Code-based ETL

Control your entire data ecosystem.

Say goodbye to drag-and-drop.

When workflows are defined as code (neat lines of Python, not spaghetti), they answer to you. That means more versioning, portability, extensibility and collaboration. With the Apache Airflow module, you can parse, refine, clean and otherwise process data, all in code. Better yet, create complex dependencies between processing tasks and schedule jobs to run anytime.

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Work faster.

A library of pre-built pipes and operators means you don’t have to create every task from scratch.

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Build trust.

Invite anyone to watch data flow, and ensure complete reliability with livestream monitoring.

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Do what you love.

Let’s face it: ETL can be boring. Streamline it and free yourself up to focus on more interesting tasks.

“Our organization is generating data from a myriad of sources ranging from oper-ations platforms to telephony systems to HR software. The analytics team spent most of their time hunting down, cleaning, and organizing data and had little left for actual analysis. Astronomer has allowed us to skip past the tedious, yet absolutely necessary, steps of extraction and transformation, and get straight to discovering the things that boost production and the bottom line.”

— James Cisneros (RTR CIO)

How does it work?

With one click via the Astronomer UI, deploy your own scalable, managed Apache Airflow instance. Right away, your engineers can build scheduled, dependency-based workflows in Python. Built by devs, for devs, it’s based on the principle that ETL is best expressed in code (which is even better when open sourced).

Find Out More in the Docs >

Not familiar with Apache Airflow?

No problem. Astronomer will walk you through DAG-writing, provide in-depth docs and stand by for support. And if you don’t have time to write DAGs, don’t know Python or aren’t sure what to do once the data is transformed, our team can cover that too.