Securely Scaling Big Data Access Controls At Pinterest

Securely Scaling Big Data Access Controls At Pinterest

摘要

Businesses collect many different types of data. Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary. As businesses grow, so does the variety of these datasets and the complexity of their handling requirements. Consequently, access control mechanisms also need to scale constantly to handle the ever-increasing diversification. Pinterest decided to invest in a newer technical framework to implement a finer grained access control (FGAC) framework. The result is a multi-tenant Data Engineering platform, allowing users and services access to only the data they require for their work. In this post, we focus on how we enhanced and extended Monarch, Pinterest’s Hadoop based batch processing system, with FGAC capabilities.