Learn about the next generation of Amazon SageMaker

Amazon Redshift

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

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Why Amazon Redshift?

Tens of thousands of customers use Amazon Redshift for modern data analytics at scale, delivering up to 3x better price performance and 7x better throughput than other cloud data warehouses. Amazon Redshift seamlessly integrates with Amazon SageMaker Lakehouse, allowing you to use its powerful SQL analytic capabilities on your unified data across Amazon Redshift data warehouses and Amazon Simple Storage Service (Amazon S3) data lakes. Enable near real-time analytics to accelerate decision-making with Amazon Redshift zero-ETL integrations, which connect data from streaming services, operational databases, and third-party enterprise applications without building complex data pipelines. Amazon Redshift Serverless makes scaling your analytics effortless, allowing you to analyze petabytes of data without the burden of infrastructure management. Boost your team's productivity with Amazon Q in Amazon Redshift, which simplifies SQL authoring through natural language. Maximize the value of your data by using Amazon Redshift as a structured knowledge base for generative AI assistants in Amazon Bedrock, leading to more relevant and accurate outputs for your applications.

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 Amazon 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 and 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

Use the powerful SQL analytic capabilities of Amazon Redshift across all of your unified data with its seamless integration in SageMaker Lakehouse. Query your data in open formats stored on Amazon S3 with high performance, removing the need to move or duplicate data between your data lakes and data warehouse. Effortlessly include your Amazon 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 (ML) 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. Use zero-ETL integrations to seamlessly move transactional data from databases like Amazon Aurora, Amazon Relational Database Service (Amazon RDS), and Amazon DynamoDB into Amazon Redshift without performance impact. Ingest high volume real-time data from Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK) with native streaming services integrations. With all your data in one place, enable near real-time analytics and directly build predictive ML models in Amazon Redshift for powerful business insights.

Get easy SQL analytics without managing infrastructure

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

Contextualize applications and boost user productivity with generative AI

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

How it works

Amazon Redshift uses SQL to analyze structured and semistructured data across data warehouses, operational databases, and data lakes, using hardware and ML designed by AWS to deliver the best price performance at any scale.

Use cases

Improve financial and demand forecasts

Ingests hundreds of megabytes of data per second so you can query data in near real time and build low latency analytics applications for fraud detection, live leaderboards, and IoT.

Optimize your business intelligence

Build insight-driven reports and dashboards using Amazon Redshift and BI tools such as Amazon QuickSight, Tableau, Microsoft PowerBI, or others.

Accelerate ML in SQL

Use SQL to build, train, and deploy ML models for many use cases including predictive analytics, classification, regression and more to support advanced analytics on large amount of data.

Monetize your data

Build applications on top of all your data across databases, data warehouses, and data lakes. Seamlessly and securely share and collaborate on data to create more value for your customers, monetize your data as a service, and unlock new revenue streams.

Easily combine your data with third-party data sets

Whether it's market data, social media analytics, weather data or more, subscribe to and combine third-party data in AWS Data Exchange with your data in Amazon Redshift, without hassling over licensing and onboarding processes and moving the data to the warehouse.