Big Data and Analytics
- Length: 224 pages
- Edition: 1
- Language: English
- Publisher: Notion Press
- Publication Date: 2022-01-12
- ISBN-10: B09QF8JN82
- ISBN-13: 9798885304870
- Sales Rank: #9918439 (See Top 100 Books)
Big data is a state-of-the-art technology that revolutionizes system design and decision-making. On the other hand, Hadoop is a distributed framework that allows the effective management of big data. This book combines theoretical and practical facets of big data technology. The first few chapters provide a theoretical introduction to big data and Hadoop, with individual chapters covering different components of the Hadoop ecosystem. The rest of the book provides lab tutorials, giving basic working knowledge of the different components and how they can synergistically be used to develop a big data application.
Key features of the book include:
It provides a background of the big data problem and introduces Hadoop in light of how it solves it.
It covers all the processes of the big data lifecycle and the different components of Hadoop that serve these processes.
It offers dedicated lab tutorials for installation and demonstration of the different components of the Hadoop ecosystem.
Cover Title Page Copyright Page Contents Preface Acknowledgments Chapter 1: Introduction to Big Data and Hadoop Objectives 1.1: Big Data Concept 1.2: IBM’s 3v Model 1.3: How Can Big Data Benefit Businesses 1.4: Hadoop and its Applications 1.5: Limitations of Existing Architectures 1.6: Key Characteristics of hadoop 1.7: Key Differences Between RDBMS and Hadoop Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 2: Hadoop and HDFS Architecture Objectives 2.1: Hadoop Ecosystem 2.2: Core Components of Hadoop 2.X 2.3: Hadoop Functions 2.4: Hadoop 2.X Cluster Architecture – Federation and Availability 2.5: Resource Management in Hadoop 2.X 2.6: Cluster Modes 2.7: Configuration Files Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 3: Basics of Mapreduce Framework Objectives 3.1: Mapreduce Programming Paradigm 3.2: Traditional Way of Processing Large Data 3.3: The Mapreduce Approach 3.4: Mapreduce vs Traditional Programming Approaches 3.5: Mapreduce Implementation 3.6: Mapreduce Architecture Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 4: Advanced Concepts in Mapreduce Programming Objectives 4.1: Input Splits in Mapreduce 4.2: Partitioner 4.3: Combiner 4.4: Map And Reduce Side Joins 4.5: Counters 4.6: Input Formats 4.7: Mrunit Testing Framework Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 5: Pig Objectives 5.1: Introduction to Pig 5.2: The Yahoo! Story 5.3: Key Characteristics 5.4: Performance: Pig vs. Mapreduce 5.5: Limitations 5.6: Applications 5.7: Working With Pig Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 6: Hive Objectives 6.1: Background 6.2: The Facebook Story 6.3: Hive Basics 6.4: Differences Between Hive and Pig 6.5: Differences Between Hive and Traditional Rdbms 6.6: Hive Architecture 6.7: Components of Hive 6.8: Limitations of Hive 6.9: Hive Scripting Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 7: NoSQL Databases and Hbase Objectives 7.1: Introduction 7.2: Need for Hbase 7.3: Classification of NoSQL Databases 7.4: Defining HBase 7.5: Uses of HBase 7.6: Limitations of HBase 7.7: Components of HBase 7.8: HBase Storage Architecture 7.9: Need for Zookeeper 7.10: Working in HBase Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 8: Oozie Objectives 8.1: Understanding Oozie 8.2: Functional Components of Oozie Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 9: Integrating R With Hadoop Objectives 9.1: Introduction to R 9.2: Using R With Hadoop 9.3: Integration Methods for R and Hadoop 9.4: Solving Problems With R and Hadoop Objective-Type Questions Short Answer Questions Long Answer Questions Chapter 10: Setting Up Hadoop Standalone Cluster Chapter 11: Setting Up Hadoop Multi-Node Cluster Chapter 12: Basic HDFS Commands Chapter 13: Writing and Executing Mapreduce Programs Chapter 14: Advanced Programming in Mapreduce Chapter 15: Pig Commands and Scripting Chapter 16: Hive Commands and Scripting Chapter 17: Working in HBase Chapter 18: Job Management in Oozie Chapter 19: Data Loading Techniques Chapter 20: Project Appendix: Index About the Author
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