DuckLake 1.1 solves the absurd complexity of data lakes at AI scale
How much simpler could a data lakehouse get if an ordinary SQL database kept track of its files? That’s the The post DuckLake 1.1 solves the absurd complexity of data lakes at AI scale appeared first on The New Stack .
DuckLake 1.1, an open-source table lakehouse format, addresses the complexities of data lakes at AI scale, says Pedro Holanda, lead DuckLake developer at Amsterdam-based DuckLabs. By storing table metadata within a standard SQL database, DuckLake eliminates scattered manifest files and the associated complexity. Unlike traditional lakehouse formats, DuckLake treats metadata as a relational database problem, serving advanced data lake features through Parquet files and SQL databases.
Holanda, who has contributed to the DuckDB open-source project since its early research stage, explains that DuckLake reduces files and complexity by taking advantage of the familiarity developers have with SQL and schemas. By issuing queries, the server can directly answer questions, reading only the necessary Parquet files instead of multiple ones. Additionally, DuckLake reduces the "small files problem" in streaming data lakes by using inlining and flushing techniques.
In October, DuckLake 1.1 launched alongside DuckDB 2.0.0 LTS. Amazon's CEO Andy Jassy highlighted the complexity of moving data between data stores and analytics engines during Amazon's Q3 earnings call, mentioning DuckDB's solution as a reason for its DuckLabs acquisition. With DuckLake, AWS will offer a universal SQL dialect for its storage and analytics, simplifying data processing and enabling customers to write SQL queries once and execute them using any AWS analytics service.
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