Just when you think you have your Kafka and Hadoop clusters set up and humming and you’re well on your path to democratizing data, you realize that you now have a very different set of challenges to solve. You want to provide unfettered access to data to your data scientists, but at the same time, you need to preserve the privacy of your members, who have entrusted you with their data. Shirshanka Das and Tushar Shanbhag outline the path LinkedIn has taken to protect member privacy in its scalable distributed data ecosystem built around Kafka and Hadoop. They also discuss three foundational building blocks for scalable data management that can meet data compliance regulations: a centralized metadata system, a standardized data lifecycle management platform, and a unified data access layer. Some of these systems are open source and can be of use to companies that are in a similar situation. Along the way, they also look to the future—specifically, to the General Data Protection Regulation, which comes into effect in 2018—and outline LinkedIn’s plans for addressing those requirements. But technology is just part of the solution. Shirshanka and Tushar also share the culture and process change they’ve seen happen at the company and the lessons they’ve learned about sustainable process and governance.