Glossary

Apache Iceberg

An open table format adding ACID transactions, schema evolution, and time travel to files stored in a data lake.

Also called: Iceberg

Apache Iceberg is an open table format originally developed at Netflix and now an Apache Software Foundation project. It sits on top of data files, typically Apache Parquet, stored in object storage, and adds a metadata layer that tracks table schema, partitioning, and every snapshot of the table's state, giving file-based data the transactional behavior of a database table.

Iceberg tracks changes at the individual file level rather than the whole-table level, so a write only touches the files affected and readers always see one consistent, atomic snapshot even while writes are in progress; every snapshot is retained, which enables time travel to query the table as it existed at a prior point. Its schema evolution and partition evolution are designed so that adding a column or changing partitioning does not require rewriting existing data files, which distinguishes it from simpler file-on-disk approaches.

Iceberg matters because it lets multiple engines — a warehouse, Spark, a query engine — read and write the same underlying tables with consistent semantics, reducing lock-in to any single vendor's storage format, which is central to the data lakehouse pattern. It competes with Delta Lake and Apache Hudi as the leading open table formats; the choice between them mostly comes down to ecosystem support and existing tooling rather than a fundamental capability gap.

Last reviewed September 22, 2026

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Related terms

Open table format A specification that adds transactions, schema management, and versioning to files stored in a data lake. Apache Parquet An open-source, columnar file format for storing large analytical datasets efficiently in disk or object storage. Data lakehouse A storage architecture that adds warehouse-style structure and transactions directly on top of low-cost data lake storage. Schema evolution The ability of a table to accommodate structural changes, like an added or renamed column, without breaking data. Partitioning Physically splitting a table's data into segments, usually by a column like date, so queries can skip irrelevant ones. Apache Spark An open-source distributed computing engine for processing large datasets in parallel across a cluster of machines.

Related tools

Amazon Kinesis AWS's managed service for real-time data streaming, ingesting and processing continuous data at scale without managing servers. Apache Iceberg Open-source table format that adds warehouse-style transactions, schema evolution, and time travel to data-lake files. Cloudera Data Platform Enterprise data platform unifying data engineering, warehousing, ML, and streaming across hybrid and multi-cloud environments. Dremio SQL query and semantic layer engine for querying data lakes directly, built around Apache Iceberg and Apache Arrow. Streamkap Managed streaming CDC platform that replicates database changes to warehouses and lakes in under a second.