Member-only story
DuckDB Just Solved Its Biggest Problem
Meet DuckLake: The minimal-infra answer to Delta Lake and Iceberg.
Not a medium memeber here is friend link:
Over the past few years, DuckDB has become default Swiss Army knife for embedded data processing. If you need to crunch tens of gigabytes of analytical data without spinning up an over-engineered Spark cluster, nothing beats the raw speed and zero-friction setup of DuckDB’s in-process SQL engine.
But if you’ve ever tried building production-grade analytical pipelines on raw Parquet files, you already know where the honeymoon ends.
The moment your workload requires incremental updates, ACID guarantees, or schema evolution, raw Parquet starts showing its limits. Traditionally, the answer was reaching for standard open lakehouse table formats like Apache Iceberg or Delta Lake. But when I came across DuckLake — the extension designed to bring native lakehouse capabilities straight into the DuckDB ecosystem — I wanted to see how far it could be pushed.
I spent the past few weeks putting DuckLake through its paces, running CRUD operations, micro-batches, and time-travel queries across historical electorate profile data spanning over 1 million rows.
