Data Science &
Orchestration with Astro, the fully managed service powered by Airflow, frees data scientists and ML engineers to focus on work that drives value.
Astro lets you develop and deploy
ML solutions faster.
The complexity of modern data engineering is one of the biggest impediments to building, deploying, and maintaining data science and machine learning (ML) projects. Data scientists create complex pipelines that acquire data from multiple upstream sources and feed it to different kinds of cloud and on-prem compute services, where they shape and engineer it.
Once they’re finished, they hand their work off to ML engineers or to data engineers, who often recreate it from scratch, using their own preferred tools and methods. In addition to the obvious cost in time and resources, this process tends to introduce errors and inconsistencies as each team writes its own custom logic. Complications like this drastically slow down development and result in some projects never even making it into production.
Astro — the fully managed service from Astronomer built on top of Apache Airflow — solves for these problems, providing a common orchestration framework that data scientists, ML and data engineers, and others can use to acquire and engineer data at every phase of a data science or ML project’s lifecycle.
More time on
less on model
A common complaint among data scientists and ML engineers is that they spend more time addressing nuts-and-bolts data engineering problems than on the actual work of data science or machine learning. Astro’s built-in features correct this imbalance, allowing organizations to custom-tailor Airflow deployments to suit the needs of data scientists and ML engineers. It gives them a consistent, reproducible environment for building, debugging, and — with one click — deploying their data pipelines.
Astro makes it easier for these experts to collaborate with data engineers and DevOps/SRE specialists to support data science and ML solutions once they’re in production. It enables data scientists and ML engineers to focus on their core responsibilities — identifying new business use cases for ML, building models, and other work that drives value for the business.
Astro provides a dependable foundation for data engineering, allowing data scientists, ML engineers, and data engineers to iterate faster and produce repeatable, predictable results as they train, test, and evaluate machine learning models and move them to production. It also provides visibility into the data pipelines that feed machine learning models, speeding up debugging, simplifying troubleshooting, and making it much easier to diagnose and resolve data outages.
See how Astro makes data orchestration easy and accessible for data science and ML teams, and accelerates the ML lifecycle.Get Started
Removing bottlenecks and empowering teams
Data team members can often feel like they exist on separate islands. Siloing within a data team can result in miscommunication, unexpected inconsistencies, and time-wasting bottlenecks.
Astro brings data scientists and engineers together, working in the same language — Python, the lingua franca of data scientists, ML engineers, and data engineers — and the same workflow management framework. When it comes time to operationalize data science and ML projects, data engineers are able to work with the same data pipelines, hardening them to ensure reliability and designing new logic to improve resiliency. The result is a dynamic collaboration among all members of the data team that reduces problems in the path from model development to production.
Compatible, general-purpose orchestration
Astro makes it easy to do complex things. There is no need to change the way you work — Airflow has pre-built integrations into just about every data platform and analytics framework out there, so you can continue using your favorite tools and expect them to work seamlessly with your data pipelines. Astro’s general-purpose orchestration platform encourages congruity among all the tools in your data stack.