DuckDB is the best way to execute SQL on a single node. But with its embedding-friendly nature, it makes an excellent foundation for building distributed systems. George Fraser, CEO of Fivetran, will tell us how Fivetran used DuckDB to power its Iceberg data lake writer—coordinating thousands of small, parallel tasks across a fleet of workers, each running DuckDB queries on bounded datasets. The result is a high-throughput, dual-format (Iceberg + Delta) data lake architecture where every write scales linearly, snapshots stay perfectly in sync, and performance rivals a commercial database while remaining open and portable.

We were told to scale compute. But what if the real problem was never about big data, but about bad data access? In this talk, we’ll unpack two powerful, often misunderstood techniques—projection pushdown and predicate pushdown—and why they matter more than ever in a world where we want lightweight, fast queries over large datasets. These optimizations aren’t just academic—they’re the difference between querying a terabyte in seconds vs. minutes. We’ll show how systems like Flink and DuckDB leverage these techniques, what limits them (hello, Protobuf), and how smart schema and storage design—especially in formats like Iceberg and Arrow can unlock dramatic speed gains. Along the way, we’ll highlight the importance of landing data in queryable formats, and why indexing and query engines matter just as much as compute. This talk is for anyone who wants to stop fully scanning their data lakes just to read one field.