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…
Discover, govern, and collaborate on data and AI securely with Amazon SageMaker Data and AI Governance
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. This announcement includes Amazon SageMaker Data and AI Governance, a set of capabilities that streamline the management of data and AI assets. Data teams often face challenges when trying to locate, access, and collaborate on data and AI models across their organizations. The process of discovering relevant assets, understanding their context, and obtaining proper access can be time-consuming and complex, potentially hindering productivity and innovation. SageMaker Data and AI Governance offers a comprehensive set of features by providing a unified experience for cataloging, discovering, and governing data and AI assets. It’s centered…
Announcing the general availability of data lineage in the next generation of Amazon SageMaker and Amazon DataZone
Today, I’m happy to announce the general availability of data lineage in Amazon DataZone, following its preview release in June 2024. This feature is also extended as part of the catalog capabilities in the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. Traditionally, business analysts have relied on manual documentation or personal connections to validate data origins, leading to inconsistent and time-consuming processes. Data engineers have struggled to evaluate the impact of changes to data assets, especially as self-service analytics adoption increases. Additionally, data governance teams have faced difficulties in enforcing practices and responding to auditor queries about data movement. Data lineage in Amazon DataZone addresses the challenges faced by organizations striving to remain competitive…