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Unlocking Performance: Amazon Redshift's Graviton-Powered RG Instances

5 min read AWS BlogMay 12, 2026Reviewed for accuracy
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Amazon Redshift has introduced RG instances powered by AWS Graviton, addressing the need for enhanced performance in data warehouse workloads. This innovation allows for efficient execution of data lake queries directly on cluster nodes, eliminating the need for Amazon Redshift Spectrum. By keeping data lake queries within your VPC boundary, you benefit from existing IAM roles and avoid per-terabyte scanning charges, making it a cost-effective solution for data management.

The RG instances not only streamline your data processing but also support advanced data formats like Apache Iceberg and Apache Parquet. Queries on Apache Iceberg can achieve performance improvements of up to 2.4x compared to RA3 instances, while Apache Parquet offers up to 1.5x faster query performance. This means you can handle larger datasets with greater efficiency, ultimately leading to faster insights and decision-making in your organization.

In production, you should be aware of the recent update that removed the Middle East (UAE) region from available regions for these instances. This could affect deployment strategies if your workloads are region-specific. As you adopt RG instances, consider your existing architecture and how these new capabilities can be integrated seamlessly into your data workflows.

Key takeaways

  • Leverage AWS Graviton technology for better performance in data warehouse workloads.
  • Execute data lake queries directly on cluster nodes, eliminating the need for Amazon Redshift Spectrum.
  • Query Apache Iceberg with performance up to 2.4x faster than RA3 instances.
  • Utilize Apache Parquet for improved query speeds, achieving up to 1.5x faster performance.
  • Keep data lake queries within your VPC to avoid per-terabyte scanning charges.

Why it matters

This advancement significantly reduces costs associated with data lake queries while enhancing performance, allowing teams to derive insights faster and more efficiently.

When NOT to use this

The official docs don't call out specific anti-patterns here. Use your judgment based on your scale and requirements.

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