Astro is an end-to-end solution for your data integration needs.
ETL and ELT tasks are more crucial to business success than they’ve ever been. But ETL and ELT task failures are also more common now that data ecosystems have grown more complex, spanning both the on-prem data center and one or more cloud service providers. Another factor contributing to an increase in task failures is that it is no longer just data engineers who are integrating data: data scientists, analytic engineers, and other experts now routinely acquire and engineer data to do their work.
Astro, the fully managed orchestration service powered by Apache Airflow, makes it easy for organizations to customize different types of Airflow deployments to suit the needs of all kinds of data practitioners. It gives practitioners across different teams a common orchestration framework — with a secure, controlled path to production — that they can use to build, debug, and deploy distributed data pipelines as code. And it allows organizations to maintain oversight, thanks to a central control plane that they can use to observe and manage their Airflow deployments.
Integrating the modern data stack
Organizations can use Astro to run and manage legacy ETL and ELT workflows, along with the new ephemeral ETL/ELT patterns enabled by cloud-native computing and the modern data stack. They can count on Astro to orchestrate the batch ETL or ELT dataflows that populate their cloud data warehouses, data lakes, and/or data lakehouses, as well as feed batches of data to their fraud-detection alerts, clickstream analytics, and other time-critical services.
Just as important, Astro supports the unique requirements of data scientists and ML engineers, who need orchestration capabilities but typically prefer to use their own tools to work with data.
Connect to data wherever it lives
Astro facilitates seamless interoperability among distributed resources of all kinds, from cloud services that use APIs for data exchange to on-prem resources that exchange data with host-based or client-server interfaces. With its hundreds of software connectors, you can use Astro to orchestrate pipelines that integrate data from SaaS, PaaS, and IaaS services, Git repositories, and the web, in addition to legacy on-prem apps, databases, and file systems.
Take advantage of innovative tools and practices
Data and analytic engineers expect to use tools like Airbyte, dbt, Fivetran, and Great Expectations to integrate and validate their data. Sometimes they need to use cloud services, like AWS Glue and Azure Data Factory, to integrate data that lives in AWS or Azure. All of these tools are easy to use, but they do not natively interoperate with one another: instead, experts must write custom logic to manage dependencies between them and recover from task failures.
Astro’s extensive library of prebuilt operators and sensors eliminates custom coding, allowing experts to focus on hardening their data pipelines to make them fast, efficient, and ultra-reliable.
Observe and analyze data lineage
Astro automatically extracts and analyzes lineage metadata, giving organizations a complete view of data lineage from source to target. This lets support personnel quickly pinpoint and resolve data outages, and allows data stewards to monitor and improve data quality. Lineage metadata also gives organizations the information they need to effectively govern their data, enabling CDOs, CAOs, data stewards, and others to identify silos, protect sensitive data, and comply with regulatory requirements, as well as promote the responsible reuse of data.
Seamless orchestration that just works
With its support for in-place, push-button upgrades, Astro completely automates the work of maintaining, securing, and updating Airflow. And by making it easy to use Airflow — to design, manage, and maintain data pipelines as code — Astro makes data engineers more productive, helps minimize the risk of human error, and helps keep cloud costs down. It eliminates the most tedious and wasteful part of data integration and lets experts focus solely on engineering data.