Amazon Managed Workflows for Apache Airflow (Amazon MWAA) now supports DuckDB operators. DuckDB is an embedded analytical database that lets customers perform data quality checks, summaries, and transformations on data from sources DuckDB supports, including Amazon S3. Native support is available for DuckDB on Amazon MWAA Provisioned environments running Apache Airflow 3.3.1. DuckDB comes preinstalled and preconfigured for AWS connections in the latest MWAA Airflow 3.3.1 update. After updating their MWAA environment to Airflow 3.3.1 and adding execution role access to their data stores, customers can start writing tasks with DuckDBExecuteQueryOperator with no additional setup. This feature is available in all AWS Regions where Amazon MWAA is available. For more information, see the https://docs.aws.amazon.com/mwaa/latest/userguide/get-started.html#regions. To learn more, see https://docs.aws.amazon.com/mwaa/latest/userguide/samples-duckdb.html in the Amazon MWAA User Guide.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) now supports DuckDB operators. DuckDB is an embedded analytical database that lets customers perform data quality checks, summaries, and transformations on data from sources DuckDB supports, including Amazon S3. Native support is available for DuckDB on Amazon MWAA Provisioned environments running Apache Airflow 3.3.1. DuckDB comes preinstalled and preconfigured for AWS connections in the latest MWAA Airflow 3.3.1 update. After updating their MWAA environment to Airflow 3.3.1 and adding execution role access to their data stores, customers can start writing tasks with DuckDBExecuteQueryOperator with no additional setup. This feature is available in all AWS Regions where Amazon MWAA is available. For more information, see the AWS Region table. To learn more, see Using DuckDB with Amazon MWAA in the Amazon MWAA User Guide.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports Apache Airflow connections and variables, so your workflows can reach databases, APIs, and other external systems. You can manage credentials and configuration outside your workflow code. You store sensitive connections and variables in the secrets backend of your choice, such as AWS Secrets Manager, AWS Systems Manager Parameter Store, or your own custom backend, configure it on your workflow, and grant your workflow execution role access. Tasks retrieve them at run time through standard Apache Airflow methods. You can define non-secret values as environment variables on the workflow. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. For more information, see Amazon MWAA Serverless Regions. To learn more, see Managing connections and variables and Using environment variables in Amazon MWAA Serverless workflows in the Amazon MWAA Serverless User Guide.

Amazon MWAA Serverless now supports operators from Apache Airflow provider packages, such as HTTP, Snowflake, or dbt, and custom operators that you write. This gives data engineers the flexibility to integrate with more systems and build specialized workflow tasks without provisioning infrastructure. To get started, add the provider package or your own operator to a code package, upload it to Amazon S3, and reference the operator in your workflow definition when you create or update a workflow. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. For a list of supported Regions, see Amazon MWAA Serverless Regions. To learn more, see Running custom code with Python, Bash, and custom operators in the Amazon MWAA Serverless User Guide.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports Apache Airflow connections and variables, so your workflows can reach databases, APIs, and other external systems. You can manage credentials and configuration outside your workflow code. You store sensitive connections and variables in the secrets backend of your choice, such as AWS Secrets Manager, AWS Systems Manager Parameter Store, or your own custom backend, configure it on your workflow, and grant your workflow execution role access. Tasks retrieve them at run time through standard Apache Airflow methods. You can define non-secret values as environment variables on the workflow. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. For more information, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/what-is-mwaa-serverless.html#regions. To learn more, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/connections.html and https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/workflows-environment-variables.html in the Amazon MWAA Serverless User Guide.

Amazon MWAA Serverless now supports operators from Apache Airflow provider packages, such as HTTP, Snowflake, or dbt, and custom operators that you write. This gives data engineers the flexibility to integrate with more systems and build specialized workflow tasks without provisioning infrastructure. To get started, add the provider package or your own operator to a code package, upload it to Amazon S3, and reference the operator in your workflow definition when you create or update a workflow. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. For a list of supported Regions, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/what-is-mwaa-serverless.html#regions. To learn more, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/custom-code-operators.html in the Amazon MWAA Serverless User Guide.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) now supports https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html. Tags you apply to an Amazon MWAA environment now propagate to the compute that runs it, including workers, schedulers, and web servers. You can then track Amazon MWAA spend by tag in AWS Cost Explorer and AWS Cost and Usage Reports. With this launch, platform teams can show back or charge back Airflow costs to the team, project, or cost center that owns each environment. You can use cost allocation tags when you create a new environment or update an existing one. To get started, tag your environment using the Amazon MWAA console, AWS CLI, or Amazon MWAA API, then activate the tag key as a cost allocation tag in the AWS Billing and Cost Management console. This feature is available in all AWS Regions where Amazon MWAA is available. For more information, see the https://docs.aws.amazon.com/mwaa/latest/userguide/get-started.html#regions. To learn more, visit https://docs.aws.amazon.com/mwaa/latest/userguide/cost-allocation-tags.html in the Amazon MWAA User Guide.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) now supports cost allocation tags. Tags you apply to an Amazon MWAA environment now propagate to the compute that runs it, including workers, schedulers, and web servers. You can then track Amazon MWAA spend by tag in AWS Cost Explorer and AWS Cost and Usage Reports. With this launch, platform teams can show back or charge back Airflow costs to the team, project, or cost center that owns each environment. You can use cost allocation tags when you create a new environment or update an existing one. To get started, tag your environment using the Amazon MWAA console, AWS CLI, or Amazon MWAA API, then activate the tag key as a cost allocation tag in the AWS Billing and Cost Management console. This feature is available in all AWS Regions where Amazon MWAA is available. For more information, see the AWS Region table. To learn more, visit Cost allocation tags in Amazon MWAA in the Amazon MWAA User Guide.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Provisioned environments are now available in the Mexico (Central) Region (mx-central-1). Amazon MWAA Serverless was already available in this Region, and customers can now also use Amazon MWAA Provisioned environments for their Apache Airflow workflows. Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier for customers to set up and operate end-to-end data pipelines in the cloud at scale. Customers can create Amazon MWAA Provisioned environments using the AWS Management Console, the AWS CLI, the AWS SDKs, or AWS CloudFormation. Customers in Mexico (Central) can now run their Amazon MWAA Provisioned workflows closer to their data sources and end users, reducing latency and helping meet data residency requirements. To learn more, visit the Amazon MWAA product page and the Amazon MWAA supported Regions documentation.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Provisioned environments are now available in the Mexico (Central) Region (mx-central-1). Amazon MWAA Serverless was already available in this Region, and customers can now also use Amazon MWAA Provisioned environments for their Apache Airflow workflows. Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier for customers to set up and operate end-to-end data pipelines in the cloud at scale. Customers can create Amazon MWAA Provisioned environments using the AWS Management Console, the AWS CLI, the AWS SDKs, or AWS CloudFormation. Customers in Mexico (Central) can now run their Amazon MWAA Provisioned workflows closer to their data sources and end users, reducing latency and helping meet data residency requirements. To learn more, visit the https://aws.amazon.com/managed-workflows-for-apache-airflow/ and the https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html#regions-mwaa.

Amazon Managed Workflows for Apache Airflow (MWAA) now integrates with the Agent Toolkit for AWS to provide built-in AI agent skills that let you author, debug, and test MWAA workflows and upgrade MWAA environments using any AI coding assistant that supports agent skills. The four skills are authoring-mwaa-workflow, debugging-mwaa-workflow, testing-mwaa-workflow, and upgrading-mwaa-environments. Together they let your AI assistant interact directly with MWAA using natural language, reducing the manual effort of building and maintaining data pipelines. For example, you can describe an ETL pipeline in plain language and the authoring skill generates a working DAG complete with task dependencies and error handling. The Amazon MWAA AI agent skills work with both Amazon MWAA Provisioned and Amazon MWAA Serverless environments, and are available in all AWS Regions where Amazon MWAA is available. To get started, install the aws-data-analytics plugin and configure your AI coding assistant to use the MWAA agent skills. To learn more, see Using agent skills with Amazon MWAA in the Amazon MWAA User Guide and the Agent Toolkit for AWS documentation.

Amazon Managed Workflows for Apache Airflow (MWAA) now integrates with the https://docs.aws.amazon.com/agent-toolkit/latest/userguide/what-is-agent-toolkit.html to provide built-in AI agent skills that let you author, debug, and test MWAA workflows and upgrade MWAA environments using any AI coding assistant that supports agent skills. The four skills are authoring-mwaa-workflow, debugging-mwaa-workflow, testing-mwaa-workflow, and upgrading-mwaa-environments. Together they let your AI assistant interact directly with MWAA using natural language, reducing the manual effort of building and maintaining data pipelines. For example, you can describe an ETL pipeline in plain language and the authoring skill generates a working DAG complete with task dependencies and error handling. The Amazon MWAA AI agent skills work with both Amazon MWAA Provisioned and Amazon MWAA Serverless environments, and are available in all AWS Regions where Amazon MWAA is available. To get started, install the https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics#agent-skills and configure your AI coding assistant to use the MWAA agent skills. To learn more, see https://docs.aws.amazon.com/mwaa/latest/userguide/using-agent-skills.html in the Amazon MWAA User Guide and the https://docs.aws.amazon.com/agent-toolkit/.

Amazon Managed Workflows for Apache Airflow (MWAA) Serverless is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West). Amazon MWAA Serverless eliminates the operational overhead of managing Apache Airflow infrastructure by automatically provisioning and scaling compute resources on demand, with a pay-per-use model that charges only for actual workflow run time. For a full list of supported regions, see the https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/what-is-mwaa-serverless.html#regions. To learn more, see the https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/what-is-mwaa-serverless.html.

Amazon Managed Workflows for Apache Airflow (MWAA) Serverless is now available in AWS GovCloud (US-East) and AWS GovCloud (US-West). Amazon MWAA Serverless eliminates the operational overhead of managing Apache Airflow infrastructure by automatically provisioning and scaling compute resources on demand, with a pay-per-use model that charges only for actual workflow run time. For a full list of supported regions, see the Amazon MWAA Serverless regions page. To learn more, see the Amazon MWAA Serverless documentation.

Amazon Managed Workflows for Apache Airflow (MWAA) now includes a built-in monitoring experience on the environment detail page in the AWS Management Console. A new metrics dashboard displays key Amazon CloudWatch metrics for your environment in one place, and each graph includes an optional toggle to overlay suggested warning ranges, helping you quickly identify conditions that may affect your environment's health and performance. The environment detail page also now shows an alarms table that lists all Amazon CloudWatch alarms associated with your MWAA environment. You can use the one-click Create Recommended Alarms action to provision a curated set of alarms from an AWS-managed template, so you can start monitoring critical metrics without having to configure each alarm manually. This feature is available for Amazon MWAA Provisioned environments in all regions where Amazon MWAA is available. Standard Amazon CloudWatch pricing applies for metric queries and alarms. To get started, open the AWS Management Console, review the Amazon MWAA supported regions, or visit the Amazon MWAA documentation to learn more.

Amazon Managed Workflows for Apache Airflow (MWAA) now includes a built-in monitoring experience on the environment detail page in the https://console.aws.amazon.com/mwaa/home. A new metrics dashboard displays key Amazon CloudWatch metrics for your environment in one place, and each graph includes an optional toggle to overlay suggested warning ranges, helping you quickly identify conditions that may affect your environment's health and performance. The environment detail page also now shows an alarms table that lists all Amazon CloudWatch alarms associated with your MWAA environment. You can use the one-click Create Recommended Alarms action to provision a curated set of alarms from an AWS-managed template, so you can start monitoring critical metrics without having to configure each alarm manually. This feature is available for Amazon MWAA Provisioned environments in all regions where https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. Standard Amazon CloudWatch pricing applies for metric queries and alarms. To get started, open the https://console.aws.amazon.com/mwaa/home, review the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/, or visit the https://docs.aws.amazon.com/mwaa/latest/userguide/monitoring-dashboard.html to learn more.  

Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 3.3.1, the latest release of the popular open-source workflow orchestration framework. Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud. Apache Airflow 3.3 introduces stateful tasks and multi-language support. With the new Task and Asset State Store, tasks can now persist durable state across retries and reruns, enabling cursor tracking and crash-safe reconnection to long-running jobs. The Language Task SDK (experimental) lets teams write task logic in Java or Go while keeping orchestration in Python. Additional improvements include expanded asset partitioning, pluggable retry policies, and bulk actions for DAG runs and task instances. Apache Airflow 3.3.1 also delivers stability, security, and UI improvements on top of these capabilities. You can launch a new Apache Airflow 3.3.1 environment on Amazon MWAA, or upgrade from 3.2 or later, with a few clicks in the AWS Management Console in all currently supported Amazon MWAA regions. To learn more about Apache Airflow 3.3.1, visit the Amazon MWAA documentation and the Apache Airflow 3.3.1 change log in the Apache Airflow documentation. Apache, Apache Airflow, and Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.

Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 3.3.1, the latest release of the popular open-source workflow orchestration framework. Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud. Apache Airflow 3.3 introduces stateful tasks and multi-language support. With the new Task and Asset State Store, tasks can now persist durable state across retries and reruns, enabling cursor tracking and crash-safe reconnection to long-running jobs. The Language Task SDK (experimental) lets teams write task logic in Java or Go while keeping orchestration in Python. Additional improvements include expanded asset partitioning, pluggable retry policies, and bulk actions for DAG runs and task instances. Apache Airflow 3.3.1 also delivers stability, security, and UI improvements on top of these capabilities. You can launch a new Apache Airflow 3.3.1 environment on Amazon MWAA, or upgrade from 3.2 or later, with a few clicks in the https://console.aws.amazon.com/mwaa/home in all currently supported https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. To learn more about Apache Airflow 3.3.1, visit the https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html and the https://airflow.apache.org/docs/apache-airflow/3.3.1/release_notes.html#airflow-3-3-1-2026-08-12 in the Apache Airflow documentation. Apache, Apache Airflow, and Airflow are either registered trademarks or trademarks of the https://www.apache.org/ in the United States and/or other countries.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports running custom Python functions and shell scripts directly in the serverless runtime using PythonOperator and BashOperator. With this launch, data engineering teams can execute the code patterns they rely on daily, including data transformations, format conversions, and data quality checks, without provisioning additional infrastructure. Package your Python modules or shell scripts as code packages, upload them to Amazon S3, and reference them when creating or updating a workflow. The service snapshots your code at workflow creation time and uses that snapshot for all subsequent runs, ensuring consistency across executions. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. To learn more, visit https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/operators-python-bash-detail.html.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports running custom Python functions and shell scripts directly in the serverless runtime using PythonOperator and BashOperator. With this launch, data engineering teams can execute the code patterns they rely on daily, including data transformations, format conversions, and data quality checks, without provisioning additional infrastructure. Package your Python modules or shell scripts as code packages, upload them to Amazon S3, and reference them when creating or updating a workflow. The service snapshots your code at workflow creation time and uses that snapshot for all subsequent runs, ensuring consistency across executions. This feature is available in all AWS Regions where Amazon MWAA Serverless is available. To learn more, visit Using Python and Bash operators.

こんにちはmuroですAWS事業本部サービス開発室でopswitchの開発・運用を担当していますopswitchは今年の月にApacheAirflowベースのアーキテクチャからAWSStepFuncti

Amazon Managed Workflows for Apache Airflow (MWAA) Serverless now supports workflow and task state change events to https://aws.amazon.com/eventbridge/, enabling data engineering and platform teams to build event-driven automation for their Apache Airflow workflows. Previously, monitoring workflow execution required custom polling logic or manual observation. With this launch, MWAA Serverless can emit events when workflows transition between states, including started, running, succeeded, or failed, and when individual tasks change state, such as scheduled, succeeded, failed, or up for retry. With this feature, you can further automate your existing workflows - for example, using EventBridge notifications to trigger alerts when a production workflow fails, automatically restart dependent pipelines when an upstream workflow succeeds, or log state transitions to Amazon S3 for compliance and auditing. This feature is available in all AWS Regions where https://aws.amazon.com/managed-workflows-for-apache-airflow/ is available. For the complete list of supported Regions, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/what-is-mwaa-serverless.html#mwaa-serverless-regions in the Amazon MWAA Serverless User Guide. For pricing details, see https://aws.amazon.com/eventbridge/pricing/. To learn more, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/monitoring.html in the Amazon MWAA Serverless User Guide and https://docs.aws.amazon.com/eventbridge/latest/ref/events-ref-airflow-serverless.html in the Amazon EventBridge Events Reference.

Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 2.11.2. Amazon MWAA is a managed service that runs Apache Airflow at scale without the operational overhead of managing the underlying infrastructure. Apache Airflow 2.11.2 is a maintenance release that includes security improvements, bug fixes, and dependency upgrades. This release upgrades core dependencies with security patches and stability improvements to the Airflow webserver and task execution layers. It also includes fixes to task lifecycle management for queued tasks, enhanced secrets masking in logs, UI corrections in the Task Instances list view, and provider package updates for S3 and CloudWatch log delivery. You can create a new Apache Airflow 2.11.2 environment on Amazon MWAA or upgrade your existing environments with a few clicks in the AWS Management Console in all currently available Amazon MWAA regions. To learn more, visit the https://docs.aws.amazon.com/mwaa/latest/userguide/what-is-mwaa.html, review the https://airflow.apache.org/docs/apache-airflow/2.11.2/release_notes.html#airflow-2-11-2-2026-03-14, and explore the https://docs.aws.amazon.com/mwaa/latest/userguide/airflow-versions.html.

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports shared VPC subnets. Previously, customers using subnets shared via AWS Resource Access Manager (AWS RAM) received a validation error when creating MWAA Serverless workflows. With this update, MWAA Serverless correctly validates subnet ownership in shared VPC configurations, consistent with MWAA Provisioned environments. Sharing VPC subnets across accounts using AWS RAM is a common pattern in multi-account landing zone architectures. Organizations that centrally manage networking can now launch MWAA Serverless workflows in member accounts using shared subnets — no workarounds required. Customers using Amazon SageMaker Unified Studio Workflows also benefit from this update when their projects are configured with shared VPC networking. This update is available in all AWS Regions where https://aws.amazon.com/managed-workflows-for-apache-airflow/ is supported. To learn more, see the https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/networking.html#networking-support section of the Amazon MWAA Serverless User Guide.

Amazon SageMaker Unified Studio Workflows now supports 19 new operators for Amazon Bedrock, Amazon S3 Tables, Amazon S3 Vectors, AWS Glue Data Catalog, and Amazon MWAA Serverless. With these operators, customers can add new tasks using the visual workflow creator to orchestrate these services without writing custom integration code. With this launch, data workers and builders can create workflows that manage Bedrock guardrails, provision and delete S3 Tables and S3 Vectors resources, manage Glue Data Catalog tables and databases, and trigger MWAA Serverless workflow runs. This expands the breadth of AWS services you can orchestrate from SageMaker Unified Studio Workflows, reducing the need to switch between consoles or write custom DAG code. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. For more information, see the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/. To learn more, see https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/operators.html. To get started, see https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/serverless-visual-workflows.html in the Amazon SageMaker Unified Studio User Guide.