Category: Amazon Redshift

With Amazon Redshift Serverless queue-based Query Monitoring Rules (QMR), administrators can define workload-aware thresholds and automated actions at the queue level—a significant improvement over previous workgroup-level monitoring. You can create dedicated queues for distinct workloads such as BI reporting, ad hoc analysis, or data engineering, then apply queue-specific rules to automatically abort, log, or restrict queries that exceed execution-time or resource-consumption limits. By isolating workloads and enforcing targeted controls, this approach protects mission-critical queries, improves performance predictability, and prevents resource monopolization—all while maintaining the flexibility of a serverless experience. In this post, we discuss how you can implement your workloads with query queues in Redshift Serverless.

re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, […]

Amazon Redshift federated permissions simplify permissions management across multiple Redshift warehouses. In this post, we show you how to define data permissions one time and automatically enforce them across warehouses in your AWS account, removing the need to re-create security policies in each warehouse.

In this post, we discuss the best practices that you can follow while querying Apache Iceberg data with Amazon Redshift

As we witness the gradual transition from IPv4 to IPv6, AWS continues to expand its support for dual-stack networking across its service portfolio. In this post, we show how you can migrate your Amazon Redshift Serverless workgroup from IPv4-only to dual-stack mode, so you can make your data warehouse future ready.

In 2025, Amazon Redshift delivered several performance optimizations that improved query performance over twofold for Iceberg workloads on Amazon Redshift Serverless, delivering exceptional performance and cost-effectiveness for your data lake workloads. In this post, we describe some of the optimizations that led to these performance gains.

In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance.

In this post, you learn how Amazon Redshift Serverless Reservations can help you lower your data warehouse costs. We explore ways to determine the optimal number of RPUs to reserve, review example scenarios, and discuss important considerations when purchasing these reservations.

In this post, we show you the upgrade process from DC2 instances to Amazon Redshift Serverless. By using Amazon Redshift Serverless, you can run and scale analytics without managing data warehouse infrastructure.

As analytical demands grow, many customers are upgrading from DC2 to RA3 or Amazon Redshift Serverless, which offer independent compute and storage scaling, along with advanced capabilities such as data sharing, zero-ETL integration, and built-in artificial intelligence and machine learning (AI/ML) support with Amazon Redshift ML. This post provides a practical guide to plan your target architecture and migration strategy, covering upgrade options, key considerations, and best practices to facilitate a successful and seamless transition.