AWS Redshift / SageMaker Lakehouse

Natural language to SQL query generation feature in Redshift Query Editor that enables data users to write SQL queries more quickly and easily using generative AI.

First-party excerpt: 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.

Natural language interface for writing SQL queries in Redshift Query Editor, leveraging generative AI to help users more quickly and easily author SQL without manual coding.

First-party excerpt: 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.

Natural language SQL query generation powered by generative AI that enables data users to more quickly and easily write SQL queries using natural language.

First-party excerpt: 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.

Graviton-based instances that run data warehouse and data lake workloads up to 2.4x faster than RA3 instances at 30% lower cost per vCPU, with integrated data lake query engine for open formats like Apache Iceberg and Parquet.

First-party excerpt: Amazon Redshift RG instances, powered by AWS Graviton processors, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 - at 30% lower per-vCPU cost.

Serverless data warehouse that automatically scales compute to handle evolving analytic needs without infrastructure provisioning or management, enabling analysis of data in seconds.

First-party excerpt: 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.

Unified, open, secure data lakehouse that integrates Amazon Redshift data warehouses, Amazon S3 data lakes, and third-party federated data sources, enabling SQL analytics across unified data with high-performance query capabilities.

First-party excerpt: Unify access across Amazon Redshift data warehouses, Amazon S3 data lakes, and third-party and federated data sources with the lakehouse in Amazon SageMaker

Native read and write access for Apache Iceberg v3 tables in Redshift data lakes, with support for default column values, row lineage, and deletion vectors for efficient schema evolution and high-frequency updates.

First-party excerpt: Amazon Redshift now provides read and write access for Apache Iceberg v3 tables in your data lake, with support for default column values, row lineage, and deletion vectors.

Read and write access for Apache Iceberg v3 tables in data lakes, with support for default column values, row lineage, and deletion vectors to enable schema evolution and CDC pipelines.

First-party excerpt: Amazon Redshift now provides read and write access for Apache Iceberg v3 tables in your data lake, with support for default column values, row lineage, and deletion vectors.

Seamless integration with Amazon Bedrock to invoke large language models for advanced natural language processing tasks like text summarization, entity extraction, and sentiment analysis directly within SQL queries on warehouse data.

First-party excerpt: 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.

Accelerates low-latency SQL query response times by up to 7x from the first run using enhanced code generation and query-specific compilation for analytics applications, BI dashboards, and agentic AI workloads.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Query code generation optimization that accelerates response times for low-latency SQL queries from first run, producing highly optimized query-specific compiled code for real-time analytics, BI dashboards, and agentic AI workloads.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Optimized query compilation that accelerates response times of low-latency SQL queries up to 7x faster from the first run, designed for near real-time analytics and autonomous AI agents.

First-party excerpt: Enhanced code generation produces highly optimized, query-specific compiled code that ensures queries start fast and stay fast.

Accelerates query response times of low-latency SQL queries by up to 7x through enhanced code generation that produces highly optimized, query-specific compiled code for near real-time analytics and BI dashboards.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Query optimization capability using enhanced code generation to produce highly optimized, query-specific compiled code for low-latency SQL queries, achieving up to 7x faster response times on first execution.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents. Enhanced code generation produces highly optimized, query-specific compiled code that ensures queries start fast and stay fast.

Amazon Redshift accelerates query response times of low-latency SQL queries from the first run, with enhanced code generation producing highly optimized, query-specific compiled code for BI dashboards, ETL pipelines, and AI agents.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Capability to build, train, and deploy machine learning models directly in Redshift using SQL for use cases including predictive analytics, classification, and regression on large datasets.

First-party excerpt: 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.

Configure Redshift system table data retention beyond the 7-day default by automatically writing system table data to Amazon S3 Tables in Apache Iceberg format with managed partitioning and compaction.

First-party excerpt: You can now configure your Redshift system table data retention beyond the current 7-day limit to meet your compliance, auditing, and observability requirements.

Capability to use Redshift as a structured knowledge base for Amazon Bedrock to provide more accurate generative AI output and enable advanced natural language processing tasks like text summarization, entity extraction, and sentiment analysis.

First-party excerpt: Use Redshift as your structured knowledge base in Amazon Bedrock for more accurate generative AI output.

Unified data lakehouse providing SQL analytics across Redshift data warehouses, S3 data lakes, and third-party federated data sources within SageMaker for querying, analysis, and ML model building.

First-party excerpt: 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 the lakehouse in Amazon SageMaker

Seamless integration enabling SQL analytics on unified data across a lakehouse architecture in Amazon SageMaker, allowing queries on open formats stored in S3 without moving data between data lakes and data warehouse.

First-party excerpt: Leverage Redshift's powerful SQL analytic capabilities across all of your unified data through its seamless integration in Amazon SageMaker. 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.

Enhanced code generation that optimizes SQL queries to run up to 7x faster from the first execution, supporting near real-time analytics, BI dashboards, ETL pipelines, and agentic AI workloads.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

Enhanced code generation feature that accelerates query response times of low-latency SQL queries from the first run by producing highly optimized, query-specific compiled code for near real-time analytics, BI dashboards, ETL pipelines, and autonomous AI agents.

First-party excerpt: Amazon Redshift accelerates query response times of low-latency SQL queries from the very first run, such as those used in near real-time analytics applications, BI dashboards, ETL pipelines, and autonomous, goal-seeking AI agents.

AWS Graviton-powered data warehouse instances delivering 2.4x faster performance than previous RA3 generation at 30% lower per-vCPU cost, with built-in data lake query engine for Apache Iceberg and Parquet formats.

First-party excerpt: Amazon Redshift RG instances, powered by AWS Graviton processors, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 - at 30% lower per-vCPU cost.

AWS Graviton-powered compute instances for Redshift that deliver up to 2.4x faster performance on data warehouse and data lake workloads compared to RA3, with 30% lower per-vCPU cost and built-in data lake query engine supporting Apache Iceberg and Parquet.

First-party excerpt: Amazon Redshift RG instances, powered by AWS Graviton processors, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 - at 30% lower per-vCPU cost. With a built-in data lake query engine, Redshift RG instances natively process open formats — including Apache Iceberg and Apache Parquet.

Extension of Redshift system table data retention beyond 7 days by automatically writing system tables to Amazon S3 in Apache Iceberg format with managed partitioning and compaction for compliance and auditing.

First-party excerpt: You can now configure your Redshift system table data retention beyond the current 7-day limit to meet your compliance, auditing, and observability requirements.

Serverless analytics platform that automatically scales compute to handle evolving analytic needs without requiring infrastructure provisioning or management, with workload learning capabilities.

First-party excerpt: 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.

Integration enabling invocation of large language models from Amazon Bedrock through Redshift for advanced NLP tasks like text summarization, entity extraction, and sentiment analysis on data in the warehouse.

First-party excerpt: 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.

Native integrations that seamlessly move transactional data from databases like Amazon Aurora, RDS, DynamoDB into Redshift, and ingest high-volume real-time data from Kinesis and MSK without complex data pipelines.

First-party excerpt: 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

Integration enabling Redshift to invoke large language models from Amazon Bedrock for NLP tasks like text summarization, entity extraction, and sentiment analysis directly on warehouse data via SQL.

First-party excerpt: 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.

Unified, open, and secure data lakehouse in Amazon SageMaker that unifies access across Redshift data warehouses, S3 data lakes, and third-party federated data sources for SQL analytics and ML workflows.

First-party excerpt: Unify access across Amazon Redshift data warehouses, Amazon S3 data lakes, and third-party and federated data sources with the lakehouse in Amazon SageMaker

Unified, open, and secure data lakehouse that unifies access across Amazon Redshift data warehouses, Amazon S3 data lakes, and third-party/federated data sources, enabling SQL analytics across unified data in Amazon SageMaker.

First-party excerpt: Unify access across Amazon Redshift data warehouses, Amazon S3 data lakes, and third-party and federated data sources with the lakehouse in Amazon SageMaker

Unified environment within Amazon SageMaker that enables querying, analyzing, and building ML models across both Redshift data warehouses and data lakes stored in open formats on S3.

First-party excerpt: Power SQL analytics across your lakehouse in Amazon SageMaker, giving you one environment to query, analyze, and build across your warehouse and data lake.

Native streaming and real-time data integration capabilities connecting Redshift with Amazon Aurora, RDS, DynamoDB, Kinesis, and MSK without requiring complex data pipelines, enabling near real-time analytics.

First-party excerpt: 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.

Integration with Amazon Bedrock LLMs for advanced natural language processing tasks like text summarization, entity extraction, and sentiment analysis directly within SQL queries on organizational data.

Accelerates query response times for low-latency SQL queries with enhanced code generation producing highly optimized, query-specific compiled code for analytics, BI dashboards, ETL, and AI agent workloads.

Integration enabling Redshift data to serve as a structured knowledge base in Amazon Bedrock for more accurate generative AI output and advanced NLP tasks like text summarization, entity extraction, and sentiment analysis via SQL.

Integration enabling organizations to build personalized applications using their organizational data in Redshift with LLMs from Amazon Bedrock for advanced NLP tasks and generative AI capabilities.

Seamless integration enabling invocation of large language models from Amazon Bedrock and SageMaker for advanced NLP tasks like text summarization, entity extraction, and sentiment analysis on data within Redshift.

Integrated development environment for building and scaling generative AI applications with pre-built models and APIs.

Generative AI assistant for software development that helps discover data, build and train ML models, generate SQL queries, and create data pipeline jobs through natural language.

Generative AI assistant for software development that helps discover data, build and train ML models, generate SQL queries, and create data pipeline jobs through natural language.

Enterprise data warehouse providing SQL analytics capabilities for gaining insights from structured data with price-optimized query performance.

Unified, open, secure data lakehouse that provides SQL analytics across Redshift data warehouses, S3 data lakes, and third-party federated data sources, enabling high-performance queries on open formats without data movement.

Integration of Redshift as a structured knowledge base for Amazon Bedrock generative AI models, enabling more accurate and contextual AI output using organizational data.

Integration enabling Redshift data to serve as a contextualized knowledge base for Amazon Bedrock LLMs, improving accuracy of generative AI outputs for enterprise applications.

Capability to build, train, and deploy machine learning models directly in Redshift using SQL for predictive analytics, classification, and regression tasks.

Enhanced code generation for Amazon Redshift that accelerates low-latency SQL query response times by up to 7x on first run, optimized for near real-time analytics, BI dashboards, ETL pipelines, and autonomous AI agents.

Unified lakehouse platform integrating Redshift with SageMaker, enabling SQL analytics across structured warehouse data and open data lake formats for analytics and ML workloads.

Comprehensive AI development platform for building, training, and deploying ML and foundation models with fully managed infrastructure, tools, and workflows across the entire AI lifecycle.

Data and AI governance platform for securely discovering, governing, and collaborating on data and AI artifacts, built on Amazon DataZone.

Data preparation and integration capabilities for analyzing, preparing, and processing data for analytics and AI using open source frameworks on Athena, EMR, and Glue.

High-performance distributed training infrastructure for accelerating large-scale ML model training.

Pre-built ML solutions and pre-trained models to accelerate model development and deployment.

Unified, open, and secure data lakehouse that provides seamless SQL analytics access across Redshift data warehouses, S3 data lakes, and third-party federated data sources.

Unified, open data lakehouse in Amazon SageMaker that integrates Redshift data warehouses with S3 data lakes and third-party data sources, enabling SQL analytics across consolidated data without moving or duplicating data.

Operational tools and workflows for managing, monitoring, and scaling ML models in production.

Integrated development environment providing unified access to data, analytics, and AI tools in a single serverless notebook with built-in AI agent.