AWS Glue 6.0 now available with 30% lower price and full Apache Iceberg v3 support

AWS Glue 6.0 is here, and it brings meaningful improvements to your data pipeline economics. Built on Apache Spark 4.1, Python 3.12, and Scala 2.13, the new runtime delivers a 30% cost reduction compared to previous Glue versions—a significant win if you’re running data transformation jobs at scale. Beyond pricing, native Apache Iceberg v3 support means your data lake architecture gains better performance, schema evolution, and time-travel capabilities without additional workarounds.

Here’s what’s changed under the hood. Glue 6.0 uses a fully modernized runtime that takes advantage of Spark 4.1’s performance enhancements, including improved query optimization and memory management. Python 3.12 brings speed improvements and better resource utilization, while Scala 2.13 ensures compatibility with modern data frameworks. The result is the same job running faster and consuming fewer resources—hence the 30% cost reduction. Apache Iceberg v3 integration means you’re no longer bolting on Iceberg support through external libraries; it’s a first-class citizen in Glue, handling ACID transactions, schema evolution, and hidden partitioning natively.

Consider a practical scenario: you’re running daily ETL jobs that process clickstream data from S3, transform it using Glue, and write partitioned results back to S3. With Glue 6.0 and Iceberg v3, you can now evolve your schema without rewriting partitions, query historical data snapshots for auditing, and do it all 30% cheaper than before. Similarly, if you’re consolidating data from multiple sources with evolving schemas—common in data lake consolidation projects—Iceberg’s schema evolution means your Glue jobs need less custom logic to handle schema mismatches.

The practical takeaway: if you’re still using Glue 5.x, upgrading to 6.0 is worth testing in a non-production job first. Check your job configurations, especially if you’re using custom Python libraries or Scala dependencies, but the modernized runtime is generally backward compatible. The cost savings alone justify the effort, and the Iceberg native support removes friction from data lake architectures that increasingly rely on open table formats. This is a straightforward upgrade that pays for itself through lower execution costs and cleaner data pipeline code.