Astro Observe meshes data orchestration with data observability to simplify management of crucial data products
Astronomer, the company behind Astro, the leading data orchestration and observability platform powered by Apache Airflow®, today announced the general availability of Astro Observe. Astro Observe provides the most comprehensive single pane of glass for Apache Airflow pipelines, providing visibility into the health and performance of business critical data products built on one of the most popular open source projects in the data and AI ecosystem.
“Enterprise and data teams have long been burdened with a fragmented DataOps stack that restricts their ability to turn data into a competitive advantage,” said Andy Byron, CEO, Astronomer. “Organizations need a more consolidated and streamlined approach to leverage their critical data assets in the age of advanced analytics and AI. With the addition of Astro Observe, Astro is now a unified DataOps platform that cuts through today’s chaos above the compute layer, replacing tool fragmentation with end-to-end visibility, control, and automation.”
“The evolution of data products from basic reporting tools to mission-critical business assets demands a new approach to operational excellence,” said Julian LaNeve, CTO, Astronomer. “With the general availability of Astro Observe, we are building AI and intelligence-driven enhancements into Astro today that boost reliability, efficiency, and productivity across the entire data lifecycle. From AI-powered root cause analysis to the new Insights Engine, we’ve added several new capabilities to Astro Observe that simplify observability and orchestration.”
Meeting the Growing Demands of Modern Data Products
Today’s data landscape has evolved dramatically, with data products now driving essential business operations, from retail recommendations and dynamic pricing to automated customer support and AI applications. Full-stack data orchestration has become fundamental to these modern software solutions, making reliability and observability more critical than ever.
If enterprises are going to actually see ROI from production AI or truly leverage their data for competitive advantage, the higher layers of the modern data stack need to undergo their own disruptions.
The DataOps layer sits above the data compute layer, with the purpose of operationalizing data, transforming raw inputs into data products ready for consumption. Most importantly, DataOps involves orchestrating complex data pipelines and workflows that handle critical tasks like data ingestion, integration, transformation, ML/AI processes, and more. All of which has to be augmented with separate tooling to take care of the essential controls around that data — discovery, integration, observability, quality monitoring, and data governance. The addition of Astro Observe’s capabilities to Astro’s existing orchestration prowess is a crucial milestone in starting to provide enterprises with a unified DataOps platform.
What’s New in Astro Observe
Astro Observe provides all teams using Apache Airflow with an actionable view of their entire data supply chain. The solution enables proactive optimization, rapid problem resolution, and will support any team’s Apache Airflow projects – whether running on Astro, open source Apache Airflow, or other managed services. By combining enterprise-grade observability with Astro’s powerful orchestration capabilities, organizations can now seamlessly build, run, and observe data pipelines as code, transforming how they develop and maintain modern data products.
“We are excited about Astro Observe because it is filling a void that we would otherwise have to build ourselves,” said Brendan Frick, Senior Engineering Manager, GumGum. “Adding data observability alongside orchestration allows us to get ahead of issues before they impact users and downstream systems. Assigning SLAs to our critical data products means that we can have confidence that our data products are leveraging the right data, and are on schedule.”
In addition to previously announced Astro Observe features during the private and public previews, Astro Observe also now includes:
- AI Log Summaries – AI-driven analysis of logs related to incidents impacting Apache Airflow pipelines. These synopses pinpoint the logs associated with failures down to the task-level and provide targeted next steps for instant root cause analysis, faster debugging and issue resolution. This saves users time from sifting through and interpreting Apache Airflow logs.
- Data Health Dashboard – A high-level view of data pipeline health across organizations’ Apache Airflow ecosystem to more easily monitor data ecosystem health and costs. This view helps data engineering managers and team leads see health across multiple pipelines in one place, monitor operational metrics like DAG runtimes, and task failures that may affect data reliability or cost.
- Timeline View – A historical view of Service Level Agreement (SLA) compliance and task execution providing a granular replay of data product delivery and failures, with targeted root cause analysis tools that pinpoint upstream failures and anomalous events.
- Snowflake Cost Management – A pre-built DAG to connect Astro Observe with Snowflake deployments and surface associated cost attribution details. With Snowflake Cost Management, users can understand the activities within data pipelines that are driving spend within Snowflake, and pinpoint opportunities for more efficient scaling and cost savings.
- Insights Engine – Observe customers will also get early access to the Insights Engine, which offers AI-powered guidance built on Airflow best practices to proactively signal risks to pipelines and opportunities for optimization – including bottlenecks and anomalies within data products.
