Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data.

With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work

Build a self-service portal to support data discovery, quality, lineage, and governance
Select the best approach for each self-service capability using open source cloud technologies
Tailor self-service for the people, processes, and technology maturity of your data platform
Implement capabilities to democratize data and reduce time to insight
Scale your self-service portal to support a large number of users within your organization