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.

Amazon DynamoDB zero-ETL integration with Amazon Redshift is now supported in the Asia Pacific (Taipei) region. This expansion enables customers to run high-performance analytics on their DynamoDB data in Amazon Redshift with no impact on production workloads running on DynamoDB.  Zero-ETL integrations help you derive holistic insights across many applications, break data silos in your organization, and gain significant cost savings and operational efficiencies. Now you can run enhanced analysis on your DynamoDB data with the rich capabilities of Amazon Redshift, such as high performance SQL, built-in ML and Spark integrations, materialized views with automatic and incremental refresh, and data sharing. Additionally, you can use history mode to easily run advanced analytics on historical data, build lookback reports, and build Type 2 Slowly Changing Dimension (SCD 2) tables on your historical data from DynamoDB, out-of-the-box in Amazon Redshift, without writing any code. The Amazon DynamoDB zero-ETL integration with Amazon Redshift is now available in Asia Pacific (Taipei), in addition to previously supported regions. For a complete list of supported regions, please refer to the AWS Region Table where Amazon Redshift is available. To learn more, visit the getting started guides for DynamoDB and Amazon Redshift. For more information on using history mode, we encourage you to visit our recent blog post here.

required access to the company's Redshift platform read more about Amazon Redshift gets new default settings to prevent data breaches

Amazon Redshift now supports federated permissions across multi-warehouse architectures Amazon Redshift now supports federated permissions, which simplify permissions management across multiple Redshift data warehouses. Customers are adopting multi-warehouse architectures to scale and isolate workloads and are looking for simplified, consistent permissions management across warehouses. With Redshift federated permissions, you define data permissions once from any Redshift warehouse and automatically enforce them across all warehouses in the account. Amazon Redshift warehouses with federated permissions are auto-mounted in every Redshift warehouse, and you can use existing workforce identities with AWS IAM Identity Center or use existing IAM roles to query data across warehouses. Regardless of which warehouse is used for querying, row-level, column-level, and masking controls always apply automatically, delivering fine-grained access compliance. You can get started by registering a Redshift Serverless namespace or Redshift provisioned cluster with AWS Glue Data Catalog and start querying across warehouses using Redshift Query Editor V2, or any supported SQL client. You get horizontal scalability with multiple warehouses by allowing you to add new warehouses without increasing governance complexity, as new warehouses automatically enforce permission policies and analysts immediately see all databases from registered warehouses. Amazon Redshift federated permissions is available at no additional cost in supported https://docs.aws.amazon.com/redshift/latest/dg/federated-permissions-considerations.html. To learn more, visit the http://docs.aws.amazon.com/redshift/latest/dg/federated-permissions.html documentation.

New capabilities help optimize application performance, analyze unlimited prefixes, and simplify metrics analysis through S3 Tables integration.

Effortlessly analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines and enabling seamless insights extraction.

Unifying data silos, Amazon SageMaker Lakehouse seamlessly integrates S3 data lakes and Redshift warehouses, enabling unified analytics and AI/ML on a single data copy through open Apache Iceberg APIs and fine-grained access controls.

Amazon Redshift RG instances, powered by AWS Graviton, run data warehouse and data lake workloads up to 2.4x as fast as RA3 instances at 30% lower price per vCPU. Its integrated data lake query engine supports open table formats such as Apache Iceberg.

Amazon Redshift now supports federated permissions across multi-warehouse architectures Amazon Redshift now supports federated permissions, which simplify permissions management across multiple Redshift data warehouses. Customers are adopting multi-warehouse architectures to scale and isolate workloads and are looking for simplified, consistent permissions management across warehouses. With Redshift federated permissions, you define data permissions once from any Redshift warehouse and automatically enforce them across all warehouses in the account. Amazon Redshift warehouses with federated permissions are auto-mounted in every Redshift warehouse, and you can use existing workforce identities with AWS IAM Identity Center or use existing IAM roles to query data across warehouses. Regardless of which warehouse is used for querying, row-level, column-level, and masking controls always apply automatically, delivering fine-grained access compliance. You can get started by registering a Redshift Serverless namespace or Redshift provisioned cluster with AWS Glue Data Catalog and start querying across warehouses using Redshift Query Editor V2, or any supported SQL client. You get horizontal scalability with multiple warehouses by allowing you to add new warehouses without increasing governance complexity, as new warehouses automatically enforce permission policies and analysts immediately see all databases from registered warehouses. Amazon Redshift federated permissions is available at no additional cost in supported AWS regions. To learn more, visit the Amazon Redshift documentation.

Amazon Redshift has enhanced its vacuum operations to support increased concurrency across different tables in data warehouses. This feature reduces maintenance time and improves resource utilization by allowing multiple vacuum operations to run simultaneously. Vacuum operations maintain optimal query performance by performing two critical functions: sorting table data and reclaiming disk space from deleted rows. Redshift already offers automatic vacuum operations to minimize the need for manual maintenance. Now, these operations run at a higher concurrency automatically managed by Redshift. Further, users can also run multiple manual vacuum operations concurrently on different tables across sessions. This enhancement also extends support to run automatic and manual vacuum operations simultaneously across various vacuum modes. Concurrent vacuum operations reduces the maintenance time and enables better resource utilization of the data warehouse, solving a key operational challenge for database administrators. This support for concurrent vacuum operations is available in all AWS Regions where Amazon Redshift is available. For more information, please refer to our documentation.

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Amazon Redshift has enhanced its vacuum operations to support increased concurrency across different tables in data warehouses. This feature reduces maintenance time and improves resource utilization by allowing multiple vacuum operations to run simultaneously. Vacuum operations maintain optimal query performance by performing two critical functions: sorting table data and reclaiming disk space from deleted rows. Redshift already offers automatic vacuum operations to minimize the need for manual maintenance. Now, these operations run at a higher concurrency automatically managed by Redshift. Further, users can also run multiple manual vacuum operations concurrently on different tables across sessions. This enhancement also extends support to run automatic and manual vacuum operations simultaneously across various vacuum modes. Concurrent vacuum operations reduces the maintenance time and enables better resource utilization of the data warehouse, solving a key operational challenge for database administrators. This support for concurrent vacuum operations is available in all AWS Regions where Amazon Redshift is available. For more information, please refer to our https://docs.aws.amazon.com/redshift/latest/dg/r_VACUUM_command.html#r_VACUUM_usage_notes_concurrent.

https://aws.amazon.com/redshift/ today announces the general availability of write capability to Apache Iceberg tables, enabling users to run analytics read and write queries for append-only workloads on Apache Iceberg tables within Amazon Redshift. Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service used by tens of thousands of customers. Whether your data is stored in operational data stores, data lakes, streaming engines or within your data warehouse, Amazon Redshift helps you quickly ingest, securely share data, and achieve the best performance at the best price. The Apache Iceberg open table format has been used by many customers to simplify data processing on rapidly expanding and evolving tables stored in data lakes. Customers have been using Amazon Redshift to run queries on data lake tables in various file and table formats, achieving a wide range of scalability across data warehouse and data lake workloads. Data lake use cases continue to evolve and become increasingly sophisticated, and require capabilities like transactional consistency for record-level updates and deletes while having seamless schema and partition evolution support. With this milestone Amazon Redshift now supports SQL DDL (data definition language) operations to CREATE an Apache Iceberg table, SHOW the table definition SQL, DROP the table and perform DML (data manipulation language) operations such as INSERT. You can continue to use Amazon Redshift to read from your Apache Iceberg tables in AWS Glue Data Catalog and perform write operations on those Apache Iceberg tables while other users or applications can safely run DML operations on your tables. Apache Iceberg support in Amazon Redshift is available in all AWS regions where Amazon Redshift is available. To get started, visit the documentation page for Amazon Redshift https://docs.aws.amazon.com/redshift/latest/dg/iceberg-writes.html.

Amazon Redshift Concurrency Scaling is now available in the AWS Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Osaka), Asia Pacific (Thailand), Europe (Milan), Middle East (Bahrain), Mexico (Central) and AWS GovCloud (US-West) regions. With the Amazon Redshift Concurrency Scaling feature, you can now support thousands of concurrent users and concurrent queries, with consistently fast query performance. Amazon Redshift Concurrency Scaling elastically scales query processing power to provide consistently fast performance for hundreds of concurrent queries. Concurrency Scaling resources are added to your Redshift cluster transparently in seconds, allowing for increased concurrency to process queries with minimal wait time. Amazon Redshift customers with an active Redshift cluster earn up to one hour of free Concurrency Scaling credits, which is sufficient for the concurrency needs of most customers. Concurrency scaling enables you to specify usage control, providing customers with predictable month-to-month costs, even during periods of fluctuating analytical demand. To enable Amazon Redshift Concurrency Scaling, set the Concurrency Scaling Mode to Auto in your https://console.aws.amazon.com/. You can allocate Concurrency Scaling usage to specific user groups and workloads, control the number of Concurrency Scaling clusters that can be used, and monitor Amazon CloudWatch performance and usage metrics. To learn more about concurrency scaling including regional-availability, see our https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling.html and https://aws.amazon.com/redshift/pricing/.

Today, Amazon Redshift announced the support for automatically and incrementally refreshable materialized views (MVs) on tables in a zero-ETL integration. This new feature eliminates the need for full refreshes, which previously required re-executing underlying select statements and rewriting all data in the MV. With this launch, you can leverage the power of zero-ETL integrations with automatically and incrementally refreshable MVs, enabling more efficient data updates and faster query responses. This feature automatically keeps MVs up-to-date as the underlying data changes, eliminating the need for manual refresh operations or complex ETL processes. Organizations handling high-volume, dynamic datasets can now access near real-time insights while significantly reducing their operational workload. To learn more and get started with zero-ETL integration, visit the getting started guides for Amazon Redshift. To learn more about refreshing a materialized view, visit the documentation.

Amazon Redshift now allows you to get started with Amazon Redshift Serverless with a lower data warehouse base capacity configuration of 4 Redshift Processing Units (RPUs). Amazon Redshift Serverless measures data warehouse capacity in RPUs, and you pay only for the duration of workloads you run in RPU-hours on a per-second basis. Previously, the minimum base capacity required to run Amazon Redshift Serverless was 8 RPUs. You can start using Amazon Redshift Serverless for as low as $1.50 per hour and pay only for the compute capacity your data warehouse consumes when it is active. Amazon Redshift Serverless enables users to run and scale analytics without managing data warehouse clusters. The new lower capacity configuration makes Amazon Redshift Serverless suitable for both production and development environments, particularly when workloads require minimal compute and memory resources. This entry-level configuration supports data warehouses with up to 32 TB of Redshift managed storage, offering a maximum of 100 columns per table and 64 GB of memory. Amazon Redshift Serverless with 4 RPU Minimum Capacity Option is generally available in the AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Mumbai), Europe (Ireland), Europe (Stockholm). To get started, see the Amazon Redshift Serverless feature page, use cases, user documentation, API Reference, and pricing.

Amazon Redshift Serverless announces the general availability of dual-stack mode that supports Internet Protocol version 6 (IPv6). This enhancement enables you to modernize your network infrastructure and meet the growing demands of internet connectivity. Redshift Serverless supports configuring your Redshift workgroups with both IPv4 and IPv6 addresses (dual-stack) or IPv4-only configurations within your AWS Virtual Private Clouds (VPCs). You can enable IPv6 support when creating new Redshift Serverless workgroups or modify existing workgroups to support IPv6 addressing. With this capability, you can deploy Redshift warehouses in IPv6-enabled VPC subnets and configure network settings to support the expanding address space requirements of your applications. Your applications can now communicate with Redshift warehouses using either IPv4 or IPv6 protocols, ensuring compatibility with both existing and future network architectures. This feature is available in all AWS commercial regions where Redshift Serverless is available. To get started, read the documentation and blog.

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (Osaka) and Asia Pacific (Malaysia) regions. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.

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 announces the availability of All Upfront and Partial Upfront payment options for 1-year and 3-year reserved instances for RG instances. Reserved instances allow customers to benefit from significant savings over on-demand rates. The new payment options join the previously available No Upfront option, giving customers greater flexibility to optimize compute costs based on their financial preferences. All Upfront delivers the maximum discount by paying for the full reservation term at the start, while Partial Upfront splits the cost between an initial payment and lower monthly installments. Amazon Redshift RG reserved instances with All Upfront and Partial Upfront payment options are now available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Paris), Europe (Stockholm), Europe (Milan), Europe (Spain), Africa (Cape Town), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Mumbai), Asia Pacific (Jakarta), Asia Pacific (Hong Kong), Asia Pacific (Osaka), Asia Pacific (Malaysia), Asia Pacific (Hyderabad), Asia Pacific (Taiwan), Asia Pacific (Melbourne), Asia Pacific (Bangkok), and Mexico (Central). For pricing details, visit the Amazon Redshift pricing page.

https://aws.amazon.com/redshift/ now allows you to get started with Amazon Redshift Serverless with a lower data warehouse https://docs.aws.amazon.com/redshift/latest/mgmt/serverless-capacity.html of 4 Redshift Processing Units (RPUs) in the Asia Pacific (Hong Kong), Asia Pacific (Seoul), Canada (Central), Europe (London), South America (Sao Paulo), AWS GovCloud (US-East), and AWS GovCloud (US-West) regions. Amazon Redshift Serverless measures data warehouse capacity in RPUs. 1 RPU provides you 16 GB of memory. You pay only for the duration of workloads you run in RPU-hours on a per-second basis. Previously, the minimum base capacity required to run Amazon Redshift Serverless was 8 RPUs. You can start using Amazon Redshift Serverless for as low as $1.50 per hour and pay only for the compute capacity your data warehouse consumes when it is active. For predictable workloads, Amazon Redshift Serverless https://docs.aws.amazon.com/redshift/latest/mgmt/serverless-billing-reserved.html#serverless-billing-reserved-works with 1-year and 3-year terms provide additional price-performance benefits. Amazon Redshift Serverless enables users to run and scale analytics without managing data warehouse clusters. The new lower capacity configuration makes Amazon Redshift Serverless suitable for both production and development environments, particularly when workloads require minimal compute and memory resources. This entry-level configuration supports data warehouses with up to 32 TB of Redshift managed storage, offering a maximum of 100 columns per table and 64 GB of memory. To get started, see the Amazon Redshift Serverless https://aws.amazon.com/redshift/redshift-serverless/, https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-serverless.html, and https://docs.aws.amazon.com/redshift-serverless/latest/APIReference/Welcome.html.

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Amazon Redshift Serverless announces the general availability of dual-stack mode that supports Internet Protocol version 6 (IPv6). This enhancement enables you to modernize your network infrastructure and meet the growing demands of internet connectivity. Redshift Serverless supports configuring your Redshift workgroups with both IPv4 and IPv6 addresses (dual-stack) or IPv4-only configurations within your AWS Virtual Private Clouds (VPCs). You can enable IPv6 support when creating new Redshift Serverless workgroups or modify existing workgroups to support IPv6 addressing. With this capability, you can deploy Redshift warehouses in IPv6-enabled VPC subnets and configure network settings to support the expanding address space requirements of your applications. Your applications can now communicate with Redshift warehouses using either IPv4 or IPv6 protocols, ensuring compatibility with both existing and future network architectures. This feature is available in all https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Redshift Serverless is available. To get started, read the https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-serverless.html and https://aws.amazon.com/blogs/big-data/ipv6-addressing-with-amazon-redshift/.