Astro gives you scalable, sustainable orchestration for operational analytics.

Organizations invest significant resources developing the KPIs, metrics, and measures that power their operational analytics, but these insights are only as good as the data they consume. The challenge is to get the right data to the right dashboard, portal, or app at the right time — a task that’s even more difficult now that dataflows crisscross multiple cloud and on-prem environments.

Astro, the fully managed data orchestration service powered by Apache Airflow, enables organizations to meet this challenge. It integrates with the many tools used in producing and maintaining operational analytics and ensures interoperability across distributed workflows. It provides a foundation for a scalable, available data infrastructure, accelerates development and deployment, and simplifies ongoing maintenance.

Collaborate across teams

Operational analytics brings together different types of experts who contribute at various stages. Each type has their own preferred tools, languages, and methods, but they all need to be able to work collaboratively to produce and maintain operational analytic insights.

As a platform built on top of Apache Airflow and 100% compatible with it, Astro has pre-built integration with hundreds of data and analytics platforms and tools, and supports all of the popular Python software packages that data scientists, data engineers, and other experts rely on. It gives teams a turnkey orchestration framework they can hook into from their preferred tools as they collaborate to design, debug, and maintain data pipelines as code, accelerating development and simplifying deployment and maintenance.

Orchestrate across environments

Astro also makes it easy for teams to build, run, and observe data pipelines that integrate data from sources that are distributed across multiple cloud services, the on-prem environment, and the network edge. It seamlessly orchestrates the pipelines that feed timely, conditioned data to the KPIs, metrics, and measures decision-makers depend on for detailed insights into business operations.

Keep operational insights accurate and relevant

As business conditions change, existing KPIs, metrics, and measures tend to become less useful. Astro’s ability to extract detailed lineage metadata gives data scientists, analytic engineers, and other experts the information they need to proactively maintain and, when necessary, refactor operational insights and the data pipelines that drive them. Lineage metadata also enables support personnel to quickly pinpoint and resolve data outages, giving them a starting point for diagnosis and enabling them to trace data quality issues back to problems in specific data pipelines or to errors in upstream systems. This minimizes the impact of service disruptions and ensures analytic insights are available and dependable.

Observe and improve data quality

Organizations can also use lineage metadata to observe their data quality levels, identifying obvious issues — such as errors in upstream sources — along with data quality problems that stem from changing business conditions, which can be difficult to diagnose. Astro collects and aggregates lineage metadata in its common control plane, giving data stewards, CDOs, and CAOs a single pane of glass they can use to observe, manage, and improve data quality.

Orchestration that just works

Astro frees developers to focus on designing the pipelines that feed data to analytic dashboards, portals, and alerts, rather than constantly writing code to keep disparate tools and other software working together. And by providing a robust foundation for a reliable, resilient data infrastructure, Astro gives organizations the features they need to support the entire operational analytics lifecycle, not just isolated phases of it.

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