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Today, I’m thrilled to announce that Amazon Redshift, a widely used, fully managed, petabyte-scale data warehouse, is taking a significant step forward in strengthening the default security posture of...

Learn about the urgent need to address the trio of SQL injection vulnerabilities (CVE-2024-12744, CVE-2024-12745, and CVE-2024-12746) in Amazon Redshift drivers.

Amazon has announced key security enhancements for Redshift, a popular data warehousing solution, to help prevent data exposures due to misconfigurations and insecure default settings.

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 has announced key security enhancements for Redshift, a popular data warehousing solution, to help prevent data exposures due to misconfigurations and insecure default settings.

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.

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

In this post, we will show you five Amazon Redshift architecture patterns that you can consider to optimize your Amazon Redshift data warehouse performance at scale using features such as Amazon Redshift Serverless, Amazon Redshift data sharing, Amazon Redshift Spectrum, zero-ETL integrations, and Amazon Redshift streaming ingestion.

Three security vulnerabilities warranting a patch immediately warning have been confirmed for users of the Amazon Redshift cloud platform—what you need to know.

RDS, Aurora, DynamoDB, Neptune, DocumentDB, MemoryDB… AWS has many database options. This guide helps you understand when each one makes sense based on your workload.

Describes the rules for working with database nulls supported by Amazon Redshift.

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.

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.

Memiliki kemampuan untuk beroperasi dengan database cloud, termasuk Redshift, Amazon ElastiCache, Microsoft Azure, MongoDB Atlas, Amazon RDS, dan Amazon

Unlock S3 data insights effortlessly with AWS' rich metadata capture; query objects by key, size, tags, and more using Athena, Redshift, and Spark at scale.

Rehearsing for humanity’s future on Mars

Skroutz chose Amazon Redshift to promote data democratization, empowering teams across the organization with seamless access to data, enabling faster insights and more informed decision-making. In this post, we share how we handled real-time schema evolution in Amazon Redshift with Debezium.

Discover more about what's new at AWS with Announcing Amazon S3 Tables – Fully managed Apache Iceberg tables optimized for analytics workloads