In this video, Dipankar breaks down how Apache Iceberg works under the hood - starting from the limitations of Hive-style tables to why Iceberg was built in the first place. He covers: Why Hive-based tables break at scale (Netflix example) How object storage changes the problem (S3 behavior, listing, throttling) Iceberg architecture (catalog, metadata, snapshots, manifests, data files) How query planning works step by step Why Iceberg is a specification — not an execution engine.

Every company has an AI roadmap. Very few have the data infrastructure to execute it. At Hevo Data, we've spent 8 years building pipelines that are reliable, simple, and transparent so 2,000+ data teams can build without second-guessing their data. We sat down with Manish Jethani, Amit Gupta, and Scott Husband to talk about what comes next. If your data isn't AI-ready, your roadmap stays a roadmap. We've re-engineered the platform to serve as the context engine your AI vision actually runs on. Because the models are only as good as the data underneath them.

Why do most BI tools feel like they were built for someone else? Because they were built to be general—and "general" doesn't cut it in the Agentic era. At our March Spotlight, ThoughtSpot CMO Micheline Nijmeh introduces the unveiling of Spotter for Industries: AI designed from the ground up to understand your specific metrics, workflows, and priorities. We’re moving past the hype to deliver real business results.