As organizations scale their data platforms, traditional data lake architectures often struggle with issues such as data consistency, schema evolution, performance, and governance. Apache Iceberg is an open table format designed to address these challenges, enabling scalable, efficient, and reliable data management. This session explores how Iceberg enhances modern data architectures by providing ACID transactions, time travel, branching and tagging and schema evolution while optimizing query performance across multiple engines like Spark, Trino, and Flink.
Additionally, we will examine how Apache Iceberg can be leveraged across different cloud providers, making it a powerful choice for companies with data lakes distributed across multiple cloud storage systems without getting locked in. By decoupling storage from compute and providing a consistent table format, Iceberg enables seamless interoperability, cost optimization, and simplified data governance. Attendees will gain insights into how Iceberg improves data reliability, reduces operational complexity, and unlocks new possibilities for analytics and machine learning in multi-cloud environments.
Finally, we will provide an overview of the upcoming Apache Iceberg version 3 and its new features, offering a glimpse into the future of this evolving technology.