All news with #aws clean rooms tag

AWS Clean Rooms adds Spark SQL tuning and cache options

🔧 AWS Clean Rooms now supports advanced runtime configurations to improve Spark SQL query performance. Customers can set Spark properties—such as shuffle partition counts and autoBroadcastJoinThreshold—select compute sizes or custom worker counts, and opt to cache existing or newly created tables containing query results to accelerate complex, large-scale queries. These controls enable collaborators to tune performance, scale, and cost for workloads like advertising lift analysis without changing SQL logic.

AWS Clean Rooms Adds Cross-Region Data Collaboration

🌐 AWS Clean Rooms now supports cross-region collaboration, letting organizations analyze partner data stored in different AWS and Snowflake Regions without copying or sharing underlying datasets. Collaboration creators can specify allowed result regions to help meet data residency and sovereignty requirements. This reduces integration work—no new pipelines or replication—and enables faster, secure joint analyses across advertising, investment, and R&D use cases.

AWS Clean Rooms Adds Data Access Budgets and Limits

🔒 AWS Clean Rooms now supports data access budgets for tables in a collaboration, letting data owners limit how often their data can be analyzed for custom ML training, inference, SQL queries, or PySpark jobs. Administrators can set daily, weekly, or monthly refresh budgets, lifetime caps, or both; once a budget is exhausted the system blocks further analyses until the budget refreshes. Budgets may be edited or reset at any time to suit changing needs. This privacy control reduces unintended data exposure while maintaining collaborative analysis.

AWS Clean Rooms adds incremental ID mapping for sync

🔁 AWS Clean Rooms now supports incremental processing for rule-based ID mapping workflows using AWS Entity Resolution, enabling collaborators to populate ID mapping tables with only new, modified, or deleted records since the last analysis. This reduces the need for full-table reprocessing and enables near-real-time synchronization of matched identifiers across partners while preserving Clean Rooms’ privacy controls. Use cases include measurement providers keeping offline purchase data current with advertisers and publishers to enable always-on campaign measurement, lower costs, and maintain collaborator privacy.

AWS Clean Rooms Adds Configurable PySpark Compute Capacity

🔧 AWS Clean Rooms now lets customers configure compute size for PySpark analyses, enabling selection of instance type and cluster size at job runtime for each analysis. Customers can choose larger instances for complex datasets and higher performance or smaller instances to optimize costs. The change provides flexible, per-job resource allocation to balance scale, throughput, and budget while maintaining Clean Rooms' collaborative data protections.

AWS Clean Rooms: Add Data Providers to Collaborations

🔒 AWS Clean Rooms now lets collaboration owners add new data provider members to existing collaborations, enabling partners to contribute data without creating a separate collaboration. New members can be configured to only supply data while inheriting the collaboration’s existing privacy controls and access rules. Invitations and member additions are recorded in the collaboration change history for transparency and auditability. This reduces onboarding time for multi‑party workflows such as publisher–advertiser measurement and third‑party enrichment.

AWS Clean Rooms adds PySpark error message controls

🔧 AWS Clean Rooms now lets code authors configure error message detail for analyses using PySpark. When every collaboration member approves an analysis, authors can enable more detailed errors to accelerate debugging and testing. This reduces troubleshooting time for models such as marketing attribution from weeks to hours or days while preserving collaborator data protections.