Historically, Datadog has relied on technologies like Snowflake and Apache Spark on raw parquet files (lacking consistent table structure) to power internal analytics and data science at scale. As usage grew across product teams, more features depended on data science teams, and our datasets grew to include more telemetry data, these systems became complex to manage and govern both technically and financially. The need for a more flexible and scalable solution led Datadog to adopt Apache Iceberg, an open source table format for data lakes that brings reliability and performance while remaining SQL-friendly.

In this episode of Datadog on, Ara Pulido, Staff Advocate, will chat with Data Engineers Leopold Boudard and Alessandro Nori. They’ll discuss why Datadog chose Apache Iceberg, how it is used today to build data-rich features across product teams, and what challenges and opportunities came with the migration. The conversation will also cover Datadog’s contributions to the Apache Iceberg community and how those improvements feed back into both Datadog’s platform and the broader ecosystem.

By the end of the episode, you’ll gain a better understanding of how Iceberg fits into modern data platforms, what considerations to keep in mind when adopting it, and how it enables Datadog engineers to deliver data-driven features at scale.