“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,” said Pareekh Jain, principal analyst at Pareekh Consulting.
“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,” Jain added.
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.
The new instances could be AWS’ response to growing enterprise demand for AI-scale analytics platforms that avoid added architectural complexity, as rivals including Databricks, Snowflake, Google Cloud with BigQuery, and Microsoft through Microsoft Fabric push unified lakehouse platforms to reduce operational sprawl, Jain said.
