Understanding DuckLake: A Table Format with a Modern Architecture

The Evolution from Databases to Table Formats

Modern data engineering has undergone a significant transformation in how analytical data is stored and processed. Traditional OLAP databases once handled both storage and compute, but this approach led to two major challenges: vendor lock-in through proprietary formats and the inability to scale storage independently from compute.

This limitation gave birth to the data lake architecture, where analytical data is stored as files (primarily in columnar formats like Parquet) on object storage systems such as AWS S3, Google Cloud Storage, or Azure Blob Storage. This decoupling allows any compute engine - Apache Spark, Trino, or DuckDB - to query the same data.

The Table Format Revolution

While storing data as Parquet files on blob storage provides flexibility, it sacrifices essential database features:

• No atomicity: Parquet files are immutable, requiring complete rewrites for updates
• No schema evolution: Adding or removing columns requires manual tracking
• No time travel: Querying historical states of data becomes complex

Table formats like Apache Iceberg and Delta Lake emerged to bridge this gap. They add a metadata layer on top of file formats, enabling:

• Metadata tracking (typically in JSON or Avro format)
• Snapshot isolation and time travel capabilities
• Schema evolution support
• Partition pruning optimization

However, these solutions introduce new complexities. They generate numerous small metadata files that are expensive to read over networks, and often require external catalogs like Unity Catalog or AWS Glue to track table locations and versions.

DuckLake: A Fresh Approach to Table Formats

DuckLake represents a fundamental rethink of table format architecture. Despite its name, DuckLake is not tied to DuckDB - it's an open standard for managing large tables on blob storage.

DuckLake in Practice

The Future of Table Formats

DuckLake's approach solves many of the metadata management challenges that plague current table formats. By leveraging proven relational database technology for metadata while maintaining open file formats for data, it offers a pragmatic solution to the complexities of modern data lakes.

While still in its early stages, DuckLake shows promise for organizations looking to simplify their data lake architecture without sacrificing the flexibility and scalability that made data lakes popular in the first place. As the ecosystem matures and more compute engines add support, DuckLake could become a compelling alternative to established formats like Iceberg and Delta Lake.