Astronomer
APIAstronomer is a managed Apache Airflow platform to build, run, and monitor data pipelines for analytics, AI, and data en
astronomer.ioLast updated: April 2026
Astronomer is a managed Apache Airflow platform to build, run, and monitor data pipelines for analytics, AI, and data engineering workflows.
About
Astronomer is a data orchestration platform built to develop, run, and monitor data pipelines using Apache Airflow at scale.
It helps data engineers and DevOps teams manage complex workflows for analytics, machine learning, and AI by providing a fully managed Airflow environment with built-in observability and deployment tools.
No manual Airflow setup. No infrastructure headaches. No fragmented data workflows.
Positioning
Astronomer sits between open source workflow orchestration tools and enterprise data platforms.
It is built for teams that want the flexibility of Apache Airflow without the operational burden of managing infrastructure, scaling, and reliability.
Every pipeline answers a simple question:
Can this workflow run reliably in production at scale?
What You Get
- Managed Apache Airflow
Build, schedule, and monitor workflows without managing Airflow infrastructure - Data pipeline orchestration
Automate ETL, ELT, machine learning, and data processing workflows - Observability and monitoring
Track pipeline health, performance, and dependencies in real time - Cloud and hybrid deployment
Run Airflow in the cloud or within your own infrastructure environments
Core Areas
Workflow Orchestration
Define and manage workflows programmatically using Airflow DAGs
Data Engineering
Automate data pipelines for analytics, transformation, and storage
MLOps & AI Pipelines
Orchestrate model training, deployment, and data preparation workflows
Data Observability
Monitor pipeline execution, failures, and dependencies across systems
Audience
- Data Engineers
- DevOps Engineers
- MLOps Teams
- Platform Engineers
If you build and manage data pipelines, Astronomer is designed for scalable and reliable orchestration.
Why It Matters
Astronomer simplifies running Apache Airflow in production by removing the operational complexity of deployment, scaling, and maintenance.
Its platform, Astro, provides a fully managed orchestration layer that allows teams to focus on building data workflows instead of managing infrastructure. :contentReference[oaicite:0]{index=0}
The goal is simple:
run data pipelines reliably without managing the platform behind them
Positioning
Astronomer is the commercial company behind Apache Airflow's modernization and the provider of Astro, the managed platform that makes Airflow production-ready without the operational burden. As the largest contributor to the Airflow project, Astronomer doesn't just host Airflow — it actively shapes the framework's roadmap, contributes critical features, and employs many of the project's core committers. This deep upstream involvement means Astro customers get features and fixes months before they appear in community releases.
Astro solves the fundamental challenge of running Airflow at scale: managing the scheduler, workers, metadata database, and dependency isolation across teams. The platform provides environment isolation, CI/CD integration, automated scaling, and enterprise-grade observability — turning Airflow from a notoriously difficult-to-operate tool into a reliable production data orchestration service.
What You Get
- Astro Cloud
Fully managed Airflow with dedicated compute, automatic dependency management, Kubernetes-based task isolation, and zero-downtime deployments. - Astro CLI
Local development environment that mirrors production exactly, with Docker-based testing, DAG validation, and one-command deployment to Astro Cloud. - Observability Suite
Cross-deployment DAG monitoring with SLA tracking, alerting, lineage visualization, and cost attribution per pipeline and team. - Hybrid Deployment
Run the Astro data plane in your own cloud account (AWS, GCP, Azure) while Astronomer manages the control plane — keeping data in your VPC. - Environment Management
Isolated environments per team or project with separate dependency trees, resource quotas, and RBAC — eliminating the "shared Airflow cluster" nightmare.
Core Areas
Data Pipeline Orchestration
Schedule, monitor, and manage complex data workflows with dependency management, retry logic, and integrations with every major data tool from Snowflake to dbt to Spark.
MLOps Workflows
Orchestrate ML pipelines including data preparation, model training, evaluation, and deployment using Airflow's extensible operator framework.
Enterprise Airflow Operations
Multi-team Airflow management with environment isolation, SSO/SCIM, audit logging, and compliance features for organizations running hundreds of DAGs.
Why It Matters
Apache Airflow is the industry standard for data orchestration, used by 70% of data engineering teams. But running Airflow in production is notoriously painful — managing the scheduler, database, workers, and Python dependencies across teams can consume an entire platform engineering role. Astronomer's Astro platform eliminates this operational tax while preserving the flexibility and ecosystem that made Airflow dominant.
For data teams, this means focusing on building pipelines rather than managing infrastructure. For platform teams, it means providing a self-service orchestration layer without becoming an Airflow support desk. The result is faster pipeline development, more reliable execution, and clear cost visibility across the organization.
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