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6 days ago

AWS Weekly Roundup: NVIDIA Nemotron 3 Super on Amazon Bedrock, Nova Forge SDK, Amazon Corretto 26, and more (March 23, 2026)

Hello! I’m Daniel Abib,...

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1 week ago

Amazon Redshift supports federated permissions with IAM Identity Center in multiple AWS Regions Amazon Redshift federated permissions are now supported with AWS IAM Identity Center (IdC) in multiple AWS Regions. You can extend IdC from your primary AWS Region to additional Regions for improved performance through proximity to users and reliability. In the additional regions, you now have simplified administration of Redshift fine-grained access controls at the table and column level using existing workforce identities with IdC. When a new Region is added in IdC, you can create Redshift and Lake Formation Identity Center applications in the new Region without replicating identities from the primary Region. This enables you to use existing workforce identities to query data across warehouses in the new Region. 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 also access Amazon Redshift with single sign-on in these new Regions from Amazon QuickSight, Amazon Redshift Query Editor, or third-party SQL tools. To get started with Redshift federated permissions using IdC, read the blog and documentation. To extend IdC support in multiple regions, read IdC documentation, Redshift documentation, Lake Formation documentation, and see the region availability.

🆕 Amazon Redshift now supports federated permissions with IAM Identity Center in multiple regions, allowing simplified access control management using existing workforce identities, single sign-on via QuickSight and SQL tools, and enhanced performance and reliability.

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1 week ago

Amazon Redshift supports federated permissions with IAM Identity Center in multiple AWS Regions

Amazon Redshift federated permissions are now supported with AWS IAM Identity Center (IdC) in multiple AWS Regions. You can extend IdC from your primary AWS Region to additiona...

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1 week ago

Amazon Redshift increases performance for new queries in dashboards and ETL workloads by up to 7x Amazon Redshift improves the performance of BI dashboards and ETL workloads by speeding up new queries by up to 7x. This significantly improves the response times of low-latency SQL queries, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents. Customers experience substantially faster query response times as Redshift accelerates the process of preparing the SQL query for execution. Queries start faster and return results quicker. This improvement is automatically enabled at no additional cost. To deliver this major improvement, Redshift added a new optimization to query compilation where new queries are processed immediately using composition. Composition is a technique that generates a lightweight arrangement of pre-existing logic while simultaneously creating highly optimized, query-specific code that is compiled and executed across available compute resources to further boost performance. Composition removes compilation from the critical path of query execution and provides immediate execution while compilation proceeds in the background. With this optimization, new queries processed by Redshift start faster and deliver performance consistent with subsequent runs. This optimization is enabled by default for any SQL query across all provisioned clusters and serverless workgroups, in all commercial AWS Regions where Amazon Redshift operates. It is available on the Redshift current track with other tracks following in upcoming patch releases. No action is required from customers to benefit from this enhancement, and it is free of charge.

🆕 Amazon Redshift enhances BI dashboard and ETL query performance by up to 7x with low-latency SQL. This free, default optimization speeds up query start times and results, boosting real-time analytics and ETL response times.

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1 week ago

Amazon Redshift increases performance for new queries in dashboards and ETL workloads by up to 7x

https://aws.amazon.com/redshift improves the performance of BI dashboards and ETL workloads by speeding up new queries by up to 7x. This significantly improves the response t...

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1 week ago

AWS Weekly Roundup: Amazon S3 turns 20, Amazon Route 53 Global Resolver general availability, and more (March 16, 2026)

Twenty years ago this past w...

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3 weeks ago

Amazon Redshift introduces new array functions for semi-structured data processing Amazon Redshift now supports nine new array functions for working with semi-structured data stored in the SUPER data type. The new functions include ARRAY_CONTAINS, ARRAY_DISTINCT, ARRAY_EXCEPT, ARRAY_INTERSECTION, ARRAY_POSITION, ARRAY_POSITIONS, ARRAY_SORT, ARRAY_UNION, and ARRAYS_OVERLAP, enabling you to search, compare, sort, and transform arrays directly within your SQL queries. Previously, performing these operations required writing complex custom PartiQL SQL logic. These functions simplify complex data transformations and reduce query complexity by enabling sophisticated array operations in a single SQL statement. For example, you can use ARRAY_CONTAINS and ARRAY_POSITION for element lookup, ARRAY_INTERSECTION and ARRAY_EXCEPT for set operations, or ARRAY_SORT and ARRAY_DISTINCT to organize and deduplicate data. These functions are particularly valuable for applications involving nested data structures, event processing, and analytics workflows where data needs to be aggregated, filtered, or transformed at scale. The new Amazon Redshift array functions are available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. To learn more, please visit our documentation.

🆕 Amazon Redshift adds nine new array functions for SUPER data type, simplifying semi-structured data processing with operations like ARRAY_CONTAINS, ARRAY_SORT, and ARRAY_UNION, reducing complex custom SQL logic and enhancing data transformation in SQL queries.

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3 weeks ago

Amazon Redshift introduces reusable templates for COPY operations Amazon Redshift now supports templates for the COPY command, allowing you to store and reuse frequently used COPY parameters. This new feature enables you to create reusable templates that contain commonly utilized formatting parameters, eliminating the need to manually specify parameters for each COPY operation. Templates help maintain consistency across data ingestion operations that use the COPY command. They also reduce the time and effort required to execute COPY commands. You can create standardized configurations for different file types and data sources, ensuring consistent parameter usage across your teams and reducing the likelihood of errors caused by manual input. When parameters need to be updated, changes to the template automatically apply to all future uses, simplifying maintenance and improving operational efficiency. Support for templates for the COPY command is available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. To get started with templates, see the documentation or check out the AWS Blog.

🆕 Amazon Redshift now offers reusable templates for COPY operations, allowing users to store and reuse common parameters, ensuring consistency, reducing manual input, and simplifying maintenance across all AWS Regions.

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3 weeks ago

Amazon Redshift introduces new array functions for semi-structured data processing

https://aws.amazon.com/redshift/ now supports nine new array functions for working with semi-structured data stored in the docs.aws.amazon.com/redshift/latest/dg/super...

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3 weeks ago

Amazon Redshift introduces reusable templates for COPY operations

https://aws.amazon.com/redshift/ now supports templates for the COPY command, allowing you to store and reuse frequently used docs.aws.amazon.com/redshift/latest/dg/r_COP... This new ...

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3 weeks ago

Amazon Redshift Serverless now maintains datashare permissions during restore Amazon Redshift Serverless now preserves datashare permissions when you restore a snapshot to the same namespace, simplifying data sharing workflows and reducing administrative overhead. Previously, restoring a serverless namespace from a snapshot required administrators to manually re-grant datashare permissions to consumer clusters and recreate consumer databases, even when restoring to the same namespace. With this enhancement, datashare permissions are automatically maintained when you restore a snapshot to the same producer namespace, provided the datashare permission existed both when the snapshot was taken and on the current namespace. For consumer namespaces, datashare access remains unchanged after restore, eliminating the need for producer administrators to re-grant permissions. This streamlines disaster recovery and testing workflows by reducing manual configuration steps and potential errors. Amazon Redshift also provides EventBridge notifications to alert you when datashares are dropped, consumer access is revoked, or public accessibility changes during restore operations. This feature is available in all AWS Regions that support Amazon Redshift. To learn more, see the Amazon Redshift Management Guide.

🆕 Amazon Redshift Serverless preserves datashare permissions during restores, simplifying workflows and cutting admin tasks. It maintains permissions when restoring snapshots within the same namespace, eliminating manual re-granting, and is available in all AWS Regions suppor…

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3 weeks ago

Amazon Redshift Serverless now maintains datashare permissions during restore

Amazon Redshift Serverless now preserves datashare permissions when you restore a snapshot to the same namespace, simplifying data sharing workflows and reducing administrative overhead. Previou...

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1 month ago

Amazon Redshift Serverless introduces 3-year Serverless Reservations Amazon Redshift now offers 3-year Serverless Reservations for Amazon Redshift Serverless, a new discounted pricing option that provides up to 45% savings and improved cost predictability for your analytics workloads. With Serverless Reservations, you commit to a specific number of Redshift Processing Units (RPUs) for a 3-year term with a no-upfront payment option. Amazon Redshift Serverless allows you to run and scale analytics without having to provision and manage clusters with a pay-as-you-go pricing model. Serverless Reservations help you further optimize compute costs and improve cost predictability of existing and new workloads on Amazon Redshift Serverless. Managed at the AWS payer account level, Serverless Reservations can be shared between multiple AWS accounts, reducing your compute costs by up to 45% on all Amazon Redshift Serverless workloads in your AWS account. Serverless Reservations are billed hourly and metered per second, offering a consistent billing model (24 hours a day, seven days a week) while maintaining the flexibility offered by Amazon Redshift Serverless. Any usage exceeding the specified RPU level is charged at standard on-demand rates. You can purchase Serverless Reservations via the Amazon Redshift console or by invoking the Serverless Reservations API “create-reservation”. Serverless Reservations are available in all regions where Amazon Redshift Serverless is currently available. To learn more about Amazon Redshift Serverless pricing options, see the Redshift Serverless feature page, Redshift Pricing Page, or the Amazon Redshift Management Guide.

🆕 Amazon Redshift Serverless now offers 3-year Serverless Reservations for up to 45% savings, no upfront, shared across accounts, billed hourly, and available in all regions where Redshift Serverless operates.

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1 month ago

Amazon Redshift Serverless introduces 3-year Serverless Reservations

Amazon Redshift now offers 3-year Serverless Reservations for Amazon Redshift Serverless, a new discounted pricing option that provides up to 45% savings and improved cost predictability for your analyti...

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1 month ago

Amazon Redshift now supports allocating extra compute for automatic optimizations

Amazon Redshift now supports allocating extra compute for automatic optimization features, known as autonomics. Database administrators managing Amazon Redshift workloads can now allocate ad...

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1 month ago

Amazon Redshift now supports allocating extra compute for automatic optimizations Amazon Redshift now supports allocating extra compute for automatic optimization features, known as autonomics. Database administrators managing Amazon Redshift workloads can now allocate additional resources for their clusters to enable autonomics even during periods of high user activity, eliminating the need to manually schedule optimizations such as Automatic Table Optimization (ATO), Automatic Table Sorting (ATS), Auto Vacuum, and Auto Analyze. This enhancement extends Amazon Redshift's autonomics capabilities to automatically leverage extra compute resources, to run reliably without impacting user workloads. It also includes a cost control feature for provisioned clusters, allowing database administrators to limit the amount of resources available to autonomics. Additionally, the new SYS_AUTOMATIC_OPTIMIZATION system table enhances observability by providing detailed information on autonomics operations for both provisioned clusters and serverless workgroups. This feature is available in all AWS Regions where Amazon Redshift is supported. To learn more, see Allocating extra compute resources for automatic database optimization.

🆕 Amazon Redshift now offers extra compute for automatic optimizations, enabling autonomics during high activity without manual scheduling. It includes cost control and observability via the SYS_AUTOMATIC_OPTIMIZATION table. Available in all AWS Regions.

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1 month ago

Amazon Redshift now supports autonomics for multi-cluster environments

Amazon Redshift now supports autonomics—automatic optimization features—for multi-cluster environments. Database administrators managing distributed Amazon Redshift workloads can now benefit from a...

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1 month ago

Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables

New capabilities help optimize appli...

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2 months ago

Zero-ETL for self-managed Database Sources now available in 7 new regions

docs.aws.amazon.com/glue/latest/dg/zero-etl-... now supports zero-ETL for self-managed database sources in seven additional regions. Using Glue z...

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2 months ago

Amazon Redshift ODBC 2.x Driver now supports Apple macOS

https://aws.amazon.com/redshift/ ODBC 2.x driver now supports Apple macOS, expanding platform compatibility for developers and analysts. This enhancement allows Apple macOS users to connect to Amazon Redshift cluste...

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2 months ago

Amazon Redshift Serverless adds queue-based query resource management

Amazon Redshift Serverless introduces queue-based query resource management. You can create dedicated query queues with customized monitoring rules for different workloads. This feature provides granula...

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2 months ago

Amazon Redshift Serverless adds queue-based query resource management Amazon Redshift Serverless introduces queue-based query resource management. You can create dedicated query queues with customized monitoring rules for different workloads. This feature provides granular control over resource usage. Queues let you set metrics-based predicates and automated responses. For example, you can configure rules to automatically abort queries that exceed time limits or consume too many resources. Previously, Query Monitoring Rules (QMR) were applied only at the Redshift Serverless workgroup level, affecting all queries run in this workgroup uniformly. The new queue-based approach lets you create queues with distinct monitoring rules. You can assign these queues to specific user roles and query groups. Each queue operates independently, with rules affecting only the queries within that queue. The available monitoring metrics can be found in Query monitoring metrics for Amazon Redshift Serverless. This feature is available in all AWS regions that support Amazon Redshift Serverless. You can manage QMR with queues through the AWS Console and Redshift APIs. For implementation details, see the documentation in the Amazon Redshift management guide.

🆕 Amazon Redshift Serverless adds queue-based query resource management with dedicated queues and custom monitoring for varied workloads. This offers fine-tuned control over resource use and automated responses for metrics, available in all Redshift Serverless regions.

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2 months ago

Amazon Redshift Serverless is now available in the AWS Asia Pacific (New Zealand) region

https://aws.amazon.com/redshift/redshift-serverless/ which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally avai...

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2 months ago

Amazon Redshift Serverless is now available in the AWS Asia Pacific (New Zealand) region 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 (New Zealand) region. 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, Apache Iceberg 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 Serverless is now live in AWS Asia Pacific (New Zealand). It lets users run analytics without managing clusters. Pay per second, enjoy automatic scaling, and get insights quickly. Start easily via the AWS Management Console.

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2 months ago

Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables

New capabilities help optimize appli...

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3 months ago

Amazon Redshift supports four new materialized view (MV) features on data shares

https://aws.amazon.com/redshift/ now allows you to run create MV and refresh MV commands from multiple Amazon Redshift data warehouses. This update also allows you to create an MV on shared M...

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3 months ago

Amazon Redshift supports four new materialized view (MV) features on data shares Amazon Redshift now allows you to run create MV and refresh MV commands from multiple Amazon Redshift data warehouses. This update also allows you to create an MV on shared MVs. Finally, this release now supports concurrency scaling of the create materialized view (MV) data definition language (DDL) command. With this update, you can now scale the create MV DDL command whenever your main Amazon Redshift data warehouse cluster or workgroup runs out of resources simply by enabling concurrency scaling in your Amazon Redshift account. You can start using these new capabilities immediately in all AWS regions where Amazon Redshift is available to scale your workload and build resilient analytics applications with predictable Service Level Agreements. To get started, refer to the Concurrency Scaling, Materialized Views and Data Sharing sections of the Amazon Redshift documentation.

🆕 Amazon Redshift introduces four new materialized view features for data shares, including create and refresh MV commands across warehouses, shared MV creation, and concurrency scaling for create MV DDL commands. Use these features in all AWS regions to build resilient analy…

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3 months ago

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3 months ago

Zero-ETL for self-managed Database Sources now available in 7 new regions

docs.aws.amazon.com/glue/latest/dg/zero-etl-... now supports zero-ETL for self-managed database sources in seven additional regions. Using Glue z...

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3 months ago

Zero-ETL for self-managed Database Sources now available in 7 new regions AWS Glue now supports zero-ETL for self-managed database sources in seven additional regions. Using Glue zero-ETL, you can setup an integration to replicate data from Oracle, SQL Server, MySQL or PostgreSQL databases which are located on-premises or on AWS EC2 to Redshift with a simple experience that eliminates configuration complexity. AWS zero-ETL for self-managed database sources will automatically create an integration for an on-going replication of data from your on-premises or EC2 databases through a simple, no-code interface. You can now replicate data from Oracle, SQL Server, MySQL and PostgreSQL databases into Redshift. This feature further reduces users' operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines to ingest data from self-managed databases to Redshift. AWS Glue zero-ETL for self-managed database sources are available in the following additional AWS Regions: Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (London), South America (São Paulo), and US (Virginia) regions. To get started, sign into the AWS Management Console. For more information visit the AWS Glue page or review the AWS Glue zero-ETL documentation.

🆕 AWS Glue introduces zero-ETL for self-managed databases in 7 new regions: HK, Tokyo, Singapore, Sydney, London, São Paulo, and Virginia. It eases data replication from Oracle, SQL Server, MySQL, or PostgreSQL to Redshift, cutting down e…

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