AWS First Steps
New feature

Amazon SageMaker Unified Studio now supports ODBC connections for Microsoft Power BI and other analytics tools

Amazon SageMaker Unified Studio now supports ODBC connections, enabling Microsoft Power BI and other ODBC-compatible tools to directly access governed data in SageMaker Unified Studio projects using the Amazon Athena ODBC driver

Amazon SageMaker Unified Studio now supports ODBC connections, allowing data users to connect directly to managed data within SageMaker Unified Studio projects via the Amazon Athena ODBC driver using tools like Microsoft Power BI. Connection strings and parameters can be copied from the project's JDBC and ODBC connections tab for DSN-less connections or to configure named DSNs. The Amazon Athena ODBC driver (version 2.2.0.1 or later) integrates directly with SageMaker Unified Studio in two modes: SageMakerBrowserIdc mode (via AWS IAM Identity Center and external identity providers) and SageMakerIam mode (using temporary AWS credentials). ODBC connections are available in all AWS regions where SageMaker Unified Studio is offered, at no additional cost. For more information, see the Amazon SageMaker Unified Studio user guide and Amazon Athena ODBC driver documentation.

Why it matters

Data analysts and business intelligence professionals can access governed data using their existing tools and workflows, improving analysis process efficiency

Read the original AWS announcement

Related articles and terms

Amazon SageMakerAn integrated platform for building, training, and deploying machine learning modelsNo-Code ML with Amazon SageMaker Canvas - Building Prediction Models with a Visual InterfaceA visual tool for building ML models without writing code. Simply upload a CSV and select a prediction target to build a model, with bidirectional sharing with Studio.Building BI Dashboards with Amazon QuickSight - Serverless Analytics and Embedded VisualizationAchieve fast queries with the SPICE engine and integrate BI capabilities into your own applications using the embedding API. This article introduces Q's natural language queries and Reader session-based pricing.Building an ML Platform with Amazon SageMaker - From Model Development to DeploymentFrom development in Studio to managed spot training, MLOps with Pipelines, and data drift detection with Model Monitor, this article covers how to integrate the entire ML lifecycle.BI Dashboard Visualization - Building a Data-Driven Decision Platform with Amazon QuickSightExplains how to build interactive BI dashboards with Amazon QuickSight and a serverless data analytics platform with Athena integration. Covers high-speed visualization with the SPICE engine and practical methods for sharing insights across the organization.