. Using the Editor, you can write, run, debug, and optimize queries to ensure they function correctly, first.


The Athena query can then be pasted into the Custom SQL window. Clicking ‘Finish’ in the window is the equivalent of ‘Run query’ in the Athena Query Editor Console. The query runs and returns data.


Similar to the Athena Query Editor, queries executed in the QuickSight Data Prep Console will show up in the Athena History tab, with a /* QuickSight */ comment prefix.



You will notice the three QuickSight data sets are labeled, ‘SPICE.’ According to AWS, the acronym, SPICE, stands for ‘Super-fast, Parallel, In-memory, Calculation Engine.’ QuickSight’s in-memory calculation engine, SPICE, achieves blazing fast performance at scale. SPICE automatically replicates data for high availability allowing thousands of users to simultaneously perform fast, interactive analysis while shielding your underlying data infrastructure, saving you time and resources. With the Standard Edition of QuickSight, as the first Author, you get 1 GB of SPICE in-memory data for free.

QuickSight Analysis

The QuickSight Analysis Console is where Analyses are created. A specific QuickSight Analysis will contain a collection of data sets and data visualizations (visuals). Each visual is associated with a single data set.

Types of QuickSight Analysis visuals include: horizontal and vertical, single and stacked bar charts, line graphs, combination charts, area line charts, scatter plots, heat maps, pie and donut charts, tree maps, pivot tables, gauges, key performance indicators (KPI), geospatial diagrams, and word clouds. Individual visual titles, legends, axis, and other visual aspects can be easily modified. Visuals can contain drill-downs.

A data set’s fields can be modified from within the Analysis Console. Field types and formats, such as date, numeric, currency fields, can be customized for display. The Analysis can include a Title and subtitle. There are some customizable themes available to change the overall look of the Analysis.


Analysis Filters

Data displayed in the visuals can be further shaped using a combination of Filters, Conditional formatting, and Parameters. Below, we see an example of a typical filter based on a range of dates and times. The data set contains two full days’ worth of data. Here, we are filtering the data to a 14-hour peak electrical usage period, between 8 AM and 10 PM on the same day, 12/21/2019.


Drill-Down, Drill-Up, Focus, and Exclude

According to AWS, all visual types except pivot tables offer the ability to create a hierarchy of fields for a visual element. The hierarchy lets you drill down or up to see data at different levels of the hierarchy. Focus allows you to concentrate on a single element within a hierarchy of fields. Exclude allows you to remove an element from a hierarchy of fields. Below, we see an example of all four of these features, available to apply to the ‘Central Air Conditioner’. Since the AC unit is the largest consumer of electricity on average per day, applying these filters to understand its impact on the overall electrical usage may be useful to an analysis. We can also drill down to minutes from hours or up to days from hours.


Example QuickSight Analysis

A QuickSight Analysis is shared by the Analysis Author as a QuickSight Dashboard. Below, we see an example of a QuickSight Dashboard, built and shared for this demonstration. The ‘Residential Electrical Usage Analysis’ is built from the three data sets created earlier. From those data sets, we have constructed several visuals, including a geospatial diagram, donut chart, heat map, KPI, combination chart, stacked vertical bar chart, and line graph. Each visual’s title, layout, and field display has all customized. The data displayed in the visuals have been filtered differently, including by date and time, by customer id (loc_id), and by state. Conditional formatting is used to enhance the visual appearance of visuals, such as the ‘Total Electrical Cost’ KPI.



In part one, we learned how to ingest, transform, and enrich raw, semi-structured data, in multiple formats, using Amazon S3, AWS Glue, Amazon Athena, and AWS Lambda. We built an S3-based data lake and learned how AWS leverages open-source technologies, including Presto, Apache Hive, and Apache Parquet. In part two of this post, we used the transformed and enriched datasets, stored in the data lake, to create compelling visualizations using Amazon QuickSight.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

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