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Since 2013, Amazon Redshift has given the total energy of a knowledge warehouse within the cloud, at a fraction of the on-premises value. Each architectural technology—from dense compute to Amazon RA3 cases, from provisioned to Amazon Redshift Serverless—has made every question cheaper, sooner, and extra environment friendly than the final.
For over a decade, as knowledge volumes have grown and analytics necessities have advanced, organizations more and more leverage each knowledge warehouse tables for structured, frequently-accessed knowledge and knowledge lakes for cost-effective storage of numerous datasets. Add AI brokers to the combination they usually question your knowledge warehouse at a scale that dwarfs typical human utilization, resulting in spiraling operational prices.
Amazon Redshift has doubled down on its core strengths to fulfill the calls for of any workload — whether or not pushed by people or AI brokers. For instance, in March 2026, Amazon Redshift improved the efficiency of enterprise intelligence (BI) dashboards and ETL workloads by dashing up new queries by as much as 7 instances. This considerably improves the response instances of low-latency SQL queries, corresponding to these utilized in near-real-time analytics functions, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI brokers.
In the present day, we’re asserting Amazon Redshift RG cases, a brand new occasion household powered by AWS Graviton. RG cases ship higher efficiency, operating knowledge warehouse workloads as much as 2.2x as quick as RA3 cases at 30% cheaper price per vCPU. Their built-in knowledge lake question engine enables you to run SQL analytics throughout your knowledge warehouse and knowledge lake from a single engine with efficiency as much as 2.4x as quick as RA3 for Apache Iceberg and as much as 1.5x as quick as RA3 for Apache Parquet. This mix of velocity, value effectivity, and an built-in knowledge lake question engine makes Redshift RG cases well-suited to deal with the excessive question volumes and low-latency necessities of right this moment’s analytics and agentic AI workloads.
You’ll be able to examine new RG cases and present RA3 cases:
| Present RA3 Occasion | Really useful RG occasion | vCPU | Reminiscence (GB) | Main Use Case |
ra3.xlplus |
rg.xlarge |
4 | 32 | Small cluster departmental analytics |
ra3.4xlarge |
rg.4xlarge |
12 → 16 (1.33:1) | 96 GB → 128 GB (1.33:1) | Normal manufacturing workloads, medium knowledge volumes |
This strategy reduces whole analytics prices for purchasers operating mixed knowledge warehouse and knowledge lake workloads, whereas simplifying operations by way of a single system for querying each warehouse tables and Amazon Easy Storage Service (Amazon S3) knowledge lakes. We advocate utilizing the AWS Pricing Calculator together with your particular workload patterns to estimate financial savings.
Getting began with Amazon Redshift RG cases
You’ll be able to launch new clusters or migrate current clusters by way of the AWS Administration Console, AWS Command Line Interface (AWS CLI), or AWS API. The built-in knowledge lake question engine is enabled by default.
Within the Amazon Redshift console, you may select new RG cases whenever you create a cluster.
You’ll be able to migrate previous-generation cases to RG cases with optimum paths based mostly in your cluster configuration to estimate prices, validate compatibility, and automate execution.
- Elastic Resize—in-place migration with 10-Quarter-hour downtime for suitable configurations
- Snapshot and Restore—create a RG cluster from an RA3 snapshot. That is finest for purchasers who wish to make configuration adjustments throughout the migration
Your exterior tables, schemas, and question syntax—together with current Spectrum queries—stay unchanged. There isn’t any have to recreate exterior tables or modify software code. To study extra, go to the Redshift Administration Information.
Amazon Redshift now executes knowledge lake queries on cluster nodes—the identical compute that processes knowledge warehouse workloads. Because of this, Amazon Redshift Spectrum is now not required. Knowledge lake queries keep inside your VPC boundary, use current IAM roles, and incur zero per-terabyte scanning costs. This removes the $5/TB Spectrum scanning charges that beforehand added to whole Redshift prices.
Now obtainable
Amazon Redshift RG cases at the moment are obtainable within the following AWS Areas: US East (N. Virginia, Ohio), US West (N. California, Oregon), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Taiwan, Tokyo), Canada (Central), Europe (Frankfurt, Eire, Milan, London, Paris, Spain, Stockholm), and South America (São Paulo). For Regional availability and a future roadmap, go to the AWS Capabilities by Area. For Redshift Provisioned, you may choose On-Demand Situations with hourly billing and no commitments or select Reserved Situations for value financial savings. To study extra, go to the Amazon Redshift Pricing web page.
Give RG cases a attempt within the Redshift console and ship suggestions to AWS re:Submit for Amazon Redshift or by way of your common AWS Help contacts.
— Channy
Up to date 5/12/26: Center East (UAE) faraway from obtainable areas.
