Amazon Redshift has progressively deepened its integration with Apache Iceberg. Earlier this year we launched Amazon Redshift RG, powered by AWS Graviton, with a purpose-built, integrated vectorized query engine designed from the ground up for data lakes. Instead of sending scans to a separate fleet, RG runs them natively on the cluster using vectorized Parquet scans, a smart-prefetch I/O subsystem, partition- and file-level pruning, improved bloom filters, and automatic Iceberg statistics collection through JIT Analyze for better query plans. Together, these deliver up to 2.4x faster Apache Iceberg queries than RA3, at 30 percent lower cost per vCPU and with no per-terabyte scan charges on data lake queries. On top of that performance foundation, you can write directly to Iceberg tables with full ACID alignment using INSERT, CTAS, UPDATE, DELETE, and MERGE. You can govern access with AWS Identity and Access Management (IAM) permissions through the external schema’s IAM…
Materialize once, query anywhere: Introducing Iceberg materialized views in Amazon Redshift | Amazon Web Services
