"Earlier, Amazon Redshift RA3 systems operated as two separate engines, with Redshift handling warehouse data and Spectrum handling S3 data lake queries. When a query required both, AWS had to coordinate between the two systems, which added complexity, slowed performance, and made Spectrum scan costs unpredictable,"
"The new RG instances combine those worlds into one integrated engine running directly inside Redshift itself. That means Iceberg, Parquet, and S3 lake data can now be queried natively alongside warehouse data with less movement, lower overhead, and better performance optimization while also eliminating separate Spectrum per-scan charges,"
"The separate Spectrum charges, the analyst further added, were increasingly becoming a pain point for enterprises as AI workloads drove higher query volumes, more machine-generated analytics, and greater data-processing demands, with many customers disliking Spectrum's separate scan-based pricing because of the possibility of sudden bill spikes."
AWS released Graviton-powered Amazon Redshift RG instances designed to reduce analytics costs and operational complexity in lakehouse architectures. The instances use an integrated data lake query engine that runs SQL analytics across Redshift warehouse data and Amazon S3 data lakes. Previously, Redshift RA3 systems used separate engines, with Redshift handling warehouse data and Spectrum handling S3 queries, requiring coordination when queries spanned both. The RG instances combine these into one engine running inside Redshift, enabling native querying of Iceberg, Parquet, and S3 lake data alongside warehouse data. This approach reduces data movement and overhead, improves performance optimization, and eliminates separate Spectrum per-scan charges that could cause unpredictable bill spikes as AI workloads increase query volumes.
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