Deliver unmatched price-performance at scale with SQL for your data lakehouse

Why Amazon Redshift?

Amazon Redshift powers modern data analytics at scale, delivering up to 3x better price-performance and 7x better throughput than other cloud data warehouses. Redshift Serverless helps you scale analytics workloads effortlessly without managing data warehouse infrastructure. Zero-ETL integrations enable near real-time analytics by easily connecting data from streaming services, operational databases, and third-party enterprise applications without the need for complex data pipelines. Amazon Q in Redshift boosts productivity, simplifying SQL authoring through natural language. Get more accurate output from your generative AI applications by using Redshift as your structured knowledge base in Amazon Bedrock. Redshift seamlessly integrates with the next generation of Amazon SageMaker, allowing you to leverage its powerful SQL analytics capabilities on unified data across Amazon SageMaker Lakehouse.

Powering the next generation of Amazon SageMaker

Unified, open, and secure data lakehouse

Unify access across Amazon Redshift data warehouses, Amazon S3 data lakes, and third-party and federated data sources with Amazon SageMaker Lakehouse

SQL analytics in Amazon SageMaker

Gain insights on unified data using Amazon Redshift, the most price-performant SQL engine

Benefits

Achieve exceptional price-performance, scalability, and security

Gain up to 3x better price-performance and 7x better throughput than other cloud data warehouses as you scale your data analytic workloads in Redshift. Reduce costs and meet business critical SLAs by isolating workloads with scalable multi-data warehouse architectures across your organization. With comprehensive security features like network isolation, fine grained access controls such as row level and column level permissions you can protect your data at no additional cost.

Unlock insights with SQL across unified data in the lakehouse

Leverage Redshift's powerful SQL analytic capabilities across all of your unified data through its seamless integration in Amazon SageMaker Lakehouse. Query your data in open formats stored on Amazon S3 with high performance, eliminating the need to move or duplicate data between your data lakes and data warehouse. Effortlessly include your Redshift data as part of the SageMaker Lakehouse, opening it up for access by a broad range of AWS and Apache Iceberg-compatible analytics engines and machine learning tools.

Accelerate decision making with near real time analytics

Innovate faster by making petabytes of data available for analytics without having to build and manage complex pipelines, enabling near real-time access for analytics use cases. Leverage zero-ETL integrations to seamlessly move transactional data from databases like Amazon Aurora, RDS, and DynamoDB into Redshift without performance impact. Ingest high volume real-time data from Amazon Kinesis and Amazon MSK with native streaming services integrations. With all your data in one place, enable near real-time analytics, and build predictive machine learning models directly in Redshift for powerful business insights.

Get easy SQL analytics without managing infrastructure

Start analyzing your data in a few seconds with Amazon Redshift Serverless. Redshift Serverless learns from your workloads and automatically scales compute to handle your evolving analytic needs, so you can focus on uncovering insights without managing infrastructure. Simply connect to your data sources and start analyzing your data, with no infrastructure set up or maintenance required.

Contextualize applications and boost user productivity with generative AI

Build personalized applications with petabytes of your organizational data through Redshift’s seamless integration with Amazon Bedrock. Boost productivity by enabling data users to more quickly and easily write SQL queries using natural language with Amazon Q generative SQL in Redshift Query Editor. Invoke large language models from Amazon Bedrock and SageMaker for advanced natural language processing tasks like text summarization, entity extraction, and sentiment analysis, to gain deeper insights with your data using SQL.