Apache Iceberg is an open table format built to bring reliable, SQL-like tables to huge analytic datasets stored in an object store like S3, with ACID transactions, schema evolution, hidden partitioning, and time travel built in.
This hub is the entry point to what we have written about running Iceberg in production. It is new and will grow section by section as we publish more, starting with Iceberg use cases: real deployments, with the architecture and the numbers behind them.
Everything the Iceberg cluster covers
Companies running Apache Iceberg in production and the architectures behind their deployments, indexed as we publish them.
Learn moreHow Netflix moved its S3 data warehouse off Hive onto an Iceberg-only architecture, and how Maestro, Psyberg, and its Cassandra data movement engine lean on Iceberg's own snapshot and partition metadata.
Learn moreHow Apple built GDPR- and DMA-compliant row-level deletes on Iceberg tables holding tens of petabytes of data, sourced from a peer-reviewed paper by eight Apple engineers and named conference talks.
Learn moreHow Airbnb moved its Kafka-fed warehouse ingestion pipeline off Tez and Hive onto Spark 3 and Apache Iceberg, consolidating tables via partition-spec evolution and cutting compute by over 50%.
Learn moreHow Pinterest rebuilt CDC ingestion and ML feature backfills on Apache Iceberg, cutting pipeline latency from 24+ hours to minutes and backfill time from 140 days to 26.
Learn moreHow LinkedIn's OpenHouse control plane governs 300,000+ Apache Iceberg tables holding over an exabyte of data, with automated compaction and cross-region replication.
Learn more