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

Table of Contents

Part I. Self-Service Data Discovery

Chapter 2. Metadata Catalog Service

Chapter 3. Search Service

Chapter 4. Feature Store Service

Chapter 5. Data Movement Service

Chapter 6. Clickstream Tracking Service

Part II. Self-Service Data Prep

Chapter 7. Data Lake Management Service

Chapter 8. Data Wrangling Service

Chapter 9. Data Rights Governance Service

Part III. Self-Service Build

Chapter 10. Data Virtualization Service

Chapter 11. Data Transformation Service

Chapter 12. Model Training Service

Chapter 13. Continuous Integration Service

Chapter 14. A/B Testing Service

Part IV. Self-Service Operationalize

Chapter 15. Query Optimization Service

Chapter 16. Pipeline Orchestration Service

Chapter 17. Model Deploy Service

Chapter 18. Quality Observability Service

Chapter 19. Cost Management Service

About the Author

Sandeep Uttamchandani is the Chief Data Officer and VP of Product Engineering at Unravel Data Systems. He brings nearly two decades of experience building enterprise data products as well as running petabyte-scale data platforms for business-critical analytics and ML applications. Most recently he was at Intuit, where he ran the data platform team powering analytics and ML for Intuit's financial accounting, payroll, and payments products. Previously in his career, Sandeep was co-founder and CEO of a startup using ML for managing security vulnerabilities of open-source products. He has played engineering leadership roles at VMware and IBM for 15+ years.

Sandeep holds more than 40 issued patents, has 25+ publications in key technical conferences, and has received several product innovation and management excellence awards. He is a regular speaker in data conferences and a guest lecturer at universities. He advises startups and has served as a program/steering committee member for several conferences, including serving as Co-chair of Gartner's SF CDO Executive Summit, and Usenix Operational ML (OpML) conference. Sandeep holds a Ph.D and a Master's in Computer Science from the University of Illinois at Urbana-Champaign.