Post Syndicated from Ajay Vohra original https://aws.amazon.com/blogs/big-data/automate-secure-access-to-amazon-mwaa-environments-using-existing-openid-connect-single-sign-on-authentication-and-authorization/
Customers use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run Apache Airflow at scale in the cloud. They want to use their existing login solutions developed using OpenID Connect (OIDC) providers with Amazon MWAA; this allows them to provide a uniform authentication and single sign-on (SSO) experience using their adopted identity providers (IdP) across AWS services. For ease of use for end-users of Amazon MWAA, organizations configure a custom domain endpoint to their Apache Airflow UI endpoint. For teams operating and managing multiple Amazon MWAA environments, securing and customizing each environment is a repetitive but necessary task. Automation through infrastructure as code (IaC) can alleviate this heavy lifting to achieve consistency at scale.
This post describes how you can integrate your organization’s existing OIDC-based IdPs with Amazon MWAA to grant secure access to your existing Amazon MWAA environments. Furthermore, you can use the solution to provision new Amazon MWAA environments with the built-in OIDC-based IdP integrations. This approach allows you to securely provide access to your new or existing Amazon MWAA environments without requiring AWS credentials for end-users.
Overview of Amazon MWAA environments
Managing multiple user names and passwords can be difficult—this is where SSO authentication and authorization comes in. OIDC is a widely used standard for SSO, and it’s possible to use OIDC SSO authentication and authorization to access Apache Airflow UI across multiple Amazon MWAA environments.
When you provision an Amazon MWAA environment, you can choose public or private Apache Airflow UI access mode. Private access mode is typically used by customers that require restricting access from only within their virtual private cloud (VPC). When you use public access mode, the access to the Apache Airflow UI is available from the internet, in the same way as an AWS Management Console page. Internet access is needed when access is required outside of a corporate network.
Regardless of the access mode, authorization to the Apache Airflow UI in Amazon MWAA is integrated with AWS Identity and Access Management (IAM). All requests made to the Apache Airflow UI need to have valid AWS session credentials with an assumed IAM role that has permissions to access the corresponding Apache Airflow environment. For more details on the permissions policies needed to access the Apache Airflow UI, refer to Apache Airflow UI access policy: AmazonMWAAWebServerAccess.
Different user personas such as developers, data scientists, system operators, or architects in your organization may need access to the Apache Airflow UI. In some organizations, not all employees have access to the AWS console. It’s fairly common that employees who don’t have AWS credentials may also need access to the Apache Airflow UI that Amazon MWAA exposes.
In addition, many organizations have multiple Amazon MWAA environments. It’s common to have an Amazon MWAA environment setup per application or team. Each of these Amazon MWAA environments can be run in different deployment environments like development, staging, and production. For large organizations, you can easily envision a scenario where there is a need to manage multiple Amazon MWAA environments. Organizations need to provide secure access to all of their Amazon MWAA environments using their existing OIDC provider.
Solution Overview
The solution architecture integrates an existing OIDC provider to provide authentication for accessing the Amazon MWAA Apache Airflow UI. This allows users to log in to the Apache Airflow UI using their OIDC credentials. From a system perspective, this means that Amazon MWAA can integrate with an existing OIDC provider rather than having to create and manage an isolated user authentication and authorization through IAM internally.
The solution architecture relies on an Application Load Balancer (ALB) setup with a fully qualified domain name (FQDN) with public (internet) or private access. This ALB provides SSO access to multiple Amazon MWAA environments. The user-agent (web browser) call flow for accessing an Apache Airflow UI console to the target Amazon MWAA environment includes the following steps:
- The user-agent resolves the ALB domain name from the Domain Name System (DNS) resolver.
- The user-agent sends a login request to the ALB path
/aws_mwaa/aws-console-ssowith a set of query parameters populated. The request uses the required parametersmwaa_envandrbac_roleas placeholders for the target Amazon MWAA environment and the Apache Airflow role-based access control (RBAC) role, respectively. - Once it receives the request, the ALB redirects the user-agent to the OIDC IdP authentication endpoint. The user-agent authenticates with the OIDC IdP with the existing user name and password.
- If user authentication is successful, the OIDC IdP redirects the user-agent back to the configured ALB with a
redirect_urlwith the authorization code included in the URL. - The ALB uses the authorization code received to obtain the
access_tokenand OpenID JWT token withopenid emailscope from the OIDC IdP. It then forwards the login request to the Amazon MWAA authenticator AWS Lambda function with the JWT token included in the request header in thex-amzn-oidc-dataparameter. - The Lambda function verifies the JWT token found in the request header using ALB public keys. The function subsequently authorizes the authenticated user for the requested
mwaa_envandrbac_rolestored in an Amazon DynamoDB table. The use of DynamoDB for authorization here is optional; the Lambda code functionis_allowedcan be customized to use other authorization mechanisms. - The Amazon MWAA authenticator Lambda function redirects the user-agent to the Apache Airflow UI console in the requested Amazon MWAA environment with the login token in the redirect URL. Additionally, the function provides the logout functionality.
Amazon MWAA public network access mode
For the Amazon MWAA environments configured with public access mode, the user agent uses public routing over the internet to connect to the ALB hosted in a public subnet.
The following diagram illustrates the solution architecture with a numbered call flow sequence for internet network reachability.
Amazon MWAA private network access mode
For Amazon MWAA environments configured with private access mode, the user agent uses private routing over a dedicated AWS Direct Connect or AWS Client VPN to connect to the ALB hosted in a private subnet.
The following diagram shows the solution architecture for Client VPN network reachability.
Automation through infrastructure as code
To make setting up this solution easier, we have released a pre-built solution that automates the tasks involved. The solution has been built using the AWS Cloud Development Kit (AWS CDK) using the Python programming language. The solution is available in our GitHub repository and helps you achieve the following:
- Set up a secure ALB to provide OIDC-based SSO to your existing Amazon MWAA environment with default Apache Airflow Admin role-based access.
- Create new Amazon MWAA environments along with an ALB and an authenticator Lambda function that provides OIDC-based SSO support. With the customization provided, you can define the number of Amazon MWAA environments to create. Additionally, you can customize the type of Amazon MWAA environments created, including defining the hosting VPC configuration, environment name, Apache Airflow UI access mode, environment class, auto scaling, and logging configurations.
The solution offers a number of customization options, which can be specified in the cdk.context.json file. Follow the setup instructions to complete the integration to your existing Amazon MWAA environments or create new Amazon MWAA environments with SSO enabled. The setup process creates an ALB with an HTTPS listener that provides the user access endpoint. You have the option to define the type of ALB that you need. You can define whether your ALB will be public facing (internet accessible) or private facing (only accessible within the VPC). It is recommended to use a private ALB with your new or existing Amazon MWAA environments configured using private UI access mode.
The following sections describe the specific implementation steps and customization options for each use case.
Prerequisites
Before you continue with the installation steps, make sure you have completed all prerequisites and run the setup-venv script as outlined within the README.md file of the GitHub repository.
Integrate to a single existing Amazon MWAA environment
If you’re integrating with a single existing Amazon MWAA environment, follow the guides in the Quick start section. You must specify the same ALB VPC as that of your existing Amazon MWAA VPC. You can specify the default Apache Airflow RBAC role that all users will assume. The ALB with an HTTPS listener is configured within your existing Amazon MWAA VPC.
Integrate to multiple existing Amazon MWAA environments
To connect to multiple existing Amazon MWAA environments, specify only the Amazon MWAA environment name in the JSON file. The setup process will create a new VPC with subnets hosting the ALB and the listener. You must define the CIDR range for this ALB VPC such that it doesn’t overlap with the VPC CIDR range of your existing Amazon MWAA VPCs.
When the setup steps are complete, implement the post-deployment configuration steps. This includes adding the ALB CNAME record to the Amazon Route 53 DNS domain.
For integrating with Amazon MWAA environments configured using private access mode, there are additional steps that need to be configured. These include configuring VPC peering and subnet routes between the new ALB VPC and the existing Amazon MWAA VPC. Additionally, you need to configure network connectivity from your user-agent to the private ALB endpoint resolved by your DNS domain.
Create new Amazon MWAA environments
You can configure the new Amazon MWAA environments you want to provision through this solution. The cdk.context.json file defines a dictionary entry in the MwaaEnvironments array. Configure the details that you need for each of the Amazon MWAA environments. The setup process creates an ALB VPC, ALB with an HTTPS listener, Lambda authorizer function, DynamoDB table, and respective Amazon MWAA VPCs and Amazon MWAA environments in them. Furthermore, it creates the VPC peering connection between the ALB VPC and the Amazon MWAA VPC.
If you want to create Amazon MWAA environments with private access mode, the ALB VPC CIDR range specified must not overlap with the Amazon MWAA VPC CIDR range. This is required for the automatic peering connection to succeed. It can take between 20–30 minutes for each Amazon MWAA environment to finish creating.
When the environment creation processes are complete, run the post-deployment configuration steps. One of the steps here is to add authorization records to the created DynamoDB table for your users. You need to define the Apache Airflow rbac_role for each of your end-users, which the Lambda authorizer function matches to provide the requisite access.
Verify access
Once you’ve completed with the post-deployment steps, you can log in to the URL using your ALB FQDN. For example, If your ALB FQDN is alb-sso-mwaa.example.com, you can log in to your target Amazon MWAA environment, named Env1, assuming a specific Apache Airflow RBAC role (such as Admin), using the following URL: https://alb-sso-mwaa.example.com/aws_mwaa/aws-console-sso?mwaa_env=Env1&rbac_role=Admin. For the Amazon MWAA environments that this solution created, you need to have appropriate Apache Airflow rbac_role entries in your DynamoDB table.
The solution also provides a logout feature. To log out from an Apache Airflow console, use the normal Apache Airflow console logout. To log out from the ALB, you can, for example, use the URL https://alb-sso-mwaa.example.com/logout.
Clean up
Follow the readme documented steps in the section Destroy CDK stacks in the GitHub repo, which shows how to clean up the artifacts created via the AWS CDK deployments. Remember to revert any manual configurations, like VPC peering connections, that you might have made after the deployments.
Conclusion
This post provided a solution to integrate your organization’s OIDC-based IdPs with Amazon MWAA to grant secure access to multiple Amazon MWAA environments. We walked through the solution that solves this problem using infrastructure as code. This solution allows different end-user personas in your organization to access the Amazon MWAA Apache Airflow UI using OIDC SSO.
To use the solution for your own environments, refer to Application load balancer single-sign-on for Amazon MWAA. For additional code examples on Amazon MWAA, refer to Amazon MWAA code examples.
About the Authors
Ajay Vohra is a Principal Prototyping Architect specializing in perception machine learning for autonomous vehicle development. Prior to Amazon, Ajay worked in the area of massively parallel grid-computing for financial risk modeling.
Jaswanth Kumar is a customer-obsessed Cloud Application Architect at AWS in NY. Jaswanth excels in application refactoring and migration, with expertise in containers and serverless solutions, coupled with a Masters Degree in Applied Computer Science.
Aneel Murari is a Sr. Serverless Specialist Solution Architect at AWS based in the Washington, D.C. area. He has over 18 years of software development and architecture experience and holds a graduate degree in Computer Science. Aneel helps AWS customers orchestrate their workflows on Amazon Managed Apache Airflow (MWAA) in a secure, cost effective and performance optimized manner.
Parnab Basak is a Solutions Architect and a Serverless Specialist at AWS. He specializes in creating new solutions that are cloud native using modern software development practices like serverless, DevOps, and analytics. Parnab works closely in the analytics and integration services space helping customers adopt AWS services for their workflow orchestration needs.











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.

Nitin Arora is a Sr. Software Development Manager for Finance Automation in Amazon. He has over 18 years of experience building business critical, scalable, high-performance software. Nitin leads several data and analytics initiatives within Finance, which includes building Data Mesh. In his spare time, he enjoys listening to music and read.
Pradeep Misra is a Specialist Solutions Architect at AWS. He works across Amazon to architect and design modern distributed analytics and AI/ML platform solutions. He is passionate about solving customer challenges using data, analytics, and AI/ML. Outside of work, Pradeep likes exploring new places, trying new cuisines, and playing board games with his family. He also likes doing science experiments with his daughters.
Rajesh Rao is a Sr. Technical Program Manager in Amazon Finance. He works with Data Services teams within Amazon to build and deliver data processing and data analytics solutions for Financial Operations teams. He is passionate about delivering innovative and optimal solutions using AWS to enable data-driven business outcomes for his customers.
Andrew Long, the lead developer for data mesh, has designed and built many of the big data processing systems that have fueled Amazon’s financial data processing infrastructure. His work encompasses a range of areas, including S3-based table formats for Spark, diverse Spark performance optimizations, distributed orchestration engines and the development of data cataloging systems. Additionally, Andrew finds pleasure in sharing his knowledge of partner acrobatics.
Kumar Satyen Gaurav, is an experienced Software Development Manager at Amazon, with over 16 years of expertise in big data analytics and software development. He leads a team of engineers to build products and services using AWS big data technologies, for providing key business insights for Amazon Finance Operations across diverse business verticals. Beyond work, he finds joy in reading, traveling and learning strategic challenges of chess.


Zygimantas Koncius is an engineer at Etleap with 3 years of experience in developing robust and performant ETL software. In addition to development work, he maintains Etleap infrastructure and provides deep-level technical customer support.
Sudhir Gupta is a Principal Partner Solutions Architect, Analytics Specialist at AWS with over 18 years of experience in Databases and Analytics. He helps AWS partners and customers design, implement, and migrate large-scale data & analytics (D&A) workloads. As a trusted advisor to partners, he enables partners globally on AWS D&A services, builds solutions/accelerators, and leads go-to-market initiatives.




















































Rohit Vashishtha is a Senior Analytics Specialist Solutions Architect at AWS based in Dallas, Texas. He has 17 years of experience architecting, building, leading, and maintaining big data platforms. Rohit helps customers modernize their analytic workloads using the breadth of AWS services and ensures that customers get the best price/performance with utmost security and data governance.
Vijay Karumajji is a Database Solutions Architect with Amazon Web Services. He works with AWS customers to provide guidance and technical assistance on database projects, helping them improve the value of their solutions when using AWS.
BP Yau is a Sr Partner Solutions Architect at AWS. He is passionate about helping customers architect big data solutions to process data at scale. Before AWS, he helped Amazon.com Supply Chain Optimization Technologies migrate its Oracle data warehouse to Amazon Redshift and build its next generation big data analytics platform using AWS technologies.
Jyoti Aggarwal is a Product Manager on the Amazon Redshift team based in Seattle. She has spent the last 10 years working on multiple products in the data warehouse industry.
Adam Levin is a Product Manager on the Amazon Aurora team based in California. He has spent the last 10 years working on various cloud database services.
Nicolas Jacob Baer is a Senior Cloud Application Architect with a strong focus on data engineering and machine learning, based in Switzerland. He works closely with enterprise customers to design data platforms and build advanced analytics/ml use-cases.
Luca Mazzaferro is a Senior DevOps Architect at Amazon Web Services. He likes to have infrastructure automated, reproducible and secured. In his free time he likes to cook, especially pizza.
Kemeng Zhang is a Cloud Application Architect with a strong focus on machine learning and UX, based in Switzerland. She works closely with customers to design user experiences and build advanced analytics/ml use-cases.
Mark Walser, a Senior Global Data Architect at Amazon Web Services, collaborates with customers to develop innovative Big Data solutions that solve business problems and speed up the adoption of AWS services. Outside of work, he finds pleasure in running, swimming, and all things related to technology.
Gal Heyne is a Product Manager for AWS Glue with a strong focus on AI/ML, data engineering and BI, based in California. She is passionate about developing a deep understanding of customer’s business needs and collaborating with engineers to design easy to use data products.
























Anderson Santos is a Senior Solutions Architect at Amazon Web Services. He works with AWS Enterprise customers to provide guidance and technical assistance, helping them improve the value of their solutions when using AWS.
Arun Pradeep Selvaraj is a Senior Solutions Architect and is part of Analytics TFC at AWS. Arun is passionate about working with his customers and stakeholders on digital transformations and innovation in the cloud while continuing to learn, build and reinvent. He is creative, fast-paced, deeply customer-obsessed and leverages the working backwards process to build modern architectures to help customers solve their unique challenges.
Patrick Muller is a Senior Solutions Architect and a valued member of the Datalab. With over 20 years of expertise in analytics, data warehousing, and distributed systems, he brings extensive knowledge to the table. Patrick’s passion lies in evaluating new technologies and assisting customers with innovative solutions. During his free time, he enjoys watching soccer.




Jon Handler is a Senior Principal Solutions Architect at Amazon Web Services based in Palo Alto, CA. Jon works closely with OpenSearch and Amazon OpenSearch Service, providing help and guidance to a broad range of customers who have search and log analytics workloads that they want to move to the AWS Cloud. Prior to joining AWS, Jon’s career as a software developer included four years of coding a large-scale, eCommerce search engine. Jon holds a Bachelor of the Arts from the University of Pennsylvania, and a Master of Science and a Ph. D. in Computer Science and Artificial Intelligence from Northwestern University.
Jianwei Li is a Principal Analytics Specialist TAM at Amazon Web Services. Jianwei provides consultant service for customers to help customer design and build modern data platform. Jianwei has been working in big data domain as software developer, consultant and tech leader.
Dylan Tong is a Senior Product Manager at AWS. He works with customers to help drive their success on the AWS platform through thought leadership and guidance on designing well architected solutions. He has spent most of his career building on his expertise in data management and analytics by working for leaders and innovators in the space.
Vamshi Vijay Nakkirtha is a Software Engineering Manager working on the OpenSearch Project and Amazon OpenSearch Service. His primary interests include distributed systems. He is an active contributor to various plugins, like k-NN, GeoSpatial, and dashboard-maps.










Srividya Parthasarathy is a Senior Big Data Architect on the AWS Lake Formation team. She enjoys building data mesh solutions and sharing them with the community.
Sandeep Adwankar is a Senior Technical Product Manager at AWS. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that enable customers to improve how they manage, secure, and access data.

Ketan Verma is a Senior SDE working on Amazon OpenSearch Service. He is passionate about building large-scale distributed systems, improving performance, and simplifying complex ideas with simple abstractions. Outside work, he likes to read and improve his home barista skills.
Suresh N S is a Senior SDE working on Amazon OpenSearch Service. He is passionate towards solving problems in large scale distributed systems.
Pritkumar Ladani is an SDE-2 working on Amazon OpenSearch Service. He likes to contribute to open source software development, and is passionate about distributed systems. He is an amateur badminton player and enjoys trekking.
Bukhtawar Khan is a Principal Engineer working on Amazon OpenSearch Service. He is interested in building distributed and autonomous systems. He is a maintainer and an active contributor to OpenSearch.





































Ayah Chamseddin is a Sr. Engagement Manager at AWS. She has a deep understanding of cloud technologies and has successfully overseen and lead strategic projects, partnering with clients to define business objectives, develop implementation strategies, and drive the successful delivery of solutions.
Vamsi Bhadriraju is a Data Architect at AWS. He works closely with enterprise customers to build data lakes and analytical applications on the AWS Cloud.
Srikanth Baheti is a Specialized World Wide Principal Solutions Architect for Amazon QuickSight. He started his career as a consultant and worked for multiple private and government organizations. Later he worked for PerkinElmer Health and Sciences & eResearch Technology Inc, where he was responsible for designing and developing high traffic web applications, highly scalable and maintainable data pipelines for reporting platforms using AWS services and Serverless computing.
Raji Sivasubramaniam is a Sr. Solutions Architect at AWS, focusing on Analytics. Raji is specialized in architecting end-to-end Enterprise Data Management, Business Intelligence and Analytics solutions for Fortune 500 and Fortune 100 companies across the globe. She has in-depth experience in integrated healthcare data and analytics with wide variety of healthcare datasets including managed market, physician targeting and patient analytics.