Apache Iceberg vs Apache Hudi vs Delta Lake: Table Format Comparison

The demand for data lakehouses has been on the rise in recent years, as organizations seek out new ways to store and access large amounts of data. To meet this demand, the three main data lakehouse table formats—Apache Iceberg, Apache Hudi and Delta Lake—have emerged as powerful solutions for storing and managing large datasets.

Dremio, a leader in the field of Data Lakehouse, has released an article comparing these three platforms. In this video, we'll provide an overview of some of the key points from that article so you can get up to speed quickly.

First off is Apache Iceberg. This open source table format was created to address the challenges posed by traditional data warehouses when dealing with large datasets. Iceberg provides a unified view of data across multiple tables by allowing users to store a single version of each table in their Data Lakehouse. This makes it easier for users to access and analyze their data without having to worry about keeping track of multiple versions or replicating them across different systems. Additionally, Iceberg allows users to query their data more efficiently by using partitioning strategies such as bucketing and sorting which can help reduce query times by orders of magnitude.

Next is Apache Hudi, another open source table format designed specifically for Data Lakes. This platform provides built-in support for ACID (Atomicity, Consistency, Isolation and Durability) transactions which make it easier for users to update their data without worrying about conflicting versions or losing any existing information stored in their tables. Furthermore, Hudi also supports incremental processing which allows users to update only the parts of their tables that have changed since the last read operation rather than having to process the entire dataset from scratch every time they want to make changes.

Finally there's Delta Lake which is an open source file format designed specifically for Data Lakes and Data Warehouses that provides both ACID transactions and time travel capabilities which allow users to view previous versions of their tables at any point in time up until they delete them permanently from storage. Additionally, Delta Lake also supports streaming ingest which makes it easier for users to continuously ingest new records into their tables without having to wait until all records have been processed before committing them into storage.

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