Big data fundamentals : concepts, drivers & techniques

Detalles Bibliográficos
Autor Principal: Erl, Thomas
Otros autores o Colaboradores: Khattak, Wajid, Buhler, Paul
Formato: Libro
Lengua:inglés
Datos de publicación: Indiana : Prentice Hall, 2016
Edición:1st ed.
Temas:
BIG DATA
PROCESAMIENTO DE DATOS
Acceso en línea:Consultar en el Cátalogo
Notas:Incluye índice
Descripción Física:xv, 218 p. : il.
ISBN:9780134291079
Tabla de Contenidos:
  • PART I: THE FUNDAMENTALS OF BIG DATA
  • CHAPTER 1: Understanding Big Data
  • Concepts and Terminology
  • Datasets
  • Data Analysis
  • Data Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Business Intelligence (BI)
  • Key Performance Indicators (KPI)
  • Big Data Characteristics
  • Volume
  • Velocity
  • Variety
  • Veracity
  • Value
  • Different Types of Data
  • Structured Data
  • Unstructured Data
  • Semi-structured Data
  • Metadata
  • Case Study Background
  • History Technical
  • Infrastructure and Automation Environment
  • Business Goals and Obstacles
  • Case Study Example
  • Identifying Data Characteristics
  • Volume Velocity
  • Variety
  • Veracity
  • Value
  • Identifying Types of Data
  • CHAPTER 2: Business Motivations and Drivers for Big Data Adoption
  • Marketplace Dynamics
  • Business Architecture
  • Business Process Management
  • Information and Communications Technology
  • Data Analytics and Data Science
  • Digitization
  • Affordable Technology and Commodity Hardware
  • Social Media
  • Hyper-Connected Communities and Devices
  • Cloud Computing
  • Internet of Everything (IoE)
  • Case Study Example
  • CHAPTER 3: Big Data Adoption and Planning Considerations
  • Organization Prerequisites
  • Data Procurement
  • Privacy
  • Security
  • Provenance
  • Limited Realtime Support
  • Distinct Performance Challenges
  • Distinct Governance Requirements
  • Distinct Methodology
  • Clouds
  • Big Data Analytics Lifecycle
  • Business Case Evaluation
  • Data Identification
  • Data Acquisition and Filtering
  • Data Validation and Cleansing
  • Data Aggregation and Representation
  • Data Analysis Data Visualization
  • Utilization of Analysis Results
  • Case Study Example
  • Big Data Analytics Lifecycle
  • Business Case Evaluation
  • Data Identification
  • Data Acquisition and Filtering
  • Data Extraction
  • Data Validation and Cleansing
  • Data Aggregation and Representation
  • Data Analysis
  • Data Visualization
  • Utilization of Analysis Results.
  • CHAPTER 4: Enterprise Technologies and Big Data Business Intelligence
  • Online Transaction Processing (OLTP)
  • Online Analytical Processing (OLAP)
  • Extract Transform Load (ETL)
  • Data Warehouses
  • Data Marts Traditional BI
  • Ad-hoc Reports
  • Dashboards
  • Big Data BI
  • Traditional Data Visualization
  • Data Visualization for Big Data
  • Case Study Example
  • Enterprise Technology
  • Big Data Business Intelligence
  • PART II: STORING AND ANALYZING BIG DATA
  • CHAPTER 5: Big Data Storage Concepts
  • Clusters
  • File Systems and Distributed File Systems
  • NoSQL
  • Sharding
  • Replication
  • Master-Slave
  • Peer-to-Peer
  • Sharding and Replication
  • Combining Sharding and Master-Slave Replication
  • Combining Sharding and Peer-to-Peer Replication
  • CAP Theore
  • ACID
  • BASE
  • Case Study Example
  • CHAPTER 6: Big Data Processing Concepts
  • Parallel Data Processing
  • Distributed Data Processing
  • Hadoop
  • Processing Workloads
  • Batch
  • Transactional
  • Cluster
  • Processing in Batch Mode
  • Batch Processing with
  • MapReduce Map and Reduce Tasks
  • Map
  • Combine
  • Partition
  • Shuffle and Sort
  • Reduce
  • A Simple MapReduce Example
  • Understanding MapReduce Algorithms
  • Processing in Realtime Mode
  • Speed Consistency Volume (SCV)
  • Event Stream Processing
  • Complex Event Processing
  • Realtime Big Data Processing and SCV
  • Realtime Big Data Processing and MapReduce
  • Case Study Example
  • Processing Workloads
  • Processing in Batch Mode
  • Processing in Realtime
  • CHAPTER 7: Big Data Storage Technology
  • On-Disk Storage Devices
  • Distributed File Systems
  • RDBMS Databases
  • NoSQL Databases
  • Characteristics
  • Rationale
  • Types
  • Key-Value
  • Document
  • Column-Family
  • Graph
  • NewSQL Databases
  • In-Memory Storage Devices
  • In-Memory Data Grids
  • Read-through
  • Write-through
  • Write-behind
  • Refresh-ahead
  • In-Memory Databases
  • Case Study Example
  • CHAPTER 8: Big Data Analysis Techniques
  • Quantitative Analysis
  • Qualitative Analysis
  • Data Mining
  • Statistical Analysis
  • A/B Testing
  • Correlation
  • Regression
  • Machine Learning
  • Classification (Supervised Machine Learning)
  • Clustering (Unsupervised Machine Learning)
  • Outlier Detection
  • Filtering
  • Semantic Analysis
  • Natural Language Processing
  • Text Analytics
  • Sentiment Analysis
  • Visual Analysis
  • Heat Maps
  • Time Series Plots
  • Network Graphs
  • Spatial Data Mapping
  • Case Study Example
  • Correlation
  • Regression
  • Time Series Plot
  • Clustering
  • Classification
  • APPENDIX A: Case Study Conclusion
  • About the Authors
  • Thomas Erl
  • Wajid Khattak
  • Paul Buhler
  • Index

Ejemplares similares