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Cloud Composer | Apache Airflow 3

A fully managed workflow orchestration service built on Apache Airflow.

New customers get $300 in free credits to spend on Composer or other Google Cloud products during the first 90 days.

• 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

Cloud Composer's managed nature and Apache Airflow compatibility allows you to focus on authoring, scheduling, and monitoring your workflows as opposed to provisioning resources.

Integrates with other Google Cloud products

End-to-end integration with Google Cloud products including BigQuery, Dataflow, Dataproc, Datastore, Cloud Storage, Pub/Sub, and AI Platform gives users the freedom to fully orchestrate their pipeline.

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

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

Cloud Composer is built upon Apache Airflow, giving 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

Cloud Composer 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

Find an overview of a Cloud Composer environment and the Google Cloud products used for an Apache Airflow deployment.

Discover how to set up a continuous integration/continuous deployment (CI/CD) pipeline for processing data with managed products on Google Cloud.

Find information on using a private IP Cloud Composer environment.

Find out how to write an Apache Airflow directed acyclic graph (DAG) that runs in a Cloud Composer environment.

This four-day instructor led class provides participants a hands-on introduction to designing and building data pipelines on Google Cloud.

Use cases

• 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.

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All features

Create workflows that connect data, processing, and services across clouds, giving you a unified data environment.

Cloud Composer is built upon Apache Airflow, giving users freedom from lock-in and portability.

Ease your transition to the cloud or maintain a hybrid data environment by orchestrating workflows that cross between on-premises and the public cloud.

Leverage existing Python skills to dynamically author and schedule workflows within Cloud Composer.

Increase reliability of your workflows through easy-to-use charts for monitoring and troubleshooting the root cause of an issue.

Cloud Composer's managed nature allows you to focus on authoring, scheduling, and monitoring your workflows as opposed to provisioning resources.

Pricing

Pricing for Cloud Composer 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 Cloud Composer 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.

Start building on Google Cloud with $300 in free credits and 20+ always free products.

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