Organizations are rapidly adopting Apache Iceberg for their data lakes because it supports petabyte-scale growth and performance with the flexibility to evolve schemas and partitions without costly rewrites. Its architecture enables modern data lake management via features like time travel and incremental processing. However, managing Iceberg datasets efficiently can become a challenge over time as data grows. Many organizations retain data for months or years, driven by regulatory requirements or long-term analytics needs, and struggle to balance performance and accessibility with affordability. Maintaining tables, optimizing file layouts, and implementing cost-effective retention policies becomes an ongoing effort that consumes resources and increases associated costs.
Amazon S3 Tables introduced purpose-built tabular storage and a new bucket type for Iceberg tables that makes it simple to store structured data in Amazon S3. S3 Tables automatically handle…
https://aws.amazon.com/blogs/storage/optimize-data-management-on-s3-tables-with-intelligent-tiering/
