Category: Database

Today we are announcing that Amazon has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind the open-source analytical database DuckDB. We expect the transaction to close shortly, subject to customary closing conditions. Hannes Mühleisen and Mark Raasveldt, who created DuckDB and co-founded DuckLabs, will continue leading the team and the open-source project’s technical direction as part of AWS. The DuckDB open-source project will also continue to be driven by the DuckLabs team, remain open source under the independent Foundation (the non-profit that oversees DuckDB), and available under the MIT license as it does today.

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.

This post walks through how Autodesk re-architected a single-index Elasticsearch 7.1.1 domain on Amazon OpenSearch Service into four multi-index OpenSearch Service domains, using Migration Assistant for Amazon OpenSearch Service and a routing layer that directs each query to the shards that hold the data for that query.

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.

Upgrading your Amazon Redshift DC2 clusters to AWS Graviton-based RG instances unlocks managed storage, data sharing, zero-ETL, and an integrated data lake engine. This post covers the new features, node-mapping guidance for sizing, the available upgrade methods, and how to validate your target configuration with Amazon Redshift Test Drive.

In this post, we show you how to use Amazon SageMaker Unified Studio to implement cross-account data sharing in Amazon Redshift using data mesh principles. We demonstrate how to build a scalable data mesh architecture that supports secure, auditable data sharing across AWS accounts while reducing operational burden.

In this post, we introduce new capabilities of Amazon Redshift that enhance our multi-warehouse and scaling capabilities: remote materialized view (MV) operations, remote table DDL support, and concurrency scaling enhancements for zero-ETL and S3 event integration. These features help you build more scalable, performant decentralized analytics architectures on Amazon Redshift.

In this post, we provide a guide to help you use Tableau’s Relationships and Amazon Redshift Serverless architecture to deliver sub-second insights while maximizing every Redshift Processing Unit (RPU). We also provide guidance on five key areas: data model architecture for optimal query performance, security configuration and access control, performance optimization through smart configuration, cost management strategies, and query and join optimization techniques.

In this post, you learn how tombola followed a strict engineering principle: no changes to production without evidence. That meant a head-to-head comparison of RA3 versus RG on their actual workload. You also see benchmark results on Amazon S3 Tables and the migration from RA3 to RG instances.

In this post, you learn how to migrate Amazon Redshift RA3 clusters to Graviton-based RG instances. We compare the Elastic Resize, Classic Resize, and Snapshot/Restore migration strategies, with key considerations and best practices to support a smooth migration. We also provide mapping guidance from RA3 to RG to help you right-size your cluster.