Today, AWS announces a new bi-directional, zero-copy integration between AWS Analytics services and Workday Data Cloud. Instead of building and maintaining ETL pipelines to replicate data between systems, joint customers can now query across their AWS and Workday datasets in place, without moving or copying data, while retaining existing security controls in both environments. From AWS, you can use Amazon SageMaker Unified Studio, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon Athena to query Workday data through Workday Live Data Query or federate to Apache Iceberg tables in Workday Data Cloud through catalog federation. Configure a connection in SageMaker Unified Studio, AWS Glue, or AWS Lake Formation consoles and begin querying immediately. Access is governed by AWS and Workday permissions. From Workday, you can register your AWS Glue Data Catalog Iceberg REST endpoint as an external catalog in Workday Analytics and query your AWS Iceberg tables directly. Common use cases include joining workforce data with business data for talent analytics, workforce planning, and machine learning. This integration is available in all AWS commercial regions where AWS Glue is available. To learn more, see the AWS Glue User Guide, the Workday Data Cloud integration with AWS on Workday Data Cloud, and the AWS Big Data Blog post.

Today, AWS announces a new bi-directional, zero-copy integration between AWS Analytics services and Workday Data Cloud. Instead of building and maintaining ETL pipelines to replicate data between systems, joint customers can now query across their AWS and Workday datasets in place, without moving or copying data, while retaining existing security controls in both environments. From AWS, you can use Amazon SageMaker Unified Studio, Amazon EMR, AWS Glue, Amazon Redshift, and Amazon Athena to query Workday data through Workday Live Data Query or federate to Apache Iceberg tables in Workday Data Cloud through catalog federation. Configure a connection in SageMaker Unified Studio, AWS Glue, or AWS Lake Formation consoles and begin querying immediately. Access is governed by AWS and Workday permissions. From Workday, you can register your AWS Glue Data Catalog Iceberg REST endpoint as an external catalog in Workday Analytics and query your AWS Iceberg tables directly. Common use cases include joining workforce data with business data for talent analytics, workforce planning, and machine learning. This integration is available in all AWS commercial regions where AWS Glue is available. To learn more, see the https://docs.aws.amazon.com/glue/latest/dg/connecting-to-data-workday.html, the https://doc.workday.com/admin-guide/en-us/workday-data-cloud/data-out/workday-data-lake/workday-data-lake.html, and the https://aws.amazon.com/blogs/big-data/introducing-the-workday-data-cloud-integration-with-aws/.

Amazon Redshift announces automated cross-region snapshot copy for Amazon Redshift. You can now have your Amazon Redshift provisioned cluster or serverless workgroup automatically backed up to the target AWS region(s) you choose. The automated cross-region snapshot copy feature will automatically, always copy the most recent snapshot with no manual intervention, continuously and incrementally and ensure your cluster is backed-up to the destination region(s), giving you additional options for business continuity. The benefit to enabling the new automated cross-region snapshot copy feature is that you do not need to manually intervene to initiate a cross-region snapshot copy when an unplanned outage event occurs. Also, this feature takes the most recent snapshot available on your system to copy to the destination region, so you don't have to wait for the next snapshot to be taken, making it independent of your snapshot schedule on your local cluster. To configure cross-region snapshot copy, you need to enable the automated cross-region snapshot copy feature for each cluster and configure the destination region(s) to copy snapshots and how long to keep copied automated snapshots in the destination Region. You can choose the retention period for automated snapshots in the destination region. User snapshots are retained until you explicitly delete them. To restore the snapshot in the destination region, use the snapshot restore feature to a new provisioned cluster or serverless workgroup. Snapshots copied with the automated cross-region snapshot copy feature have names prefixed with `copy` in the destination region. This feature is available in all regions AWS Commercial and GovCloud (US) regions. For more details on configuration and usage see the Amazon Redshift documentation sections for cross-region snapshot copy: Amazon Redshift snapshots and backups. For details on restoring a cluster from a snapshot, see the documentation for Restoring a cluster from a snapshot. For pricing information, please review the 'Backup and storage' and 'Data transfer' sections of the Amazon Redshift pricing page.

https://aws.amazon.com/redshift/announces automated cross-region snapshot copy for Amazon Redshift. You can now have your Amazon Redshift provisioned cluster or serverless workgroup automatically backed up to the target AWS region(s) you choose. The automated cross-region snapshot copy feature will automatically, always copy the most recent snapshot with no manual intervention, continuously and incrementally and ensure your cluster is backed-up to the destination region(s), giving you additional options for business continuity. The benefit to enabling the new automated cross-region snapshot copy feature is that you do not need to manually intervene to initiate a cross-region snapshot copy when an unplanned outage event occurs. Also, this feature takes the most recent snapshot available on your system to copy to the destination region, so you don't have to wait for the next snapshot to be taken, making it independent of your snapshot schedule on your local cluster. To configure cross-region snapshot copy, you need to enable the automated cross-region snapshot copy feature for each cluster and configure the destination region(s) to copy snapshots and how long to keep copied automated snapshots in the destination Region. You can choose the retention period for automated snapshots in the destination region. User snapshots are retained until you explicitly delete them. To restore the snapshot in the destination region, use the snapshot restore feature to a new provisioned cluster or serverless workgroup. Snapshots copied with the automated cross-region snapshot copy feature have names prefixed with `copy` in the destination region. This feature is available in all regions AWS Commercial and GovCloud (US) regions. For more details on configuration and usage see the Amazon Redshift documentation sections for cross-region snapshot copy: https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-snapshots.html. For details on restoring a cluster from a snapshot, see the documentation for https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-snapshot-restore-cluster-from-snapshot.html. For pricing information, please review the 'Backup and storage' and 'Data transfer' sections of the https://aws.amazon.com/redshift/pricing/.

AWS Backup restore testing now supports Amazon Redshift. Restore testing helps you automate and periodically run restore tests of your backups, so you can confirm that your Amazon Redshift backups restore successfully and measure how long the restores take. You can now include Amazon Redshift in your restore testing plans alongside other supported AWS resources across storage, compute, and database services. Restore testing plans automatically select recovery points, run restores on a schedule, and measure restore job completion time against your recovery time objectives (RTO). This helps you evaluate recovery readiness and meet regulatory and compliance requirements for disaster recovery and business continuity. To get started, create a restore testing plan and assign your Amazon Redshift resources by Amazon Resource Name (ARN) or tag. Choose whether each test restores the latest or a random eligible recovery point. Optionally, use restore testing validation to confirm the restored resource before AWS Backup deletes the test resources. This support is available in all AWS Regions where both restore testing and Amazon Redshift are supported by AWS Backup. You can use the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs. For a complete list of supported Regions and features, visit the AWS Backup documentation. To learn more about restore testing, visit the product page and pricing page.

AWS Backup restore testing now supports Amazon Redshift. Restore testing helps you automate and periodically run restore tests of your backups, so you can confirm that your Amazon Redshift backups restore successfully and measure how long the restores take. You can now include Amazon Redshift in your restore testing plans alongside other supported AWS resources across storage, compute, and database services. Restore testing plans automatically select recovery points, run restores on a schedule, and measure restore job completion time against your recovery time objectives (RTO). This helps you evaluate recovery readiness and meet regulatory and compliance requirements for disaster recovery and business continuity. To get started, create a restore testing plan and assign your Amazon Redshift resources by Amazon Resource Name (ARN) or tag. Choose whether each test restores the latest or a random eligible recovery point. Optionally, use restore testing validation to confirm the restored resource before AWS Backup deletes the test resources. This support is available in all AWS Regions where both restore testing and Amazon Redshift are supported by AWS Backup. You can use the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs. For a complete list of supported Regions and features, visit the https://docs.aws.amazon.com/aws-backup/latest/devguide/backup-feature-availability.html#features-by-region. To learn more about restore testing, visit the https://docs.aws.amazon.com/aws-backup/latest/devguide/restore-testing.html and https://aws.amazon.com/backup/pricing/.

Amazon Redshift long-term retention for system table data through native integration with Amazon S3 Tables is now available in additional AWS Regions. With this feature, you can configure your Redshift system table data retention beyond the current 7-day limit to meet your compliance, auditing, and observability requirements. Once enabled, AWS automatically writes system table data to S3 Tables in Apache Iceberg format and manages partitioning, compaction, and retention. Customers use Redshift system tables to monitor query performance, audit data warehouse activity, and meet compliance requirements. With this feature, system table data is replicated automatically, eliminating the need for custom extract transform load (ETL) pipelines and any resource contention with your production workloads. If you operate multiple data warehouses, you can consolidate their system table data into a single location for cross-warehouse observability and analysis. Because the data is stored in the open Apache Iceberg format, you can query it through Redshift, Amazon Athena, DuckDB, or any Iceberg-compatible engine, and build observability dashboards using AWS services or third-party tools without additional operations overhead. Additionally, the AWS Agent Toolkit provides skills for querying system table data to surface performance insights and optimization recommendations. This feature is available for Amazon Redshift Provisioned RG and RA3 instances and Amazon Redshift Serverless in the following additional AWS Regions: Asia Pacific (New Zealand), Canada West (Calgary), AWS GovCloud (US-East), and AWS GovCloud (US-West). To learn more, visit our documentation or the blog.

https://aws.amazon.com/redshift/ long-term retention for system table data through native integration with https://aws.amazon.com/s3/features/tables/ is now available in additional AWS Regions. With this feature, you can configure your Redshift system table data retention beyond the current 7-day limit to meet your compliance, auditing, and observability requirements. Once enabled, AWS automatically writes system table data to S3 Tables in https://aws.amazon.com/what-is/apache-iceberg/ and manages partitioning, compaction, and retention. Customers use Redshift system tables to monitor query performance, audit data warehouse activity, and meet compliance requirements. With this feature, system table data is replicated automatically, eliminating the need for custom extract transform load (ETL) pipelines and any resource contention with your production workloads. If you operate multiple data warehouses, you can consolidate their system table data into a single location for cross-warehouse observability and analysis. Because the data is stored in the open Apache Iceberg format, you can query it through Redshift, https://aws.amazon.com/athena/, https://duckdb.org/, or any Iceberg-compatible engine, and build observability dashboards using AWS services or third-party tools without additional operations overhead. Additionally, the https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills provides skills for querying system table data to surface performance insights and optimization recommendations. This feature is available for Amazon Redshift Provisioned https://aws.amazon.com/redshift/features/rg/ and https://aws.amazon.com/redshift/features/ra3/ instances and Amazon Redshift https://aws.amazon.com/redshift/redshift-serverless/ in the following additional AWS Regions: Asia Pacific (New Zealand), Canada West (Calgary), AWS GovCloud (US-East), and AWS GovCloud (US-West). To learn more, visit our https://docs.aws.amazon.com/redshift/latest/mgmt/system-table-s3-tables.html or the https://aws.amazon.com/blogs/big-data/long-term-system-tables-retention-in-amazon-redshift-with-amazon-s3-tables/.

Some numbers are hard to picture until you sit with them for a moment. Last week we published the Prime Day 2026 metrics, and the one that stuck with me is 2.3 quadrillion: the daily requests Amazon ElastiCache peaked at during the event. Prime Day ran back in June, so this post always lands as […]

Amazon Redshift now supports the creation and refresh of Apache Iceberg materialized views. Materialized views pre-compute expensive joins and aggregations once and store the results in an Apache Iceberg table in Amazon S3 or Amazon S3 table buckets, registered in the AWS Glue Data Catalog. Materialized views are created using familiar SQL — CREATE MATERIALIZED VIEW ... USING ICEBERG and the results are instantly queryable by any Iceberg-compatible engine, including Amazon Athena, Apache Spark on Amazon EMR and AWS Glue, and third-party engines such as Trino, or Snowflake. Redshift keeps them current by recomputing only what has changed with manual incremental refresh, and because the results are Iceberg tables in the Glue Data Catalog, they are governed and discovered like any other catalog table. Data teams often build analytics in stages, stitching together different engines to clean and transform raw data before serving it. This adds pipeline orchestration overhead and can introduce semantic differences between engines. Iceberg materialized views deliver value in two ways. First, instead of hundreds of users and teams re-running the same expensive joins and aggregations, and re-scanning source tables on every query, you compute the result once and everyone reads the precomputed table. Second, you can run an end-to-end pipeline using a single engine like Apache Spark and now Amazon Redshift, and every downstream consumer shares the same open result without the overhead of orchestrating multiple engines. Either way, the output is an open Iceberg table that any engine can read without copies or conversion. You can still load these tables into Redshift Managed Storage (RMS) as native RMS materialized views for your most performance-sensitive dashboards. You can create Iceberg Materialized Views in any region where Redshift Serverless and provisioned Graviton instances are supported. To learn more, see Materialized views stored as Apache Iceberg tables in the Amazon Redshift Database Developer Guide, the CREATE MATERIALIZED VIEW command reference, and the Materialize once, query anywhere blog post.

Amazon Redshift now supports the creation and refresh of Apache Iceberg materialized views. Materialized views pre-compute expensive joins and aggregations once and store the results in an Apache Iceberg table in Amazon S3 or Amazon S3 table buckets, registered in the AWS Glue Data Catalog. Materialized views are created using familiar SQL — CREATE MATERIALIZED VIEW ... USING ICEBERG and the results are instantly queryable by any Iceberg-compatible engine, including Amazon Athena, Apache Spark on Amazon EMR and AWS Glue, and third-party engines such as Trino, or Snowflake. Redshift keeps them current by recomputing only what has changed with manual incremental refresh, and because the results are Iceberg tables in the Glue Data Catalog, they are governed and discovered like any other catalog table. Data teams often build analytics in stages, stitching together different engines to clean and transform raw data before serving it. This adds pipeline orchestration overhead and can introduce semantic differences between engines. Iceberg materialized views deliver value in two ways. First, instead of hundreds of users and teams re-running the same expensive joins and aggregations, and re-scanning source tables on every query, you compute the result once and everyone reads the precomputed table. Second, you can run an end-to-end pipeline using a single engine like Apache Spark and now Amazon Redshift, and every downstream consumer shares the same open result without the overhead of orchestrating multiple engines. Either way, the output is an open Iceberg table that any engine can read without copies or conversion. You can still load these tables into Redshift Managed Storage (RMS) as native RMS materialized views for your most performance-sensitive dashboards. You can create Iceberg Materialized Views in any region where Redshift Serverless and provisioned Graviton instances are supported. To learn more, see https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-iceberg.html in the Amazon Redshift Database Developer Guide, the https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-create-sql-command.html command reference, and the https://aws.amazon.com/blogs/big-data/materialize-once-query-anywhere-introducing-iceberg-materialized-views-in-amazon-redshift/ blog post.

Amazon Redshift now supports querying Amazon S3 data lake tables located in a different AWS Region. With enhanced VPC routing, data lake query traffic between Amazon S3 and Amazon Redshift stays within your own VPC. Both capabilities are powered by the integrated data lake query engine that runs directly on the compute of RG provisioned and Serverless clusters. These features are aimed at enterprises with globally distributed data and at security-sensitive customers who need tight control over how their data travels. With cross-Region support, you can use Amazon Redshift to query S3 data in another Region directly, without first copying or replicating it. This makes it easier to run global analytics, consolidate reporting across Regions, and query data that must remain in a specific Region for residency requirements. Cross-Region queries incur standard data transfer charges. Because the query engine runs on your cluster's compute, data moving between Amazon S3 and Amazon Redshift flows entirely over your VPC when you use enhanced VPC routing, never over public networks. Regulated industries such as financial services, healthcare, and government benefit from this feature as it keeps data lake queries within the network boundaries their compliance requirements demand.  Both capabilities work with the integrated data lake query engine, which is available in all regions where RG provisioned and Serverless clusters are available. To learn more, see Querying your data lake tables with enhanced VPC routing in the Amazon Redshift documentation.

Amazon Redshift now supports querying Amazon S3 data lake tables located in a different AWS Region. With enhanced VPC routing, data lake query traffic between Amazon S3 and Amazon Redshift stays within your own VPC. Both capabilities are powered by the integrated data lake query engine that runs directly on the compute of RG provisioned and Serverless clusters. These features are aimed at enterprises with globally distributed data and at security-sensitive customers who need tight control over how their data travels. With cross-Region support, you can use Amazon Redshift to query S3 data in another Region directly, without first copying or replicating it. This makes it easier to run global analytics, consolidate reporting across Regions, and query data that must remain in a specific Region for residency requirements. Cross-Region queries incur standard data transfer charges. Because the query engine runs on your cluster's compute, data moving between Amazon S3 and Amazon Redshift flows entirely over your VPC when you use enhanced VPC routing, never over public networks. Regulated industries such as financial services, healthcare, and government benefit from this feature as it keeps data lake queries within the network boundaries their compliance requirements demand.  Both capabilities work with the integrated data lake query engine, which is available in all regions where RG provisioned and Serverless clusters are available. To learn more, see  https://docs.aws.amazon.com/redshift/latest/mgmt/spectrum-enhanced-vpc.html#spectrum-enhanced-vpc-integrated-engine in the https://docs.aws.amazon.com/redshift/.

Amazon Redshift now makes it easier to troubleshoot and audit queries on data protected by federated permissions with fine-grained access control (FGAC). Using the new DEBUG permission, data owners can let specific identities see unredacted secure logging records across multiple Redshift data warehouses. With Amazon Redshift federated permissions, you define data permissions once, and Redshift enforces them automatically across every Redshift warehouse in your AWS account. When a query accesses FGAC-protected data, secure logging redacts sensitive values in system table records, such as rewritten query text, error messages, and object names. This protects the producer's data from consumers. At the same time, authorized auditors and administrators need visibility into these records to troubleshoot queries and meet compliance requirements, without turning off secure logging. With the new DEBUG permission, a superuser or database owner can choose which identities see these records without redaction. You grant DEBUG with the standard Redshift GRANT command, typically to an IAM user, IAM role, or IAM Identity Center user or group, so that identity can see unredacted records for its own queries. You can also grant DEBUG to the administrators of a consumer account for full log visibility during troubleshooting and audit. Records authorized for account administrators also stay unredacted when you export Redshift system table data to Amazon S3 Tables. You get precise, auditable control over who can see logs, and secure logging stays on. The feature is available in all AWS Regions where Amazon Redshift is supported. To learn more, see federated permissions,  Usage Notes and secure logging in the Amazon Redshift documentation.

Amazon Redshift now makes it easier to troubleshoot and audit queries on data protected by federated permissions with fine-grained access control (FGAC). Using the new DEBUG permission, data owners can let specific identities see unredacted secure logging records across multiple Redshift data warehouses. With Amazon Redshift federated permissions, you define data permissions once, and Redshift enforces them automatically across every Redshift warehouse in your AWS account. When a query accesses FGAC-protected data, secure logging redacts sensitive values in system table records, such as rewritten query text, error messages, and object names. This protects the producer's data from consumers. At the same time, authorized auditors and administrators need visibility into these records to troubleshoot queries and meet compliance requirements, without turning off secure logging. With the new DEBUG permission, a superuser or database owner can choose which identities see these records without redaction. You grant DEBUG with the standard Redshift GRANT command, typically to an IAM user, IAM role, or IAM Identity Center user or group, so that identity can see unredacted records for its own queries. You can also grant DEBUG to the administrators of a consumer account for full log visibility during troubleshooting and audit. Records authorized for account administrators also stay unredacted when you export Redshift system table data to Amazon S3 Tables. You get precise, auditable control over who can see logs, and secure logging stays on. The feature is available in all AWS Regions where Amazon Redshift is supported. To learn more, see https://docs.aws.amazon.com/redshift/latest/dg/federated-permissions.html,  https://docs.aws.amazon.com/redshift/latest/dg/r_GRANT-usage-notes.html and https://docs.aws.amazon.com/redshift/latest/mgmt/db-auditing-secure-logging.html in the Amazon Redshift documentation.

Amazon Redshift RG instances, powered by AWS Graviton processors, are now available in the AWS Europe (Zurich) Region. RG instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift's custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, enabling you to run SQL analytics across your data warehouse and data lake using a single engine. RG instances are available in four instance sizes, rg.large, rg.xlarge, rg.4xlarge and rg.12xlarge. Customers with existing RA3 clusters can upgrade them to RG using Snapshot & Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with All Upfront, Partial Upfront, and No Upfront payment options. For pricing details, visit the https://aws.amazon.com/redshift/pricing/. To get started, refer to the following resources: https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-clusters.html#rs-rg-nodes-table https://docs.aws.amazon.com/redshift/latest/mgmt/managing-cluster-considerations.html#rs-upgrading-to-ra3 https://aws.amazon.com/redshift/pricing/

Amazon Redshift RG instances, powered by AWS Graviton processors, are now available in the AWS Europe (Zurich) Region. RG instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift's custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, enabling you to run SQL analytics across your data warehouse and data lake using a single engine. RG instances are available in four instance sizes, rg.large, rg.xlarge, rg.4xlarge and rg.12xlarge. Customers with existing RA3 clusters can upgrade them to RG using Snapshot & Restore, Elastic Resize, or Classic Resize. RG instances are available with flexible pricing options, including On-Demand, and 1-year and 3-year Reserved Instances with All Upfront, Partial Upfront, and No Upfront payment options. For pricing details, visit the Amazon Redshift pricing page. To get started, refer to the following resources: Amazon Redshift RG Instance Documentation RA3 to RG Upgrade Guide Amazon Redshift pricing page

Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its […]

Amazon Redshift rg.large instances, powered by AWS Graviton processors, now support single-node clusters. Single-node support for rg.large clusters is available on P204 or later patch versions. Customers can now create a single-node rg.large cluster for smaller workloads that do not require high availability, offering a cost-effective option to conduct proofs of concept and tests quickly. RG instances deliver up to 2.4x faster performance running data warehouse and data lake workloads when compared to previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift's custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, turning on SQL analytics across your data warehouse and data lake using a single engine. These RG instances are available in the following AWS Regions: Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Osaka), Asia Pacific (Mumbai), Asia Pacific (Hyderabad), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Jakarta), Asia Pacific (Melbourne), Asia Pacific (Malaysia), Asia Pacific (Taipei), Asia Pacific (Thailand), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (Spain), Europe (Ireland), Europe (London), Europe (Paris), South America (São Paulo), US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), AWS GovCloud (US-East), AWS GovCloud (US-West) and Mexico (Central). To learn more, visit the https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-clusters.html#rs-rg-nodes-table, the https://docs.aws.amazon.com/redshift/latest/mgmt/managing-cluster-considerations.html#rs-upgrading-to-ra3, and the https://aws.amazon.com/redshift/pricing/.

Amazon Redshift rg.large instances, powered by AWS Graviton processors, now support single-node clusters. Single-node support for rg.large clusters is available on P204 or later patch versions. Customers can now create a single-node rg.large cluster for smaller workloads that do not require high availability, offering a cost-effective option to conduct proofs of concept and tests quickly. RG instances deliver up to 2.4x faster performance running data warehouse and data lake workloads when compared to previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift's custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes, turning on SQL analytics across your data warehouse and data lake using a single engine. These RG instances are available in the following AWS Regions: Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Osaka), Asia Pacific (Mumbai), Asia Pacific (Hyderabad), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Jakarta), Asia Pacific (Melbourne), Asia Pacific (Malaysia), Asia Pacific (Taipei), Asia Pacific (Thailand), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (Spain), Europe (Ireland), Europe (London), Europe (Paris), South America (São Paulo), US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), AWS GovCloud (US-East), AWS GovCloud (US-West) and Mexico (Central). To learn more, visit the Amazon Redshift RG Instance Documentation, the RA3 to RG Upgrade Guide, and the Amazon Redshift pricing page.

Amazon Redshift now supports AWS IAM Identity Center authentication for provisioned clusters and serverless workgroups configured with enhanced VPC routing (EVR). You can access Amazon Redshift with single sign-on with your corporate credentials, and the traffic traverses Amazon Virtual Private Cloud (Amazon VPC) and stays on the AWS network. This is valuable for customers with data residency, regulatory, or network-isolation requirements that mandate no public internet egress for analytics. With Redshift EVR, all traffic between your Redshift warehouse and other AWS services goes through your VPC, where you can govern it with security groups, network ACLs, and endpoint policies, and observe it in VPC Flow Logs. With this launch, Redshift validates and exchanges IAM Identity Center tokens over AWS PrivateLink interface VPC endpoints from inside your VPC, so authentication and authorization follows the same governed network path as the rest of your Redshift traffic. This feature also supports IAM Identity Center multi-Region replication for customers running Redshift in a different Region than their primary Identity Center instance. Read the Amazon Redshift enhanced VPC routing documentation and the blog post to get started. This capability is available in all AWS Regions where both Amazon Redshift and IAM Identity Center are available.

Amazon Redshift now supports AWS IAM Identity Center authentication for provisioned clusters and serverless workgroups configured with enhanced VPC routing (EVR). You can access Amazon Redshift with single sign-on with your corporate credentials, and the traffic traverses Amazon Virtual Private Cloud (Amazon VPC) and stays on the AWS network. This is valuable for customers with data residency, regulatory, or network-isolation requirements that mandate no public internet egress for analytics. With Redshift EVR, all traffic between your Redshift warehouse and other AWS services goes through your VPC, where you can govern it with security groups, network ACLs, and endpoint policies, and observe it in VPC Flow Logs. With this launch, Redshift validates and exchanges IAM Identity Center tokens over AWS PrivateLink interface VPC endpoints from inside your VPC, so authentication and authorization follows the same governed network path as the rest of your Redshift traffic. This feature also supports https://docs.aws.amazon.com/singlesignon/latest/userguide/multi-region-iam-identity-center.html for customers running Redshift in a different Region than their primary Identity Center instance. Read the Amazon Redshift enhanced VPC routing https://docs.aws.amazon.com/redshift/latest/mgmt/redshift-iam-access-control-idp-connect-evr.html and the https://aws.amazon.com/blogs/big-data/integrate-amazon-redshift-and-iam-identity-center-with-enhanced-vpc-routing/ to get started. This capability is available in all AWS Regions where both Amazon Redshift and IAM Identity Center are available. 

Amazon Redshift now supports reading from and writing to Apache Iceberg v3 tables in your data lake. Apache Iceberg is an open table format, and its v3 release adds several new features. With this launch, Amazon Redshift introduces support for default column values, row lineage, and deletion vectors. With default column values, you can define an initial value that Amazon Redshift applies when no value is provided, simplifying schema evolution as you add columns to existing tables. Row lineage exposes pseudo-columns that track each row's identity and last-updated sequence number, so you can build incremental pipelines and CDC (change data capture) workflows that process only modified rows. Deletion vectors replace Iceberg v2's positional delete files with compact compressed bitmaps, delivering faster reads and writes for high-frequency update and delete workloads such as compliance-driven record removal. You can create a v3 table with CREATE TABLE <table> ... USING ICEBERG TABLE PROPERTIES ('format-version' = '3'), or upgrade an existing v2 table in place with ALTER TABLE <table> SET TABLE PROPERTIES ('format-version' = '3'). Amazon Redshift's Graviton based provisioned and serverless clusters support the new v3 format. To learn more, see https://docs.aws.amazon.com/redshift/latest/dg/iceberg-v3-features.htmlin the https://docs.aws.amazon.com/redshift/.

Amazon Redshift now supports reading from and writing to Apache Iceberg v3 tables in your data lake. Apache Iceberg is an open table format, and its v3 release adds several new features. With this launch, Amazon Redshift introduces support for default column values, row lineage, and deletion vectors. With default column values, you can define an initial value that Amazon Redshift applies when no value is provided, simplifying schema evolution as you add columns to existing tables. Row lineage exposes pseudo-columns that track each row's identity and last-updated sequence number, so you can build incremental pipelines and CDC (change data capture) workflows that process only modified rows. Deletion vectors replace Iceberg v2's positional delete files with compact compressed bitmaps, delivering faster reads and writes for high-frequency update and delete workloads such as compliance-driven record removal. You can create a v3 table with CREATE TABLE <table> ... USING ICEBERG TABLE PROPERTIES ('format-version' = '3'), or upgrade an existing v2 table in place with ALTER TABLE <table> SET TABLE PROPERTIES ('format-version' = '3'). Amazon Redshift's Graviton based provisioned and serverless clusters support the new v3 format. To learn more, see Apache Iceberg v3 features in the Amazon Redshift Documentation.

Amazon Redshift now integrates with the Agent Toolkit for AWS, enabling you to build, query, troubleshoot, and migrate to Amazon Redshift data warehouses and date lakes directly from AI agents such as Claude Code, Kiro, and Cursor. The integration pairs the AWS MCP (Model Context Protocol) server, which provides authenticated AWS API execution on your behalf — with Redshift skills: curated packages of tested procedures and reference material that help AI agents complete Redshift tasks more effectively. The Redshift skills cover SQL syntax references to reduce query generation errors, metadata discovery to explore schemas and data without writing SQL by hand, data loading patterns, materialized view best practices, function and data type guidance, and extensions such as Qualify, Pivot, and Super. It also guides end-to-end data warehouse migrations to Amazon Redshift, including discovery, schema and SQL conversion, data movement, validation, and performance comparison. We will continue to expand these skills with additional capabilities over time. The skills work with provisioned clusters and Serverless workgroups, require no changes to existing infrastructure, and are available at no additional charge in all AWS Regions where Amazon Redshift and the AWS MCP Server are offered. To get started, install the aws-data-analytics plugin in your agent, which bundles the MCP Server configuration and Redshift skills in a single step. Agents with MCP Server access can also discover and load skills at runtime without pre-installation. For setup instructions, see the Agent Toolkit documentation or the Amazon Redshift skills documentation.