DuckDB has traditionally been seen as a last-mile analytics powerhouse, the fastest way to run a SQL query on your laptop. But DuckDB offers more than just fast SQL, of course; it supports full database semantics and ACID transactions, behaving like a fully fledged, in-process OLAP database. The in-process component has sometimes been viewed as a limitation when considering DuckDB as a data warehouse.

However, DuckDB now supports reading and writing to most Open Table Formats (OTFs), including Iceberg, Delta, and DuckLake. This capability puts DuckDB in a very different position: it allows DuckDB to act as a SQL engine in the cloud (or on your local machine) and run queries against any OTF stored in remote cloud storage. DuckDB can now be the all-mighty, single-node query engine that powers your data analytics use cases.

In this talk I will also dive into: - Why this change allows for a "multi-player" DuckDB experience. - How DuckDB efficiently queries very large tables leveraging table statistics and cache. - Why building native implementations with minimal dependencies to interact with OTFs is hard but can potentially pay off.

Projects: https://github.com/duckdb/duckdb https://github.com/duckdb/ducklake https://github.com/duckdb/duckdb-iceberg https://github.com/duckdb/duckdb-delta