INTRODUCTION SECTION I: INTRODUCTION TO BIG DATA Introduction to Big Data The Three Dimensions of Analytics The Distinction between BI and Analytics Analytics Platform Framework Data Management Body of Knowledge (DMBOK) Data Maturity Model (DMM) SECTION II: BIG DATA GOVERNANCE FUNDAMENTALS Introduction Top 10 Data Breaches Case for Big Data Governance TOGAF View of Data Governance Data Lake vs. Data Warehouse History of Hadoop Hadoop Overview Security Tools for Hadoop The Components of Big Data Governance Myths about Big Data & Hadoop Lake Enterprise Data Governance Directive: Big Data Governance Framework: A Lean & Effective Model The Enterprise Big Data Governance Pyramid Introduction to Big Data Governance Rules Organization Data Stewardship Master Data Management Meta Data Management Security, Privacy & Compliance Quality Management Metadata Best Practices SECTION III: BIG DATA GOVERNANCE BEST PRACTICES Data Governance Best Practices Data Protection Security Architecture for Data Lake Data Structure Design Sandbox Functionality Overview Split Data Design SECTION IV: BIG DATA GOVERNANCE FRAMEWORK PROGRAM Big Data Governance Framework Program Overview Summary

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.

Peter Ghavami, Senior Vice President, Head of Wholesale Data Science & Analytics at Bank of America, USA