Managed Service for Apache Airflow (formerly Cloud Composer)
A fully managed workflow orchestration service built on Apache Airflow.
New customers get $300 in free credits to spend on Managed Service for Apache Airflow or other Google Cloud products.
Author, schedule, and monitor pipelines that span across hybrid and multi-cloud environments
Built on the Apache Airflow open source project and operated using Python
Frees you from lock-in and is easy to use
New support for Apache Airflow 3 (in Preview)
Benefits
Fully managed workflow orchestration
Fully managed workflow orchestration
Managed Service for Apache Airflow's managed nature and Airflow compatibility allows you to focus on authoring, scheduling, and monitoring your workflows as opposed to provisioning resources.
Integrates with other Google Cloud products
Integrates with other Google Cloud products
End-to-end integration with Google Cloud products including BigQuery, Dataflow, Managed Service for Apache Spark, Datastore, Cloud Storage and Pub/Sub gives users the freedom to fully orchestrate their pipeline.
Supports hybrid and multi-cloud
Supports hybrid and multi-cloud
Author, schedule, and monitor your workflows through a single orchestration tool—whether your pipeline lives on-premises, in multiple clouds, or fully within Google Cloud.
Key features
Key features
Hybrid and multi-cloud
Ease your transition to the cloud or maintain a hybrid data environment by orchestrating workflows that cross between on-premises and the public cloud. Create workflows that connect data, processing, and services across clouds to give you a unified data environment.
Open source
Managed Service for Apache Airflow gives users freedom from lock-in and portability. This open source project, which Google is contributing back into, provides freedom from lock-in for customers as well as integration with a broad number of platforms, which will only expand as the Airflow community grows.
Easy orchestration
Managed Service for Apache Airflow pipelines are configured as directed acyclic graphs (DAGs) using Python, making it easy for any user. One-click deployment yields instant access to a rich library of connectors and multiple graphical representations of your workflow in action, making troubleshooting easy. Automatic synchronization of your directed acyclic graphs ensures your jobs stay on schedule.
Enhance how data workflows are built, managed, and monitored
Key enhancements include DAG versioning for auditability and confident rollbacks, alongside scheduler-managed backfills for simpler historical data reprocessing. A new Task Execution API & SDK paves the way for future multi-language support and isolated task environments. Users benefit from a faster, modern React-based UI with improved navigation. Planned event-driven scheduling aims for more reactive, near real-time pipelines. The Edge Executor optimizes remote task execution, and a split CLI (airflow/airflowctl) offers a clearer command-line experience for development and operations.
Documentation
Documentation
Use cases
Use cases
Explore use cases for Managed Service for Apache Airflow
- Data pipeline orchestration (ETL/ELT): Automating complex data workflows, including extraction, transformation, and loading (ETL/ELT) jobs, and managing dependencies between tasks.
- MLOps & machine learning workflows: Orchestrating the end-to-end ML lifecycle, from data preparation and model training/evaluation to deployment and monitoring.
- Business intelligence (BI) automation: Scheduling data extractions for BI tools, automating report generation, and refreshing dashboards.
- Infrastructure and DevOps automation: Automating cloud infrastructure tasks like provisioning and decommissioning clusters, submitting jobs, and managing CI/CD release processes.
- Hybrid and multi-cloud data integration: Coordinating data flows across diverse sources, including other cloud providers and on-premises data centers, to create unified datasets.
All features
All features
| Multi-cloud | Create workflows that connect data, processing, and services across clouds, giving you a unified data environment. |
| Open source | Managed Service for Apache Airflow gives users freedom from lock-in and portability. |
| Hybrid | Ease your transition to the cloud or maintain a hybrid data environment by orchestrating workflows that cross between on-premises and the public cloud. |
| Integrated | Built-in integration with BigQuery, Dataflow, Managed Service for Apache Spark, Datastore, Cloud Storage, Pub/Sub, and more, giving you the ability to orchestrate end-to-end Google Cloud workloads. |
| Python programming language | Leverage existing Python skills to dynamically author and schedule workflows within Managed Service for Apache Airflow. |
| Reliability | Increase reliability of your workflows through easy-to-use charts for monitoring and troubleshooting the root cause of an issue. |
| Fully managed | Managed Service for Apache Airflow nature allows you to focus on authoring, scheduling, and monitoring your workflows as opposed to provisioning resources. |
| Networking and security | During environment creation, Managed Service for Apache Airflow provides the following configuration options: Private IP, Shared VPC, VPC Service Control, CMEK encryption support, and more. |
Pricing
Pricing
Pricing for Managed Service for Apache Airflow is consumption based, so you pay for what you use, as measured by vCPU/hour, GB/month, and GB transferred/month. We have multiple pricing units because Managed Service for Apache Airflow uses several Google Cloud products as building blocks.
Pricing is uniform across all levels of consumption and sustained usage. For more information, please see the pricing page.
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