Category: Amazon SageMaker Unified Studio

Configuring VPC networking per project across a SageMaker Unified Studio domain creates inconsistent, hard-to-audit networks. This post shows administrators how to configure domain-level VPC networking once, so every new project automatically inherits consistent, private network isolation, then update existing projects and validate connectivity.

In Part 2 of this series, sign in as a data consumer, subscribe to enriched unstructured data assets in Amazon SageMaker Catalog, and query them using natural language through a no-code Amazon Bedrock chat agent and Amazon Bedrock model inference.

Connect Snowflake to Amazon SageMaker Unified Studio to build a unified data catalog. Query federated Snowflake tables without moving data, publish enriched assets to SageMaker Catalog, and validate data quality with AWS Glue Data Quality, all while keeping data in Snowflake.

Connect Microsoft Power BI directly to governed data in Amazon SageMaker Unified Studio using new authentication modes in the Amazon Athena ODBC driver, with no third-party ODBC-JDBC bridge. Part 1 covers IAM Identity Center (IDC)-based domains with both DSN-based and DSN-less connection methods.

Connect Microsoft Power BI directly to governed data in Amazon SageMaker Unified Studio using the Amazon Athena ODBC driver. Part 2 covers IAM-based domains with SageMakerIam authentication, including AWS IAM Identity Center administrator setup, for both DSN-based and DSN-less connection methods.

Learn how to track Amazon SageMaker Unified Studio project costs by custom tags. This serverless solution enriches AWS Cost and Usage Report (CUR) data with custom project tags and visualizes cost by CostCenter, Team, or Environment in an Amazon Quick Sight dashboard.

Learn how to secure Amazon SageMaker Unified Studio by integrating it with an external SAML identity provider such as Okta. This post shows you how to apply conditional access policies that enforce device compliance, IP-based restrictions, and multi-factor authentication for your data and AI workloads.

As enterprise lakehouses grow to thousands of tables across business domains and regions, fine-grained access control becomes a governance bottleneck. This post shows how to combine AWS IAM Identity Center, AWS Lake Formation tag-based access control, and trusted identity propagation in Amazon SageMaker Unified Studio for automated, auditable, least-privilege access.

In this post, we show you how to use Amazon SageMaker Unified Studio to implement cross-account data sharing in Amazon Redshift using data mesh principles. We demonstrate how to build a scalable data mesh architecture that supports secure, auditable data sharing across AWS accounts while reducing operational burden.

Apache Spark Connect bridges the gap between these two worlds: you develop in local Python, but execute on AWS Glue against actual data. Today, AWS Glue interactive sessions support Spark Connect natively. You can connect from any environment that supports the PySpark remote() API, including VS Code, PyCharm, Amazon SageMaker Unified Studio notebooks, and standalone Python applications. You don’t need to install specialized kernels or manage cluster infrastructure.