Apache Iceberg is an open-source, high-performance table format for massive analytic datasets. It's designed to bring database-like capabilities to data lakes, allowing for schema changes, time travel and rollback, ACID transactions at scale, and faster queries. The architecture of Iceberg consists of three layers: the catalog layer, which keeps track of all tables; the metadata layer, where the magic happens with a hierarchy of JSON files to track table structure and history; and the data layer, where actual data files live. This layered approach enables excellent query performance through multiple levels of filtering, reliable concurrent operations, easy schema evolution, point-in-time queries, and rollbacks. Iceberg effectively replaces traditional file organization methods, Hive table formats, raw file management in data lakes, custom solutions for managing partitioning and schema evolution, and complex workarounds for maintaining data consistency. Data teams are making the switch to Iceberg due to cost savings, improved query performance, seamless schema evolution, time travel capabilities, and multi-table transactions that actually work. While it may not be suitable for all teams, especially those with small data volumes or no need for advanced features, Iceberg offers compelling benefits for large-scale analytics workloads and is worth considering for teams dealing with growing data lakes and frequent schema changes.