AWS Big Data Blog
Category: Amazon Redshift
Materialize once, query anywhere: Introducing Iceberg materialized views in Amazon Redshift
Amazon Redshift now supports Iceberg materialized views. Compute an aggregation once in Amazon Redshift and store the result as a standard Apache Iceberg table in Amazon S3, queryable by Amazon Athena, Apache Spark, Amazon SageMaker, and AWS Glue. This post covers use cases, incremental refresh, and a step-by-step getting-started guide.
Run an automated operational review with the Amazon Redshift MCP server
Amazon Redshift automates much of its own tuning, but periodic operational reviews still pay off. Learn how the review_cluster tool in the open-source Amazon Redshift MCP server runs a full cluster diagnostic from one natural-language request, evaluating 12 diagnostic areas and returning prioritized, documentation-linked recommendations in minutes.
Getting started with Apache Iceberg write support in Amazon Redshift – Part 3
Amazon Redshift now supports evolving Apache Iceberg table schemas and partition layouts through ALTER statements, with no data rewrites or pipeline rebuilds. In this final post of the series, you rename, add, drop, and widen columns, evolve partitions, and create AWS Lake Formation resource links for governed cross-engine access to Amazon S3 Tables.
How United Airlines uses Amazon Redshift and AWS Glue Data Catalog federation to query Databricks-managed data
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.
Every team is a data team — bring Amazon Redshift analytics to ChatGPT Work
AWS is announcing the AWS Data Analytics plugin for the new Data agent in ChatGPT Work. Teams can ask questions in natural language, analyze governed data across their Amazon Redshift data warehouse and data lakes, and build shareable dashboards, all from a conversation in ChatGPT Work.
How Moovit achieved 33% cost optimization through architectural modernization
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%.
Integrate Amazon Redshift and IAM Identity Center with enhanced VPC routing
Amazon Redshift now supports AWS IAM Identity Center authentication on clusters and workgroups that use enhanced VPC routing. Create two interface VPC endpoints to give your users single sign-on with their corporate credentials while keeping all authentication traffic on the AWS private network.
Razor Group’s journey to a modern data lakehouse on AWS
Razor Group, one of Europe’s leading ecommerce aggregators managing 250+ brands, migrated from always-on Amazon Redshift clusters to an open lakehouse on Apache Iceberg, Amazon S3 Tables, and Apache Spark. Learn the architectural decisions, the five-phase migration, and the results: 65% faster P95 queries and a 63% infrastructure cost reduction.
Long-term system tables retention in Amazon Redshift with Amazon S3 Tables
Amazon Redshift system table integration with Amazon S3 Tables automatically delivers your system table logs to Amazon S3 Tables in Apache Iceberg format. You can retain this data well beyond the 7-day limit for compliance, auditing, and cross-warehouse observability, without custom ETL pipelines or cluster resource consumption.
Amazon Redshift multi-Region disaster recovery
In this post, we walk through the core concepts of cross-Region disaster recovery, introduce a framework for assessing your requirements, and then dive deep into three primary DR strategies for Amazon Redshift: Active-Passive, Active-Active, and a Hybrid approach. For each strategy, we cover architecture, trade-offs, implementation guidance, and cost considerations so you can make an informed decision for your workload.









