![]() ![]() Apache Airflow, driven by the Astronomer team, has become the generational platform for modern orchestration,” said George Mathew, Managing Director at Insight Partners. “As the modern data stack has arrived at scale, we now need an orchestration experience to support today’s sophisticated, high-velocity data pipelines. We are excited to soon unveil what’s next from our team, fueled by our Series C and the incredible support of our investors.” By integrating Datakin’s intuitive data lineage features with Astronomer, our customers are able to build faster, run with confidence and reduce their operational risks. ![]() “At the same time, data teams must be able to trust their data to effectively extract its value. “As companies of all sizes struggle to make sense of their data, data orchestration has become a competitive necessity,” said Joe Otto, CEO of Astronomer. Astronomer will also use the capital to grow its world-class Engineering and Customer Success teams, accelerate the growth of its Apache Airflow-powered data orchestration platform and scale its go-to-market operations. The funding primed Astronomer to acquire Datakin, the data operations tool from the founders of the OpenLineage and Marquez open-source projects. Morgan, K5 Global, Sutter Hill Ventures, Venrock and Sierra Ventures. We are thrilled to be leading Astronomer's $213M Series C with participation by Sutter Hill Ventures, Meritech, Salesforce Ventures, Venrock, and Sierra Ventures.SAN FRANCISCO and CINCINNATI, MaAstronomer, developer of a modern data orchestration platform powered by Apache Airflow, today announced that it has raised $213 million in a Series C round, led by global growth equity and venture capital firm Insight Partners, with participation by Meritech Capital, Salesforce Ventures, J.P. Astronomer’s leadership team, now including Joe Otto and Scott Yara, is world class and highly regarded Astronomer will continue to actively shape the Airflow ecosystem and remain an integral part of its development, adoption and scale. We’re excited the stars have finally aligned for us to work together. When I first connected with Ry Walker, Astronomer’s founder, the vision of a modern data stack was already tangible. Integrating Datakin's data lineage and observability features with Astronomer allows users to access operational lineage and pipeline management within the Modern Data Orchestration platform. Astronomer's offerings will continue to expand, including through the acquisition of Datakin, a key driver for the Series C round. Integrations options for Airflow operator deployment on virtual private clouds or Astronomer's public cloudĪll of this adds up to demand for Astronomer skyrocketing Modern Data teams - more use cases, faster deployment, great support - while reducing the burden on data engineering hours required to deploy and manage Airflow.Packaged customer support to troubleshoot issues or provide guidance based on a deep library of best practices and documentation.Containerized fragmented Airflow deployments, which enables a single point of enterprise control.Astronomer is the SaaS experience for Airflow that enables: Enter Astronomer, the Modern Data Orchestration platform, powered by Apache Airflow. Apache Airflow hit a major inflection point after Decemwith the release of V2 which increased speed, updated the core UI, and supported more robust analytical use cases.ĭespite its many incredible virtues, Airflow often remains difficult to use and challenging to configure. Today, Apache Airflow as a Python based, open-source orchestration platform is used by hundreds of thousands of data teams with over eight million monthly downloads. ![]() These structural tailwinds have introduced the need for Modern Orchestration where distributed scheduling, complex workflows, data lineage, and observability are essential capabilites for data engineering success. In essence, it is an indictment of classical data warehousing is seeing the rise of the Modern Data Stack beyond what Hadoop started, then continued by Spark, and now exploding with cloud-native data warehouses/lakes. This enables large scale ’schema on read’ capabilities where transformation of data is within the data warehouse/lake itself. In the last decade, we’ve seen the rise of the Modern Data Stack where cloud-native data warehouses and data lakes are the new 'systems of record', replacing brittle ETL (Extract, Transform, and Load) scripts with simpler, more scalable ELT (Extract, Load, Transform) tools. ![]()
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