Big Data Management : : Data Governance Principles for Big Data Analytics / / Peter Ghavami.

• Superior document: Title is part of eBook package: De Gruyter DG Ebook Package English 2021
• VerfasserIn: Ghavami, Peter,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2020]; ©2021
• Year of Publication: 2020
• Language: English
• Physical Description: 1 online resource (XVIII, 156 p.)
• Other title: Frontmatter --; Acknowledgments --; About the Author --; Contents --; Introduction --; Part 1: Big Data Overview --; Chapter 1 Introduction to Big Data --; Chapter 2 Enterprise Data Governance Directive --; Part 2: Big Data Governance Fundamentals --; Chapter 3 Data Risk Management --; Chapter 4 NoSQL Storage and Security Considerations --; Chapter 5 The Key Components of Big Data Governance --; Chapter 6 Big Data Governance Framework --; Chapter 7 Master Data Management --; Chapter 8 Big Data Governance Rules: Best Practices --; Chapter 9 Big Data Governance Best Practices --; Chapter 10 Big Data Governance Framework Program --; Part 3: Big Data and Model Risk Management --; Chapter 11 Why Data and Model Risk Management? --; Summary --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9783110664065; 9783110750720; 9783110750706; 9783110659061; 9783110704792; 9783110704716; 9783110704518; 9783110704624
• DOI: 10.1515/9783110664065
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Peter Ghavami.

Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.