Today, we announced the next generation of Amazon SageMaker, which is a unified platform for data, analytics, and AI, bringing together widely-adopted AWS machine learning and analytics capabilities. At its core is SageMaker Unified Studio (preview), a single data and AI development environment for data exploration, preparation and integration, big data processing, fast SQL analytics, model development and training, and generative AI application development. This announcement includes Amazon SageMaker Lakehouse, a capability that unifies data across data lakes and data warehouses, helping you build powerful analytics and artificial intelligence and machine learning (AI/ML) applications on a single copy of data. In addition to these launches, I’m happy to announce data catalog and permissions capabilities in Amazon SageMaker Lakehouse, helping you…
Category: AWS
Reposts from Amazon Web Services (AWS).
Amazon SageMaker Lakehouse and Amazon Redshift supports zero-ETL integrations from applications
Today, we announced the general availability of Amazon SageMaker Lakehouse and Amazon Redshift support for zero-ETL integrations from applications. Amazon SageMaker Lakehouse unifies all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines. Zero-ETL is a set of fully managed integrations by AWS that minimizes the need to build ETL data pipelines for common ingestion and replication use cases. With zero-ETL integrations from applications such as Salesforce, SAP, and Zendesk, you can reduce time spent building data pipelines and…
Simplify analytics and AI/ML with new Amazon SageMaker Lakehouse
Today, I’m very excited to announce the general availability of Amazon SageMaker Lakehouse, a capability that unifies data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and artificial intelligence and machine learning (AI/ML) applications on a single copy of data. SageMaker Lakehouse is a part of the next generation of Amazon SageMaker, which is a unified platform for data, analytics and AI, that brings together widely-adopted AWS machine learning and analytics capabilities and delivers an integrated experience for analytics and AI. Customers want to do more with data. To move faster with their analytics journey, they are picking the right storage and databases to store their data. The data…
New Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse
Amazon DynamoDB, a serverless NoSQL database, has been a go-to solution for over one million customers to build low-latency and high-scale applications. As data grows, organizations are constantly seeking ways to extract valuable insights from operational data, which is often stored in DynamoDB. However, to make the most of this data in Amazon DynamoDB for analytics and machine learning (ML) use cases, customers often build custom data pipelines—a time-consuming infrastructure task that adds little unique value to their core business. Starting today, you can use Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse to run analytics and ML workloads in just a few clicks without consuming your DynamoDB table capacity. Amazon SageMaker Lakehouse unifies all your data across Amazon S3…