Category: Amazon Redshift

Amazon Redshift now enables the secure sharing of data lake tables—also known as external tables or Amazon Redshift Spectrum tables—that are managed in the AWS Glue Data Catalog, as well as Redshift views referencing those data lake tables. By using granular access controls, data sharing in Amazon Redshift helps data owners maintain tight governance over who can access the shared information. In this post, we explore powerful use cases that demonstrate how you can enhance cross-team and cross-organizational collaboration, reduce overhead, and unlock new insights by using this innovative data sharing functionality.

Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. In this post, we highlight the performance improvements we observed using industry standard TPC-DS benchmarks. Overall execution time of TPC-DS 3 TB benchmark improved by 3x. Some of the queries in our benchmark experienced up to 12x speed up.

Salesforce and Amazon have collaborated to help customers unlock value from unified data and accelerate time to insights with bidirectional Zero Copy data sharing between Salesforce Data Cloud and Amazon Redshift. In the Part 1 of this series, we discussed how to configure data sharing between Salesforce Data Cloud and customers’ AWS accounts in the same AWS Region. In this post, we discuss the architecture and implementation details of cross-Region data sharing between Salesforce Data Cloud and customers’ AWS accounts.

This post describes how HPE Aruba automated their Supply Chain management pipeline, and re-architected and deployed their data solution by adopting a modern data architecture on AWS.

Amazon Redshift, a warehousing service, offers a variety of options for ingesting data from diverse sources into its high-performance, scalable environment. Whether your data resides in operational databases, data lakes, on-premises systems, Amazon Elastic Compute Cloud (Amazon EC2), or other AWS services, Amazon Redshift provides multiple ingestion methods to meet your specific needs. The currently […]

In this post, we demonstrate how to use the AWS CDK and DSF to create a multi-data warehouse platform based on Amazon Redshift Serverless. DSF simplifies the provisioning of Redshift Serverless, initialization and cataloging of data, and data sharing between different data warehouse deployments.

This blog post provides a step-by-step guide to integrating IAM Identity Center with Microsoft Entra ID as the IdP and configuring Amazon Redshift as an AWS managed application. Additionally, you’ll learn how to set up the Amazon Redshift driver in Tableau, enabling SSO directly within Tableau Desktop.

In a previous post, we showed how Zero Copy data federation empowers businesses to access Amazon Redshift data within the Salesforce Data Cloud to enrich customer 360 data with operational data. This two-part series explores how analytics teams can access customer 360 data from Salesforce Data Cloud within Amazon Redshift to generate insights on unified data without the overhead of extract, transform, and load (ETL) pipelines. In this post, we cover data sharing between Salesforce Data Cloud and customers’ AWS accounts in the same AWS Region. Part 2 covers cross-Region data sharing between Salesforce Data Cloud and customers’ AWS accounts.

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. However, you might need to automatically scale compute resources based on factors like query complexity and data volume to meet price-performance targets, irrespective of query queuing. […]

Unlocking the true value of data often gets impeded by siloed information. Traditional data management—wherein each business unit ingests raw data in separate data lakes or warehouses—hinders visibility and cross-functional analysis. A data mesh framework empowers business units with data ownership and facilitates seamless sharing. However, integrating datasets from different business units can present several […]