Category: Amazon SageMaker Lakehouse

At AWS re:Invent 2024, we introduced a no code zero-ETL integration between Amazon DynamoDB and Amazon SageMaker Lakehouse, simplifying how organizations handle data analytics and AI workflows. In this post, we share how to set up this zero-ETL integration from DynamoDB to your SageMaker Lakehouse environment.

In this post, we show you how to create Iceberg tables in Amazon SageMaker Unified Studio and stream data to these tables using Firehose. With this integration, data engineers, analysts, and data scientists can seamlessly collaborate and build end-to-end analytics and ML workflows using SageMaker Unified Studio, removing traditional silos and accelerating the journey from data ingestion to production ML models.

With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. This post describes how to integrate data on RMS tables through Apache Spark using SageMaker Unified Studio, Amazon EMR 7.5.0 and higher, and AWS Glue 5.0.

In this post, we introduce authorization policies for custom asset types—a new governance capability in Amazon SageMaker that gives organizations fine-grained control over who can create and manage assets using specific templates. This feature enhances data governance by allowing teams to enforce usage policies that align with business and security requirements across the organization.

In this post, we show you how to share an Amazon Redshift table and Amazon S3 based Iceberg table from the account that owns the data to another account that consumes the data. In the recipient account, we run a join query on the shared data lake and data warehouse tables using Spark in AWS Glue 5.0. We walk you through the complete cross-account setup and provide the Spark configuration in a Python notebook.

This series of posts demonstrates how you can onboard and access existing AWS data sources using SageMaker Unified Studio. This post focuses on onboarding existing AWS Glue Data Catalog tables and database tables available in Amazon Redshift.

In this post we discuss integrating additional vital data sources such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon EMR clusters. We demonstrate how to configure the necessary permissions, establish connections, and effectively use these resources within SageMaker Unified Studio. Whether you’re working with object storage, relational databases, NoSQL databases, or big data processing, this post can help you seamlessly incorporate your existing data infrastructure into your SageMaker Unified Studio workflows.

Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using AWS Identity and Access Management (IAM) principals and session tags to simplify data access, grant creation, and maintenance. In this post, we demonstrate how to get started with SageMaker Lakehouse with ABAC.

Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.

Amazon SageMaker Catalog now supports metadata rules allowing organizations to enforce metadata standards across data publishing and subscription workflows. In this post, we guide you through two workflows: setting up metadata enforcement rules for a specific domain and publishing an asset or data product in a catalog, and setting up metadata enforcement rules for a specific domain and subscribing to an asset or data product that is owned by a project within that domain.