"You make a small change to your table, adding a single row, and it affects data lake performance because, due to the way they work, a new file has to be written that contains one row, and then a bunch of metadata has to be written. This is very inefficient, because formats like Parquet really don't want to store a single row, they want to store a million rows."
"The DuckLake approach uses the metadata RDBMS to batch up those small changes and then transfers them to Parquet in relatively bigger chunks."
DuckDB has introduced DuckLake, a production-ready lakehouse format that addresses the inefficiencies associated with small changes in lakehouse systems. The solution utilizes an RDBMS to manage metadata, allowing for batching of small changes before transferring them to Parquet files. This approach improves performance by reducing the overhead of writing individual rows and updating catalogs. The DuckLake format aims to enhance the integration of data lakes and warehouses, leveraging technologies like Apache Iceberg and Delta Lake for better efficiency in data management.
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