Post Syndicated from Ying Wang original https://aws.amazon.com/blogs/big-data/using-administrative-dashboards-for-a-centralized-view-of-amazon-quicksight-objects/

“Security is job 0” is the primary maxim of all endeavors undertaken at AWS. Amazon QuickSight, the fast-growing, cloud-native business intelligence (BI) platform from AWS, allows security controls in a variety of means, including web browsers and API calls. These controls apply to various functions, such as user management, authorization, authentication, and data governance.

This post demonstrates how to build a workflow to enable a centralized visualization of QuickSight groups and user information, as well as QuickSight objects access permission auditing information. Combined with AWS CloudTrail logs, the solution enables your security team to detect any abnormal behavior in near-real time to ensure security compliance.

Benefits of a centralized dashboard

A group in QuickSight consists of a set of users. Using groups makes it easy to manage access and security. For example, you can configure three groups, called Marketing, HR, and BI Developer, and each has specific access privileges:

• The users in the Marketing group can only view the dashboards with marketing data
• The users in the HR group can only view the human resources data
• The users in the BI Developer group can edit all objects, including data sources, datasets, and dashboards

After the users and groups are configured, BI administrators can check and edit the object access permission by choosing Share for dashboards, datasets, and all other objects. The following screenshot shows the Manage dashboard sharing page on the QuickSight console.

As of this writing, individual object permission information is available on the QuickSight console, and user information is provided on the user management view on the QuickSight console. Our solution integrates QuickSight APIs with other AWS services to create an administrative dashboard that provides a centralized view of essential security information. This dashboard covers not only user lists and individual object access permission information available on the current platform, but also additional security information like group lists, user-group mapping information, and overall objects access permissions. This dashboard allows you to acquire unique security insights with its collection of comprehensive security information.

This post provides a detailed workflow that covers the data pipeline, sample Python codes, the AWS CloudFormation template, and a sample administrative dashboard. With the guidance of this post, you can configure a centralized information center in your own environment.

Solution overview

The following diagram illustrates the workflow of the solution.

The workflow involves the following steps:

1. A new user creation event in the CloudTrail log triggers the Amazon CloudWatch Events rule CreateUser.
2. The CreateUser rule triggers the AWS Lambda function User_Initiation. This function checks if the new user belongs to an existing group (for this post, we assume that their AWS Identity and Access Management (IAM) role equates to the group they should belong to in QuickSight). If such a group exists, the function adds the user into the group (CreateGroupMembership). Otherwise, it creates a new group (CreateGroup). The following is the process flow diagram of the Lambda function.
   
3. If the CreateGroupMembership event occurs, it triggers the Lambda function Data_Prepare. This function calls QuickSight APIs to get QuickSight group, user, and object access permissions information and saves the results to an Amazon Simple Storage Service (Amazon S3) bucket.

4. If the Lambda function User_Initiation creates a new QuickSight group in Step 2, it triggers a CloudWatch rule CreateGroup and the Lambda function Group_Initiation. The Group_Initiation function updates the QuickSight objects permission for the new group, such as granting it permission to view a dashboard.
5. The update object permission event in Step 4 triggers the Lambda function Data_Prepare, which updates the object access permissions information and saves the updated information to an S3 bucket.
6. The DeleteUser and DeleteGroup events also trigger the Lambda function Data_Prepare.
7. Based on the file in S3 that contains user-group mapping information and the QuickSight objects access permissions information, an Amazon Athena table is created.
8. A QuickSight dataset fetches the data in the Athena table created in Step 7 through DirectQuery Another QuickSight dataset is created based on the CloudTrail logs data. Then, based on these two datasets, a QuickSight dashboard is created.

Prerequisites

For this walkthrough, you should have the following prerequisites:

• An AWS account
• Access to the following AWS services:
  • QuickSight
  • Athena
  • Lambda
  • Amazon S3
• Basic knowledge of Python
• Security Assertion Markup Language 2.0 (SAML 2.0) or OpenID Connect (OIDC) single sign-on (SSO) configured for QuickSight access

Creating resources

Create your resources by downloading the following AWS Cloud Development Kit (AWS CDK) stack from the GitHub repo.

Pull the Administrative Dashboard folder and run the command cdk deploy QuickSightStack to deploy the resources. For more information, see AWS CDK Intro Workshop: Python Workshop.

Implementing the solution

This solution assumes that the users log in to their QuickSight account with identity federation through SAML or OIDC. For instructions on setting up SAML SSO, see Single Sign-On Access to Amazon QuickSight Using SAML 2.0, Federate Amazon QuickSight access with Okta and Enabling Amazon QuickSight federation with Azure AD. For OIDC SSO, see Use Amazon QuickSight Federated Single Sign-On with Amazon Cognito User Pools.

After you set up the IAM policy of the web identity or SAML federation role, you don’t need to invite users manually. A QuickSight user is provisioned automatically when opening QuickSight for the first time.

In this solution, one SAML federation role corresponds to a QuickSight group. There are four sample SAML roles: Marketing, HR, BI-Admin, and BI Developer.

The following code is the sample CreateUser CloudTrail event:

{
    "userIdentity": {
        "type": "AssumedRole",
        "principalId": "AROAZEAWJBC3FBJ7KDH2N:[email protected]",
        "arn": "arn:aws:sts::<aws_account_id>:assumed-role/ BI-Developer/[email protected]",
        "accountId": <aws_account_id>,
        "sessionContext": {
            "sessionIssuer": {
                "type": "Role",
                "principalId": "AROAZEAWJBC3FBJ7KDH2N",
                "arn": "arn:aws:iam:: <aws_account_id>:role/BI-Developer",
                "accountId": <aws_account_id>,
                "userName": " BI-Developer"}
        }
    },
    "eventSource": "quicksight.amazonaws.com",
    "eventName": "CreateUser",
    "awsRegion": "us-east-1",
    "eventType": "AwsServiceEvent",
…    
}

This event triggers the CloudWatch events rule CreateUser. The following screenshot shows the details of this rule.

The CreateUser rule triggers the Lambda function User_Initiation. This function gets the QuickSight group name (Marketing, HR, BI-Admin, or BI Developer) and compares the group name with the existing group list. If such a group exists, it adds this new user into that group (CreateGroupMembership). Otherwise, it creates a new group (CreateGroup).

The Data_Prepare Lambda function is triggered by adding a new user into a group event (CreateGroupMembership). This function calls the QuickSight API describe_data_set_permissions, describe_dashboard_permissions, or describe_data_source_permissions to get the object access permissions. It also calls the APIs list_user_groups and list_users to get the list of users and the groups of each user. Finally, this function creates two files containing QuickSight group, user, or object access information, and saves these files into a S3 bucket.

If a new QuickSight group is created, it triggers the Lambda function Group_Initiation to update the QuickSight dashboard, dataset, or data source permission for this new group. For example, if the HR group is created, the Group_Initiation function lets the HR group view the Employee Information dashboard.

The UpdateDashboardPermissions, UpdateDatasetPermissions, and UpdateDatasourcePermissions events trigger the Lambda function Data_Prepare to update the object access permissions information stored in the S3 bucket.

To create two Athena tables (Groups and Objects), run an AWS Glue crawler.

The following screenshot is sample data of the Groups table.

The following screenshot is sample data of the Objects table.

You can create a DirectQuery dataset in QuickSight with the two new Athena tables joined. See the following screenshot.

The Objects table contains the information of objects (such as dashboards and datasets) belonging to each group or user. Furthermore, we can create a calculated field called Ownership based on Permissions information (the actions column in objects table) to provide the objects owner with viewer or user information (the object owner can delete this object, whereas the viewer or user can’t do the deletion action).

The following screenshot shows the relevant code.

For instructions on building an Athena table with CloudTrail events, see Amazon QuickSight Now Supports Audit Logging with AWS CloudTrail. For this post, we create the table cloudtrail_logs in the default database.

After that, run the following SQL query to build an Athena view with QuickSight events for the last 24 hours:

CREATE OR REPLACE VIEW qsctlog_last_24h AS 
SELECT "useridentity"."type", "split_part"("useridentity"."sessioncontext"."sessionissuer"."arn", '/', 2) "group_name"
, COALESCE("useridentity"."username", "split_part"("useridentity"."arn", '/', 3)) "user_name"
, "eventtime"
, "eventname"
, "awsregion"
, "resources"
, "eventtype"
, "recipientaccountid"
, "serviceeventdetails"
FROM default.cloudtrail_logs
WHERE (("eventsource" = 'quicksight.amazonaws.com') AND (CAST("split_part"("eventtime", 'T', 1) AS date) > "date_add"('hour', -24, "now"()))) 

Running queries in Athena

Now we have the datasets ready in Athena and can run SQL queries against them to answer some common administrative questions.

To create a QuickSight dataset to catch all orphan users that don’t belong to any group, as well as the events done by these users in the last 24 hours, run the following SQL query:

SELECT g.*
,log.group_name as role_name
,log.user_name as log_user_name
,log.type
,log.eventtime
,log.eventname
,log.awsregion
,log.eventtype
,log.recipientaccountid
,log.serviceeventdetails
FROM "default"."qsctlog_last_24h" as log 
full outer join 
"default"."groups" as g 
on log.awsregion=g.aws_region AND log.group_name=g.group_name AND log.user_name=g.user_name 
where g.group_name is null or g.group_name=''

To create a QuickSight dataset to list objects belonging to each group or user, run the following query:

SELECT group_name AS "Group/User Name"
, object_name
, object_type
, if((actions LIKE '%Delete%'), 'Owner', 'Viewer/User') AS Ownership
FROM "default"."object" full outer
JOIN "default"."groups"
    ON group_name=principal_name
WHERE principal_type='group'
UNION
SELECT user_name AS "Group/User Name"
, object_name
, object_type
, if((actions LIKE '%Delete%'), 'Owner', 'Viewer/User') AS Ownership
FROM "default"."object" full outer
JOIN "default"."groups"
    ON user_name=principal_name
WHERE principal_type='user'
ORDER BY  "Group/User Name" asc;

The following screenshot shows the sample data.

Building dashboards

In addition to running queries directly in Athena, we can build a dashboard using this same data in QuickSight. The following screenshot shows an example dashboard that you can make using our data.

You can interactively play with the sample dashboard in the following Interactive Dashboard Demo.

Cleaning up

To avoid incurring future charges, delete the resources you created by running the following command:

cdk destroy QuickSightStack 

Then, on the Amazon S3 console, delete the S3 bucket administrative-dashboard<your_aws_account_id>.

Conclusion

This post discussed how BI administrators can use the QuickSight dashboard, Lambda functions, and other AWS services to create a centralized view of groups, users, and objects access permission information and abnormal access auditing. We also presented a serverless data pipeline to support the administrative dashboard. This dashboard can provide you with unique security insights with its collection of comprehensive security information.

About the Author

Ying Wang is a Data Visualization Engineer with the Data & Analytics Global Specialty Practice in AWS Professional Services.

Post Syndicated from Jose Kunnackal original https://aws.amazon.com/blogs/big-data/amazon-quicksight-adds-support-for-on-sheet-filter-controls/

Amazon QuickSight now supports easy and intuitive filter controls that you can place beside visuals on dashboards, allowing readers to quickly slice and dice data in the context of its visual representation. You can create these filter controls from existing or new filters with a single click, and configure them to support different operations, such as filtering specific dates, relative dates, or date rages; setting upper and lower thresholds for numeric values; adding drop-downs with single-select or multi-select options; and more.

In this post, we review how these filtering improvements, together with themes and dashboard layout options, let you create stunning, interactive dashboards that you can share with tens of thousands of users, whether in QuickSight or using embedded dashboards within apps, without any server provisioning or management needed, and paying for what you use. For this use case, we use the COVID-19 public dataset for Washington state. The following screenshot shows the dashboard with on-sheet filters added.

Using the new filter controls

Let us take a deeper look at the new filter controls, which are now placed across the sheet between visuals. Creating these controls is easy— simply add a new filter on the required dimension or metric, set the scope to either filter the entire dashboard or specific visuals within, and add it to the sheet. QuickSight now automatically maps filters across multiple datasets used within a sheet, so actions on the filter can apply to every visual on the sheet if so desired. For example, to add a new filter on the county field, create a filter on the field using the left hand navigation pane when authoring the dashboard.

Set the scope of the filter as desired, and choose Add to sheet to add a control to the sheet.

QuickSight then creates a moveable control that you can place anywhere on the sheet. QuickSight chooses the control type depending on the type of filter created. For example, when creating filters on dimensional fields, QuickSight adds a multi-select drop-down control by default. You can change the control type by choosing the Settings icon in the control’s visual menu.

In the Edit control section, you can make further updates to your control.

You can also place these controls in the control drawer on top of the sheet by choosing Pin to top.

On-sheet controls

The on-sheet controls currently supported include those previously supported with QuickSight parameter controls (single-select drop-downs, multi-select drop-downs, date and time picker, single-sided slider, single-line text box). QuickSight now supports new controls for date and time range selection, relative date selection, and numeric range selection. You can move existing parameter controls on the sheet and place them beside the new filter controls.

Let’s take a quick look at these new controls. You can use date and time range selection controls when you have a BETWEEN date range filter on your dashboard.

Relative date controls provide readers with powerful functionality to apply date filters at yearly, quarterly, monthly, weekly, daily, hourly, and minute levels. For example, you can choose to filter by current year, previous year, year to date, and last or next N years. These controls provide a great way to ensure your users always see the latest data whether viewing data in dashboards or email reports.

With a two-sided slider control, you can set lower and upper bounds on metric filters. To add a two-sided filter, you simply add the numeric BETWEEN filter to the sheet.

Additional dashboard features

In this section, we look at some other aspects of the dashboard.

Customizing your dashboard layout and theme

This dashboard uses a different set of colors and highlights than the regular palette in QuickSight. We achieve this by using a custom theme, which lets you pick a color palette for data values, background and foreground colors, fonts, and more. You can also choose to remove borders around the visual, show or remove spacing between visuals, and add margins around the sheet.

To create themes for your dashboards, go to the Themes from the left hand menu when authoring the dashboard. You can choose one of starter themes available in QuickSight and choose Save as to customize your own and use the theme editor to visualize changes before saving the theme.

On the Main page of the theme editor, you can customize your background color, foreground color, and font.

On the Data page, you can customize your data colors.

For more information about QuickSight themes, see Evolve your analytics with Amazon QuickSight’s new APIs and theming capabilities.

Images and rich text

You can use the QuickSight insight editor (available in Enterprise Edition) to add rich text and images to dashboards. To add an image, go to the insight editor, choose the Image icon, and provide the hosted location of the image. You can also hyperlink this image to any URL.

Reference lines

The bar chart within the dashboard also includes a reference line, which you can easily add to a QuickSight line or bar visual. You can configure a reference line from the visual menu.

Conditional formatting

Tables in the dashboard use conditional formatting, which you can add from the table menu.

Scaling

QuickSight dashboards default to auto-fit mode, so they are responsive based on screen size. Instead, you can choose to pick a specific screen resolution to optimize for, based on the devices your audience most commonly uses to view the dashboard. To adjust the scaling mode, choose Settings in the navigation pane while in dashboard authoring (analysis) mode.

For this post, our dashboard was built for a 1366px screen, and scales that view to a larger or smaller screen to ensure that all users see the same content (mobile devices continue to fall back to a single column, mobile-specific layout to ensure usability). Opting for the optimized mode also makes sure that your email reports look exactly like the dashboard that your viewers interact with.

Conclusion

With denser dashboards, custom themes, and new on-sheet filter controls in QuickSight, you can provide richer dashboards for your readers. Visit our user guide to learn more about on-sheet filter controls, themes, dashboard scaling options and more. For more information about authoring dashboards in QuickSight, watch Part 1 and Part 2 of our two-part interactive workshop.

About the authors

Jose Kunnackal John is a principal product manager for Amazon QuickSight.

Sahitya Pandiri is a technical program manager with Amazon Web Services. Sahitya has been in product/program management for 6 years now, and has built multiple products in the retail, healthcare, and analytics space.

Post Syndicated from Chirag Dhull original https://aws.amazon.com/blogs/big-data/icbiome-uses-amazon-quicksight-to-empower-hospitals-in-dealing-with-harmful-pathogens/

In response to the COVID-19 pandemic, hospitals and healthcare organizations are increasingly employing genetic sequencing to screen, track, and contain harmful pathogens. ICBiome is a startup that has been working on this problem for several years, creating innovative data analytics products using AWS to help hospitals and researchers address both community-associated and hospital-acquired infections. Building on its early focus on methicillin-resistant Staphylococcus aureus (MRSA), the company is expanding its solution to cover COVID-19 and other types of infections.

ICBiome has now integrated Amazon QuickSight embedded analytics into its solution to provide hospital investigators and other users with easy access to powerful analytics and visualizations on any device.

Empowering hospitals and communities

Many hospitals lack access to sequencing capabilities, and hospitals that have such capabilities often lack complex data processing and advanced bioinformatics expertise. Open-source platforms are limited, complex, and scale poorly. They require hardware resources and management beyond the reach of most hospitals and lack visualization software to interactively explore results without specialized know-how.

BiomeMRSA, ICBiome’s cloud-based genomics service, overcomes these gaps by providing easy access to sequencing through its Clinical Laboratory Improvement Amendments (CLIA)-certified provider, with storage, computing, analytics, and visualization delivered from the AWS Cloud. With the addition of QuickSight embedded analytics, hospital investigators can easily monitor pathogens coming through their hospital environments.

Seeing the whole picture

The company’s innovative data processing methodology enables the comparison of full genomes, as opposed to open-source platforms that only compare portions of them. With a vast and growing database of MRSA mutations, the company enables rapid identification of existing and new strains to help hospitals combat infection.

BiomeMRSA monitors intensive care unit (ICU) settings and identifies emerging MRSA transmissions. By using BiomeMRSA to monitor their ICU settings, hospitals can proactively target local reservoirs and reduce overall incidence rates of MRSA. To support its R&D in pathogen genomics, the company has received more than $1 million in funding from NIAID and state grants. In 2019, ICBiome completed an evaluation of BiomeMRSA at a major hospital. It’s now evaluating and validating BiomeMRSA at two additional hospitals.

Improving patient care and treatment cost

This capability gives hospitals the potential to transform how they manage outbreaks. Intake and weekly screenings of patients lack the resolution to determine how the disease was acquired. This means hospitals can be financially and reputationally penalized for a disease that was acquired before admission.

BiomeMRSA allows continuous monitoring of ICUs by routinely sequencing all samples and returning those samples to hospitals within days. This allows for earlier detection, mitigation of outbreaks, and the reduction of colonization rates of ICU patients by identifying transmissions where the patient was colonized but not infected. Lowering colonization rates can reduce the risk of post-care infections and costly readmissions.

ICBiome is now expanding this work to include tracking pandemics such as COVID-19. The Virginia-based company is adding a COVID-19 application, BiomeCOVID, to address the ongoing crisis. Previously deployed for real-time operations, BiomeCOVID will use genetics to greatly increase the accuracy of contact tracing by separating isolates within different lineages and further identifying unique clusters within those lineages. The company is accelerating the development of BiomeCOVID to meet the critical healthcare need.

Visualizing genomics to combat disease

Since its first NIH grant award in 2017, ICBiome has built its solutions in the AWS Cloud. To deliver rich, in-depth analytics capabilities to its customers, the company chose QuickSight for embedded dashboards in its products. According to Dr. Srini Iyer, founder and CEO of ICBiome, QuickSight provides the agility and scalability the company needs to deliver in-depth solutions quickly. “ICBiome has developed an end-to-end cloud architecture for processing genetic sequence data from bacterial and viral pathogens,” says Dr. Iyer. “Over the last year, ICBiome looked at several data analytics tools that can serve as the visual interface for our product line. We did a formal analysis of both cloud and on-premises business intelligence solutions, and ultimately decided to choose QuickSight for the security, scalability, cost, and reliability.”

The following image shows the Analytics Dashboard for BiomeMRSA:

ICBiome’s customers are centered in large hospitals and public health agencies. Security and data privacy are critical to these decision-makers, particularly because bacterial and viral samples are taken from patients in the course of clinical care. QuickSight uses the same HIPAA-compliant security architecture used by the rest of the company’s data ingestion and processing systems built on AWS.

Cost-efficient and highly scalable

Scalability is critical because the company expects to handle exponentially growing amounts of data due to the growth in whole-genome sequencing to identify pathogen strains. “We opted for Amazon QuickSight as it streamlines operational scaling and integration not just within the application layer but across the entire AWS ecosystem,” says Dante Martinez, Chief Technology Officer of ICBiome.

As a startup providing affordable solutions for hospitals, cost is a critical concern for ICBiome. Many others in its space have opted to use open-source business intelligence (BI) solutions for this reason. However, the company’s analysis showed that this wasn’t the most cost-effective route. “Many open-source BI platforms require a high level of maintenance to ensure reliability both for routine operations and lifecycle management,” says Martinez. “Given the critical nature of our application, we did not want to compromise product integrity by choosing a difficult-to-maintain open-source platform or an entry-level BI tool. Amazon QuickSight is highly cost-effective, but it is also full-featured and mature, providing an experience on par with much more expensive competitors.”

The following diagram illustrates ICBiome’s cloud architecture.

Visualizing a healthier future

The company has big plans for the embedded analytics functionality enabled by QuickSight. “Once we complete the launches of BiomeCOVID and BiomeMRSA as commercial SaaS products, we intend to bring to market other surveillance products such as BiomeCRE and BiomeSTD to track other critical threats to public health,” says Dr. Iyer. “We will also be targeting other biomedical areas where we can develop new classes of products that provide critical data analytical capabilities that are currently not feasible.”

With the landscape of public health now transformed in the wake of the COVID-19 pandemic, ICBiome will continue to expand its SaaS portfolio with Amazon to provide hospitals innovative data analytics tools in infection prevention, epidemiology, and patient care.

About the  Author

Chirag Dhull is a Principal Product Marketing Manager for Amazon QuickSight.

Post Syndicated from Adnan Hasan original https://aws.amazon.com/blogs/big-data/enabling-amazon-quicksight-federation-with-azure-ad/

Customers today want to establish a single identity and access strategy across all of their own apps, such as on-premises apps, third-party cloud apps (SaaS), or apps in AWS. If your organization use Azure Active Directory (Azure AD) for cloud applications, you can enable single sign-on (SSO) for applications like Amazon QuickSight without needing to create another user account or remember passwords. You can also enable role-based access control to make sure users get appropriate role permissions in QuickSight based on their entitlement stored in Active Directory attributes or granted through Active Directory group membership. The setup also allows administrators to focus on managing a single source of truth for user identities in Azure AD while having the convenience of configuring access to other AWS accounts and apps centrally.

In this post, we walk through the steps required to configure federated SSO between QuickSight and Azure AD. We also demonstrate ways to assign a QuickSight role based on Azure AD group membership. Administrators can publish the QuickSight app in the Azure App portal to enable users to SSO to QuickSight using their Azure AD or Active Directory credentials.

The solution in this post uses an identity provider (IdP)-initiated SSO, which means your end-users must log in to Azure AD and choose the published QuickSight app in the Azure App Portal portal to sign in to QuickSight.

Registering a QuickSight application in Azure AD

Your first step is to create a QuickSight application in Azure AD.

1. Log in to your Azure portal using the administrator account in the Azure AD tenant where you want to register the QuickSight application.
2. Under Azure Services, open Azure Active Directory and under Manage, choose Enterprise Application.
   
3. Choose New Application.
4. Select Non-gallery application.
5. For Name, enter Amazon QuickSight.
   
6. Choose Add to register the application.

Creating users and groups in Azure AD

You can now create new users and groups or choose existing users and groups that can access QuickSight.

1. Under Manage, choose All applications and open Amazon QuickSight
2. Under Getting Started, choose Assign users and groups.
3. For this post, you create three groups, one for each QuickSight role:
   1. QuickSight-Admin
   2. QuickSight-Author
   3. QuickSight-Reader

For instructions on creating groups in Azure AD, see Create a basic group and add members using Azure Active Directory.

Configuring SSO in Azure AD

You can now start configuring the SSO settings for the app.

1. Under Manage, choose Single sign-on.
2. For Select a single sign-on method, choose SAML.
   
3. To configure the sections, choose Edit.
4. In the Basic SAML Configuration section, for Identifier (Entity ID), enter URN:AMAZON:WEBSERVICES.

This is the entity ID passed during the SAML exchange. Azure requires that this value be unique for each application. For additional AWS applications, you can append a number to the string; for example, URN:AMAZON:WEBSERVICES2.

5. For Reply URL, enter https://signin.aws.amazon.com/saml.
6. Leave Sign on URL blank.
   
7. For Relay State, enter https://quicksight.aws.amazon.com.
8. Leave Logout Url blank.
   
   
9. Under SAML Signing Certificate, choose Download next to Federation Metadata XML.
   

You use this XML document later when setting up the SAML provider in AWS Identity and Access Management (IAM).

10. Leave this tab open in your browser while moving on to the next steps.

Creating Azure AD as your SAML IdP in AWS

You now configure Azure AD as your SAML IdP.

1. Open a new tab in your browser.
2. Log in to the IAM console in your AWS account with admin permissions.
3. On the IAM console, choose Identity providers.
4. Choose Create provider.
5. For Provider name, enter AzureActiveDirectory.
6. Choose Choose File to upload the metadata document you downloaded earlier.
   
7. Choose Next Step.
8. Verify the provider information and choose Create.
9. On the summary page, record the value for the provider ARN (arn:aws:iam::<AccountID>:saml-provider/AzureActiveDirectory).

You need this ARN to configure claims rules later in this post.

You can also complete this configuration using the AWS Command Line Interface (AWS CLI).

Configuring IAM policies

In this step, you create three IAM policies for different role permissions in QuickSight:

• QuickSight-Federated-Admin
• QuickSight-Federated-Author
• QuickSight-Federated-Reader

Use the following steps to set up QuickSight-Federated-Admin policy. This policy grants admin privileges in QuickSight to the federated user:

1. On the IAM console, choose Policies.
2. Choose Create Policy.
3. Choose JSON and replace the existing text with the following code:

   {
       "Version": "2012-10-17",
       "Statement": [
           {
               "Effect": "Allow",
               "Action": "quicksight:CreateAdmin",
               "Resource": "*"
           }
       ]
   }

4. Choose Review policy
5. For Name enter QuickSight-Federated-Admin.
6. Choose Create policy.

Now repeat the steps to create QuickSight-Federated-Author and QuickSight-Federated-Reader policy using the following JSON codes for each policy:

QuickSight-Federated-Author

The following policy grants author privileges in QuickSight to the federated user:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "quicksight:CreateUser",
            "Resource": "*"
        }
    ]
}

QuickSight-Federated-Reader

The following policy grants reader privileges in QuickSight to the federated user:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "quicksight:CreateReader",
            "Resource": "*"
        }
    ]
}

Configuring IAM roles

Next, create the roles that your Azure AD users assume when federating into QuickSight. The following steps set up the admin role:

1. On the IAM console, choose Roles.
2. Choose Create role.
3. For Select type of trusted entity, choose SAML 2.0 federation.
4. For SAML provider, choose the provider you created earlier (AzureActiveDirectory).
5. Select Allow programmatic and AWS Management Console access.
6. For Attribute, make sure SAML:aud is selected.
7. Value should show https://signin.aws.amazon.com/saml.
   
8. Choose Next: Permissions.
9. Choose the QuickSight-Federated-Admin IAM policy you created earlier.
10. Choose Next: Tags.
11. Choose Next: Review
12. For Role name, enter QuickSight-Admin-Role.
13. For Role description, enter a description.
14. Choose Create role.
    
15. On the IAM console, in the navigation pane, choose Roles.
16. Choose the QuickSight-Admin-Role role you created to open the role’s properties.
17. Record the role ARN to use later.
18. On the Trust Relationships tab, choose Edit Trust Relationship.
19. Under Trusted Entities, verify that the IdP you created is listed.
20. Under Conditions, verify that SAML:aud with a value of https://signin.aws.amazon.com/saml is present.
    
21. Repeat these steps to create your author and reader roles and attach the appropriate policies:
    1. For QuickSight-Author-Role, use the policy QuickSight-Federated-Author.
    2. For QuickSight-Reader-Role, use the policy QuickSight-Federated-Reader.

Configuring user attributes and claims in Azure AD

In this step, you return to the application in Azure portal and configure the user claims that Azure AD sends to AWS.

By default, several SAML attributes are populated for the new application, but you don’t need these attributes for federation into QuickSight. Under Additional Claims, select the unnecessary claims and choose Delete.

For this post, you create three claims:

• Role
• RoleSessionName
• SAML_SUBJECT

Creating the Role claim

To create the Role claim, complete the following steps:

1. Under Manage, choose Single sign-on.
2. Choose Edit on User Attributes & Claims section
3. Choose Add new claim.
4. For Name, enter Role.
5. For Namespace, enter https://aws.amazon.com/SAML/Attributes.
6. Under Claim conditions, add a condition for the admin, author, and reader roles. Use the parameters in the following table:

Creating the RoleSessionName claim

To create your RoleSessionName claim, complete the following steps:

1. Choose Add new claim.
2. For Name, enter RoleSessionName.
3. For Namespace, enter https://aws.amazon.com/SAML/Attributes.
4. For Source, choose Transformation.
5. For Transformation, enter ExtractMailPrefix().
6. For Parameter 1, enter user.userprincipalname.

We use the ExtractMailPrefix() function to extract the name from the userprincipalname attribute. For example, the function extracts the name joe from the user principal name value of [email protected]. IAM uses RoleSessionName to build the role session ID for the user signing into QuickSight. The role session ID is made up of the Role name and RoleSessionName, in Role/RoleSessionName format. Users are registered in QuickSight with the role session ID as the username.

Creating the SAML_SUBJECT claim

To create your final claim, SAML_SUBJECT, complete the following steps:

1. Choose Add new claim.
2. For Name, enter SAML_SUBJECT.
3. For Namespace, enter https://aws.amazon.com/SAML/Attributes.
4. For Source, choose Attribute.
5. For Source attribute, enter ““Azure AD - QuickSight SSO””.

Testing the application

You’re now ready to test the application.

1. In the Azure portal, on the Azure Active Directory page, choose All groups.
2. Update the group membership of the QuickSight-Admin group by adding the current user to it.
   
3. Under Enterprise Applications, choose Amazon QuickSight.
4. Under Manage, choose Single sign-on.
5. Choose Test this application to test the authentication flow.
6. Log in to QuickSight as an admin.

The following screenshot shows you the QuickSight dashboard for the admin user.

7. Remove the current user from QuickSight-Admin Azure AD group and add it to QuickSight-Author group.

When you test the application flow, you log in to QuickSight as an author.

8. Remove the current user from QuickSight-Author group and add it to QuickSight-Reader group.

When you test the application flow again, you log in as a reader.

By removing the user from the Azure AD group will not automatically remove the registered user in QuickSight. You have to remove the user manually in the QuickSight admin console. The user management inside QuickSight is documented in this article.

Deep-linking QuickSight dashboards

You can share QuickSight dashboards using the sign-on URL for the QuickSight application published in the Azure Apps portal. This allows users to federate directly into the QuickSight dashboard without having to land first on the QuickSight homepage.

To deep-link to a specific QuickSight dashboard with SSO, complete the following steps:

1. Under Enterprise Applications, choose Amazon QuickSight
2. Under Manage, choose Properties.
3. Locate the User access URL.
   
4. Append ?RelayState to the end of the URL containing the URL of your dashboard. For example, https://myapps.microsoft.com/signin/Amazon%20QuickSight/a06d28e5-4aa4-4888-bb99-91d6c2c4eae8?RelayState=https://us-east-1.quicksight.aws.amazon.com/sn/dashboards/224103be-0470-4de4-829f-390e55b3ef96.

You can test it by creating a custom sign-in URL using the RelayState parameter pointing to an existing dashboard. Make sure the user signing in to the dashboard has been granted proper access.

Summary

This post provided step-by-step instructions to configure a federated SSO with Azure AD as the IdP. We also discussed how to map users and groups in Azure AD to IAM roles for secure access into QuickSight.

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

About the Author

Adnan Hasan is a Global GTM Analytics Specialist at Amazon Web Services, helping customers transform their business using data, machine learning and advanced analytics. 

Post Syndicated from Dorothy Li original https://aws.amazon.com/blogs/big-data/bringing-the-power-of-embedded-analytics-to-your-apps-and-services-with-amazon-quicksight/

In the world we live in today, companies need to quickly react to change—and to anticipate it. Customers tell us that their reliance on data has never been greater than what it is today. To improve your decision-making, you have two types of data transformation needs: data agility, the speed at which data turns into insights, and data transparency, the need to present insights to decision makers. Going forward, we expect data transformation projects to become a centerpiece in every organization, big or small.

Furthermore, applications are migrating to the cloud faster than ever. Applications need to scale quickly to potentially millions of users, have global availability, manage petabytes of data, and respond in milliseconds. Such modern applications are built with a combination of these new architecture patterns, operational models, and software delivery processes, and allow businesses to innovate faster while reducing risk, time-to-market, and total cost of ownership.

An emerging area from these two trends is to combine the power of application modernization with data transformation. This emerging trend is often called embedded analytics, and is the focus of this post.

The case for embedded analytics

Applications generate a high volume of structured and unstructured data. This could be clickstream data, sales data, data from IoT devices, social data, and more. Customers who are building these applications (such as software-as-a-service (SaaS) apps or enterprise portals) often tell us that their end-users find it challenging to derive meaning from this data because traditional business intelligence (BI) approaches don’t always work.

Traditional BI tools live in disparate systems and require data engineering teams to provide connectivity and continous integration with the application, adding to complexity and delays in the overall process. Even after the connectivity is built, you must switch back and forth between your application and the BI tool, causing frustration and decreasing the overall pace of decision-making. Customers tell us that their development teams are constantly looking for new ways to delight their users, and embedding the BI capability directly into their applications is one of the most requested asks from their end-users.

Given the strategic importance of this capability, you can use this to differentiate and up-sell as a new service in their applications. Gartner research demonstrates that 63% of CEOs expect to adopt a product-as-a-service model in the next two years, making this a major market opportunity. For example, if you provide financial services software, you can empower users to perform detailed analysis of portfolio performance trends. An HR solution might enable managers to visualize and predict turnover rates. A supply chain management solution could embed the ability to slice and dice KPIs and better understand the efficiency of logistics routes.

Comparing common approaches to embedded analytics

The approach to building an embedded analytics capability needs to deliver on the requirements of modern applications. It must be scalable, handle large amounts of data without compromising agility, and seamlessly integrate with the application’s user experience. Choosing the right methodology becomes especially important in the face of these needs.

You can build your own embedded analytics solution, but although this gives you maximum control, it has a number of disadvantages. You have to hire specialized resources (such as data engineers for building data connectivity and UX developers for building dashboards) and maintain dedicated infrastructure to manage the data processing needs of the application. This can be expensive, resource-intensive, and complex to build.

Embedding traditional BI solutions that are available in the market has limitations as well, because they’re not purpose-built for embedding use cases. Most solutions are server-based, meaning that they’re challenging to scale and require additional infrastructure setup and ongoing maintenance. These solutions also have restrictive, pay-per-server pricing, which doesn’t fully meet the needs of end-users that are consuming applications or portals via a session-based usage model.

A new approach to embedded analytics

At AWS re:Invent 2019, we launched new capabilities in Amazon QuickSight that make it easy to embed analytics into your applications and portals, empowering your customers to gain deeper insights into your application’s data. Unlike building your own analytics solution, which can be time-consuming and hard to scale, QuickSight allows you to quickly embed interactive dashboards and visualizations into your applications without compromising on the ability to personalize the look and feel of these new features.

QuickSight has a serverless architecture that automatically scales your applications from a few to hundreds of thousands of users without the need to build, set up, and manage your own analytics infrastructure. These capabilities allow you to deliver embedded analytics at hyperscale. So, why does hyperscale matter? Traditional BI tools run on a fixed amount of hardware resources, therefore more users, more concurrency, or more complex queries impact performance across all users, which requires you to add more capacity (leading to higher costs).

The following diagram illustrates a traditional architecture, which requires additional servers (and higher upfront cost) to scale.

With QuickSight, you have access to the power and scale of the AWS Cloud. You get auto scaled, consistent performance no matter the concurrency or scale of the userbase, and a truly pay-per-use architecture, meaning you only pay when your users access the dashboards or reports. The following diagram illustrates how QuickSight scales seamlessly with its serverless architecture, powered by the AWS cloud.

Furthermore, QuickSight enables your users to perform machine learning based insights such as anomaly detection, forecasting, and natural language queries. It also has a rich set of APIs that allow you to programmatically manage your analytics workflows, such as moving dashboards across accounts, automating deployments, and managing access for users with single sign-on (SSO).

New features in QuickSight Embedded Analytics

We recently announced the launch of additional embedding capabilities that allow you to do even more with QuickSight embedded analytics. QuickSight now allows you to embed dashboard authoring within applications (such as SaaS applications and enterprise portals), allowing you to empower your end-users to create their own visualizations and reports.

These ad hoc data analysis and self-service data exploration capabilities mean you don’t have to repeatedly create custom dashboards based on requests from your end-users, and can provide end-users with even greater agility and transparency with their data. This capability helps create product differentiation and up-sell opportunities within customer applications.

With this launch, QuickSight also provides namespaces, a multi-tenant capability that allows you to easily maintain data isolation while supporting multiple workloads within the same QuickSight account. For example, if you’re an independent software vendor (ISV), you can now assign dedicated namespaces to different customers within the same QuickSight account. This allows you to securely manage multiple customer workloads as users (authors or readers) within one namespace, and they can only discover and share content with other users within the same namespace, without exposing any data to other parties.

Without namespaces, you could set up your own embedded dashboards for hundreds of thousands of users with QuickSight. For example, see the following dashboard for our fictional company, Oktank Analytica.

With namespaces in place, you can extend this to provide ad-hoc authoring capabilities using curated datasets specific to each customer, created and shared by the developer or ISV. See the following screenshot.

For more information about these new features, see Embed multi-tenant analytics in applications with Amazon QuickSight.

Customer success stories

Customers are already using embedded analytics in QuickSight to great success. In this section, we share the stories of a few customers.

Blackboard

Blackboard is a leading EdTech company, serving higher education, K-12, business, and government clients around the world.

“The recent wave in digital transformation in the global education community has made it clear that it’s time for a similar transformation in the education analytics tools that support that community,” says Rachel Scherer, Sr. Director of Data & Analytics at Blackboard. “We see a need to support learners, teachers, and leaders in education by helping to change their relationship with data and information—to reduce the distance between information and experience, between ‘informed’ and ‘acting.’

“A large part of this strategy involves embedding information directly where our users are collaborating, teaching, and learning—providing tools and insights that aid in assessment, draw attention to opportunities learners may be missing, and help strategic and academic leadership identify patterns and opportunities for intervention. We’re particularly interested in making the experience of being informed much more intuitive—favoring insight-informed workflows and/or embedded prose over traditional visualizations that require interpretation.

“By removing the step of interpretation, embedded visualizations make insights more useful and actionable. With QuickSight, we were able to deliver on our promise of embedding visualizations quickly, supporting the rapid iteration that we require, at the large scale needed to support our global user community.”

For more information about Blackboard’s QuickSight use case, see the AWS Online Tech Talk Embedding Analytics in your Applications with Amazon QuickSight at the 25:50 mark.

Comcast

Syndication Insights (SI) enables Comcast’s syndicated partners to access the same level of rich data insights that Comcast uses for platform and operational improvements.

“The SI platform enables partners to gain deeper business insights, such as early detection into anomalies for users, while ensuring a seamless experience through embedded, interactive reports,” says Ajay Gavagal, Sr. Manager of Software Development at Comcast. “From the start, scalability was a core requirement for us. We chose QuickSight as it is scalable, enabling SI to extend to multiple syndicated partners without having to provision or manage additional infrastructure. Furthermore, QuickSight provides interactive dashboards that can be easily embedded into an application. Lastly, QuickSight’s rich APIs abstract away a lot of functionality that would otherwise need to be custom built.”

For more information about how Comcast uses QuickSight, see the AWS Online Tech Talk Embedding Analytics in your Applications with Amazon QuickSight at the 38:05 mark.

Panasonic Avionics Corporation

Panasonic Avionics Corporation provides customized in-flight entertainment and communications systems to more than 300 airlines worldwide.

“Our cloud-based solutions collect large amounts of anonymized data that help us optimize the experience for both our airline partners and their passengers,” says Anand Desikan, Director of Cloud Operations at Panasonic Avionics Corporation. “We started using Amazon QuickSight to report on in-flight Wi-Fi performance, and with its rich APIs, pay-per-session pricing, and ability to scale, we quickly rolled out QuickSight dashboards to hundreds of users. The constant evolution of the platform has been impressive: ML-powered anomaly detection, Amazon SageMaker integration, embedding, theming, and cross-visual filtering. Our users consume insights via natural language narratives, which allows them to read all their information right off the dashboard with no complex interpretation needed.”

EHE Health

EHE Health is national preventive health and primary care Center of Excellence provider system.

“As a 106-year-old organization moving toward greater agility and marketplace nimbleness, we needed to drastically upgrade our ability to be transparent within our internal and external ecosystems,” says David Buza, Chief Technology Officer at EHE Health. “With QuickSight, we are not constrained by pre-built BI reports, and can easily customize and track the right operational metrics, such as product utilization, market penetration, and available inventory to gain a holistic view of our business. These inputs help us to understand current performance and future opportunity so that we can provide greater partnership to our clients, while delivering on our brand promise of creating healthier employee populations.

“QuickSight allowed our teams to seamlessly communicate with our clients—all viewing the same information, simultaneously. QuickSight’s embedding capabilities, along with its secure platform, intuitive design, and flexibility, allowed us to service all stakeholders—both internally and externally. This greater flexibility and customization allowed us to fit the client’s needs seamlessly.”

Conclusion

Where data agility and transparency are critical to business success, embedded analytics can open a universe of possibilities, and we are excited to see what our customers will do with these new capabilities.

Additional resources

For more resources, see the following:

• Amazon QuickSight Embedded Analytics Demo (Video)
• Amazon QuickSight Gallery (Embedded samples)
• Embedding the Full Functionality of the Amazon QuickSight Console (Documentation)
• Supporting Multitenancy with Isolated Namespaces (Documentation)
• Customizing Access to the Amazon QuickSight Console (Documentation)
• Embed Amazon QuickSight (Hands-on tutorial)
• Tools to Build on AWS (AWS SDK)
• Amazon QuickSight Embedding SDK (JavaScript SDK)

About the Author

Dorothy Li is the Vice President and General Manager for Amazon QuickSight.