In this Back to Basics episode, join Brian as he discusses how to leverage AWS Clean Rooms for secure data collaboration and analytics across organizations – without compromising data privacy or compliance standards. We walk through a real example where a sneaker brand, ad platform, and market research firm need to combine sensitive customer datasets to measure a marketing campaign’s impact. However, sharing raw data raises privacy concerns. See how AWS Clean Rooms enables multi-party analytics on collective data, while allowing owners to maintain control over their datasets through granular access rules. No raw data is exposed or moved during the collaboration.
You’ll get step-by-step guidance on setting up an AWS Clean Room:
1) Creating a collaboration and inviting partners
2) Configuring data tables with analysis rules
3) Associating datasets within the collaboration
4) Running SQL queries on the combined data
We also cover key AWS Clean Rooms concepts like privacy controls, analysis rules, query output management, auditing, and how it differs from services like AWS Glue and Lake Formation. Discover use cases ideal for AWS Clean Rooms, such as multi-party analytics on structured datasets requiring stringent privacy safeguards for compliance. Don’t let privacy roadblocks hamper data-driven insights. Unlock new opportunities through responsible, secure data collaboration using AWS Clean Rooms!
Additional Resources:
AWS Clean Rooms: https://aws.amazon.com/clean-rooms/
Guidance for Building Queries in AWS Clean Rooms: https://aws.amazon.com/solutions/guidance/building-queries-clean-rooms/
Check out more resources for architecting in the #AWS cloud:
http://amzn.to/3qXIsWN
#AWS #AmazonWebServices #CloudComputing #BackToBasics #AWSCleanRooms #DataCollaboration #DataPrivacy #DataCollaboration #DataAnalytics #DataGovernance #DataSecurity
