Category: Amazon Redshift

Amazon Redshift has enhanced its Redshift ML feature to support integration of large language models (LLMs). As part of these enhancements, Redshift now enables native integration with Amazon Bedrock. This integration enables you to use LLMs from simple SQL commands alongside your data in Amazon Redshift, helping you to build generative AI applications quickly. This powerful combination enables customers to harness the transformative capabilities of LLMs and seamlessly incorporate them into their analytical workflows.

This is the final part of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to process data with Amazon Redshift Spectrum and create the gold (consumption) layer.

Amazon Redshift now offers enhanced query performance with optimizations such as Enhanced Histograms for Selectivity Estimation in the absence of fresh statistics by relying on metadata statistics gathered during ingestion. In this post, we cover new performance optimizations in Redshift data warehouse query processing and how elastic histogram statistics help enhance selectivity estimation and the overall quality of query plans for Amazon Redshift data warehouse queries in the absence of fresh table statistics.

This post is co-written with Matt Vogt from Immuta.  Organizations are looking for products that let them spend less time managing data and more time on core business functions. Data security is one of the key functions in managing a data warehouse. With Immuta integration with Amazon Redshift, user and data security operations are managed […]

Amazon Redshift has introduced a new feature called the Query profiler. The Query profiler is a graphical tool that helps users analyze the components and performance of a query. This feature is part of the Amazon Redshift console and provides a visual and graphical representation of the query’s run order, execution plan, and various statistics. The Query profiler makes it easier for users to understand and troubleshoot their queries. In this post, we cover two common use cases for troubleshooting query performance. We show you step-by-step how to analyze and troubleshoot long-running queries using the Query profiler.

In this post, we explain how ultrafast delivery pioneer, Getir, unleashed the power of data democratization on a large scale through their data mesh architecture using Amazon Redshift. We start by introducing Getir and their vision—to seamlessly, securely, and efficiently share business data across different teams within the organization for BI, extract, transform, and load (ETL), and other use cases. We’ll then explore how Amazon Redshift data sharing powered the data mesh architecture that allowed Getir to achieve this transformative vision.

We’re excited to announce the general availability (GA) of Amazon DynamoDB zero-ETL integration with Amazon Redshift, which enables you to run high-performance analytics on your DynamoDB data in Amazon Redshift with little to no impact on production workloads running on DynamoDB. As data is written into a DynamoDB table, it’s seamlessly made available in Amazon Redshift, eliminating the need to build and maintain complex data pipelines.

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.