Learn how AWS Emulator is transforming serverless application testing by enabling fast, secure, and AI-friendly AWS integration testing without requiring a cloud account.

Amazon Redshift announces automated cross-region snapshot copy for provisioned clusters and serverless workgroups, enabling hands-off backup to destination regions without manual intervention.

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/.

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.

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.

Learn how Amazon Redshift helps organizations analyze large datasets using SQL, build analytical dashboards, and make data-driven decisions. Explore Redshift architecture, data loading, Redshift Serverless, security, cost optimization, and essential AWS certification concepts.

Ask an AI agent to write Amazon Redshift SQL and it often writes PostgreSQL: string_agg, CREATE INDEX, SERIAL, stl_query on Serverless. The new Redshift skills for the Agent Toolkit for AWS start with one line: Redshift is NOT PostgreSQL. Here are the 10 traps they catch, with working SQL for each, a 5-minute setup for Claude Code and Kiro, the region detail that trips people up, and why IAM, not the skill, is your real guardrail.

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 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 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.

Learn how Amazon Bedrock AgentCore agents in one account can generate answers from an Amazon Bedrock knowledge base backed by Amazon Redshift Serverless in another account, without copying source data. This post covers the architecture, security boundary, and two orchestration models: a code-based Strands agent and a declarative AgentCore harness.

Need to cache frequently accessed data in memory? That is ElastiCache Need a fully managed NoSQL database that scales instantly? That is DynamoDB Need to warehouse and analyze massive datasets? That is Redshift Need to move and transform data between those services automatically? That is AWS Glue

Learn how United Airlines uses AWS Glue Data Catalog federation to query Databricks Unity Catalog data directly from Amazon Redshift Serverless without duplicating data, using resource links and AWS Lake Formation for governance.

Build a real-world AWS Data Analytics Pipeline using Amazon S3, AWS Glue, Amazon Redshift, and Amazon QuickSight. Learn how to ingest raw CSV sales data, transform it with Glue, store it in Redshift, and create an interactive dashboard to analyze total sales, categories, regions, and trends.

Learn how Moovit modernized its data platform with a multi-engine lakehouse architecture: offloading heavy aggregation workloads from Amazon Redshift to Amazon EMR with Spark SQL, isolating workloads with Amazon Redshift Serverless, and cutting overall data pipeline cost by 33%.

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 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 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. 

Learn how Amazon Bedrock AgentCore agents in one account can generate answers from an Amazon Bedrock knowledge base backed by Amazon Redshift Serverless in another account, without copying source data. This post covers the architecture, security boundary, and two orchestration models: a code-based Strands agent and a declarative AgentCore harness.

Learn how Amazon Bedrock AgentCore agents in one account can generate answers from an Amazon Bedrock knowledge base backed by Amazon Redshift Serverless in another account, without copying source data. This post covers the architecture, security boundary, and two orchestration models: a code-based Strands agent and a declarative AgentCore harness.

Amazon Redshift now supports long-term retention for system table data through native integration with Amazon S3 Tables. 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. Previously, extending retention required building and maintaining custom extract transform load (ETL) pipelines to copy system table data, adding development effort and ongoing operational overhead. Customers operating multiple data warehouses faced additional complexity, relying on Redshift data sharing to consolidate system table data from each warehouse into a central location for cross-warehouse analysis. With this feature, system table data is replicated automatically, eliminating the need for custom 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, 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 AWS Regions: US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Taipei), 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 (Thailand), Canada (Central), Europe (Frankfurt), Europe (Zurich), Europe (Stockholm), Europe (Milan), Europe (Spain), Europe (Ireland), Europe (London), Europe (Paris), Israel (Tel Aviv), and South America (Sao Paulo). To learn more, visit our documentation or read the blog.