Astronomer, the company behind Astro, the leading data orchestration and observability platform powered by Apache Airflow, has announced the general availability of Astro Observe. This new solution provides unparalleled visibility into the health and performance of data pipelines, consolidating insights into a single pane of glass for mission-critical data products. Built on one of the most widely adopted open-source projects in the data and AI ecosystem, Astro Observe empowers organizations to optimize and manage their Apache Airflow environments more effectively.
“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.”
Addressing the Needs of Modern Data Products
The role of data products has expanded significantly, driving key business operations such as personalized recommendations, automated pricing, AI-powered customer support, and more. In this evolving landscape, full-stack data orchestration is essential, making reliability and observability a top priority for enterprises aiming to maximize the ROI of their AI and data strategies.
To unlock the full potential of production AI and data-driven decision-making, organizations must modernize the higher layers of the data stack. The DataOps layer, positioned above the compute layer, plays a crucial role in operationalizing data by transforming raw inputs into actionable insights. It encompasses the orchestration of complex data workflows, including ingestion, transformation, machine learning (ML), and AI processes. However, achieving end-to-end control requires additional tools for data discovery, integration, observability, quality monitoring, and governance. With the introduction of Astro Observe, Astronomer is taking a major step toward delivering a fully unified DataOps platform that streamlines these capabilities.
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Key Features of Astro Observe
Astro Observe equips teams using Apache Airflow with a powerful observability solution that enhances visibility and control over data pipelines. Designed to work seamlessly across Astro, open-source Apache Airflow, and other managed services, it enables proactive issue resolution and optimization. By integrating enterprise-grade observability with Astro’s orchestration capabilities, organizations can now develop, manage, and monitor data pipelines as code, transforming how they oversee modern data operations.
“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 features introduced during the private and public previews, Astro Observe now includes:
AI Log Summaries – AI-driven insights analyze logs related to Apache Airflow pipeline incidents, pinpointing task-level failures and providing actionable next steps for faster debugging and resolution, significantly reducing troubleshooting time.
Data Health Dashboard – A centralized view of data pipeline health across an organization’s Apache Airflow environment, helping teams monitor operational metrics like DAG runtimes and task failures, ensuring data reliability and cost efficiency.
Timeline View – A historical snapshot of Service Level Agreement (SLA) compliance and task execution, offering granular insights into data product delivery and failure points, with targeted root cause analysis tools.
Snowflake Cost Management – A pre-built DAG that integrates Astro Observe with Snowflake, enabling detailed cost attribution insights. This feature allows teams to track spending within data pipelines and identify opportunities for cost optimization.
Insights Engine – An AI-powered module providing proactive risk assessment and optimization recommendations based on Apache Airflow best practices. This includes early detection of pipeline bottlenecks and anomalies, ensuring smooth and efficient data workflows.
With the launch of Astro Observe, Astronomer continues to push the boundaries of data orchestration and observability, offering enterprises a comprehensive solution to manage the increasing complexity of modern data ecosystems.
