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AWS S3 Tables and Apache Iceberg V3 Change Everything
How automated table maintenance and new data types are changing cloud lakehouses — and what engineers need to know before upgrading.
A data lake can store petabytes of information and still be surprisingly expensive to operate. Streaming pipelines generate small files. Updates create additional metadata. Historical snapshots accumulate. Query engines spend time planning scans before they even begin processing data. And the team responsible for the platform ends up maintaining a growing collection of jobs just to keep tables healthy.
This is one of the less glamorous challenges of data engineering: storing data is easy; keeping analytical data efficient, consistent, and maintainable is much harder.
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Now, let’s get back to the topic.
Amazon S3 Tables and Apache Iceberg V3 offer a new approach to that problem. AWS provides managed table storage and maintenance, while Iceberg V3 introduces capabilities for richer data types, row-level changes, and incremental processing.
