PART I: THE FUNDAMENTALS OF BIG DATA
CHAPTER 1: Understanding Big Data
Key Performance Indicators (KPI)
Technical Infrastructure and Automation Environment
Identifying Data Characteristics
CHAPTER 2: Business Motivations and Drivers for Big Data Adoption
Information and Communications Technology
Data Analytics and Data Science
Affordable Technology and Commodity Hardware
Hyper-Connected Communities and Devices
CHAPTER 3: Big Data Adoption and Planning Considerations
Distinct Performance Challenges
Distinct Governance Requirements
Data Acquisition and Filtering
Data Aggregation and Representation
Utilization of Analysis Results
Data Acquisition and Filtering
Data Aggregation and Representation
Utilization of Analysis Results
CHAPTER 4: Enterprise Technologies and Big Data Business Intelligence
Online Transaction Processing (OLTP)
Online Analytical Processing (OLAP)
Traditional Data Visualization
Data Visualization for Big Data
Big Data Business Intelligence
PART II: STORING AND ANALYZING BIG DATA
CHAPTER 5: Big Data Storage Concepts
File Systems and Distributed File Systems
Combining Sharding and Master-Slave Replication
Combining Sharding and Peer-to-Peer Replication
CHAPTER 6: Big Data Processing Concepts
Batch Processing with MapReduce
Understanding MapReduce Algorithms
Speed Consistency Volume (SCV)
Realtime Big Data Processing and SCV
Realtime Big Data Processing and MapReduce
CHAPTER 7: Big Data Storage Technology
CHAPTER 8: Big Data Analysis Techniques
Classification (Supervised Machine Learning)
Clustering (Unsupervised Machine Learning)