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DevOps for Databases

You're reading from   DevOps for Databases A practical guide to applying DevOps best practices to data-persistent technologies

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837637300
Length 446 pages
Edition 1st Edition
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Author (1):
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David Jambor David Jambor
Author Profile Icon David Jambor
David Jambor
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Database DevOps
2. Chapter 1: Data at Scale with DevOps FREE CHAPTER 3. Chapter 2: Large-Scale Data-Persistent Systems 4. Chapter 3: DBAs in the World of DevOps 5. Part 2: Persisting Data in the Cloud
6. Chapter 4: Cloud Migration and Modern Data(base) Evolution 7. Chapter 5: RDBMS with DevOps 8. Chapter 6: Non-Relational DMSs with DevOps 9. Chapter 7: AI, ML, and Big Data 10. Part 3: The Right Tool for the Job
11. Chapter 8: Zero-Touch Operations 12. Chapter 9: Design and Implementation 13. Chapter 10: Database Automation 14. Part 4: Build and Operate
15. Chapter 11: End-to-End Ownership Model – a Theoretical Case Study 16. Chapter 12: Immutable and Idempotent Logic – A Theoretical Case Study 17. Chapter 13: Operators and Self-Healing Data Persistent Systems 18. Chapter 14: Bringing Them Together 19. Part 5: The Future of Data
20. Chapter 15: Specializing in Data 21. Chapter 16: The Exciting New World of Data 22. Index 23. Other Books You May Enjoy

Automated data discovery and cataloging – unveiling the hidden treasures in today’s data landscape

As the digital revolution rages on, data has risen to become the world’s most valuable resource, driving innovation, strategic decisions, and operational efficiencies. However, as data grows in volume, variety, and velocity, businesses grapple with a fundamental challenge – knowing what data they have and where it is stored. Enter automated data discovery and cataloging – a groundbreaking technological innovation that enables businesses to navigate the increasingly complex data landscape effectively.

Understanding data discovery and cataloging

Data discovery refers to the process of finding and understanding patterns and trends in data. In contrast, data cataloging involves creating a comprehensive inventory of data assets and providing details about their source, usage, relationships, and business context. Combined, data discovery and cataloging...

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