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Big Data And Analytics

Big Data And Analytics

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Big data is a term used for massive mounds of structured, semi-structured and unstructured data that has the potential to be mined for information. The real power lies not just in having colossal data ...

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About the Book

This book is a comprehensive coverage on the concepts and practice of Big Data, Hadoop and Analytics. From the Do It Yourself steps and guidelines to set up a Hadoop Cluster to the deeper understanding of concepts and ample time-tested hands-on practice exercises on the concepts learned, this one book has it all! Big Data and Analytics is a term used for massive mounds of structured, semi-structured and unstructured data that has the potential to be mined for information.
· Types of Digital Data
· Introduction to Big Data
· Big Data Analytics
· The Big Data Technology Landscape
· Introduction to Hadoop
· Introduction to MongoDB
· Introduction to Cassandra
· Introduction to MAPREDUCE Programming
· Introduction to Hive
· Introduction to Pig
· Jasper Report using Jasper soft
· Introduction to Machine Learning

About the Author

Seema Acharya Seema Acharya is Lead Principal with the Education, Training and Assessment Department of Infosys Limited. She is an educator by choice and vocation and has rich experience in both academia and the software industry. Her areas of interest and expertise are centered on Business Intelligence, Big Data and Analytics, technologies such as Data Warehousing, Data Mining, Data Analytics, Text Mining and Data Visualization. Subhashini Chellappan Subhashini Chellappan is a Lead with the Education, Training and Assessment Department of Infosys Limited. She has rich experience in both academia and the software industry. She has published couple of papers in various Journals and Conferences. Her areas of interest and expertise are centered on Business Intelligence, Big Data and Analytics technologies such as Hadoop, NoSQL Databases, Spark and Machine Learning.



Table of Contents:
Chapter 1 Types of Digital Data 1.1 Classification of Digital Data Chapter 2 Introduction to Big Data 2.1 Characteristics of Data 2.2 Evolution of Big Data 2.3 Definition of Big Data 2.4 Challenges with Big Data 2.5 What is Big Data? 2.6 Other Characteristics of Data Which are not Definitional Traits of Big Data 2.7 Why Big Data? 2.8 Are We Just an Information Consumer or Do we also Produce Information? 2.9 Traditional Business Intelligence (BI) versus Big Data 2.10 A Typical Data Warehouse Environment 2.11 A Typical Hadoop Environment 2.12 What is New Today? 2.13 What is changing in the Realms of Big Data? Chapter 3 Big Data Analytics 3.1 Where do we Begin? 3.2 What is Big Data Analytics? 3.3 What Big Data Analytics Isn’t? 3.4 Why this Sudden Hype Around Big Data Analytics? 3.5 Classification of Analytics 3.6 Greatest Challenges that Prevent Businesses from Capitalizing on Big Data 3.7 Top Challenges Facing Big Data 3.8 Why is Big Data Analytics Important? 3.9 What Kind of Technologies are we looking Toward to Help Meet the Challenges Posed by Big Data? 3.10 Data Science 3.11 Data Scientist…Your New Best Friend!!! 3.12 Terminologies Used in Big Data Environments 3.13 Basically Available Soft State Eventual Consistency (BASE) 3.14 Few Top Analytics Tools Chapter 4 The Big Data Technology Landscape 4.1 NoSQL (Not Only SQL) 4.2 Hadoop Chapter 5 Introduction to Hadoop 5.1 Introducing Hadoop 5.2 Why Hadoop? 5.3 Why not RDBMS? 5.4 RDBMS versus Hadoop 5.5 Distributed Computing Challenges 5.6 History of Hadoop 5.7 Hadoop Overview 5.8 Use Case of Hadoop 5.9 Hadoop Distributors 5.10 HDFS (Hadoop Distributed File System) 5.11 Processing Data with Hadoop 5.12 Managing Resources and Applications with Hadoop YARN (Yet another Resource Negotiator) 5.13 Interacting with Hadoop Ecosystem Chapter 6 Introduction to MongoDB 6.1 What is MongoDB? 6.2 Why MongoDB? 6.3 Terms Used in RDBMS and MongoDB 6.4 Data Types in MongoDB 6.5 MongoDB Query Language Chapter 7 Introduction to Cassandra 7.1 Apache Cassandra - An Introduction 7.2 Features of Cassandra 7.3 CQL Data Types 7.4 CQLSH 7.5 Keyspaces 7.6 CRUD (Create, Read, Update and Delete) Operations 7.7 Collections 7.8 Using a Counter 7.9 Time to Live (TTL) 7.10 Alter Commands 7.11 Import and Export 7.12 Querying System Tables 7.13 Practice Examples Chapter 8 Introduction to MAPREDUCE Programming 8.1 Introduction 8.2 Mapper 8.3 Reducer 8.4 Combiner 8.5 Partitioner 8.6 Searching 8.7 Sorting 8.8 Compression Chapter 9 Introduction to Hive 9.1 What is Hive? 9.2 Hive Architecture 9.3 Hive Data Types 9.4 Hive File Format 9.5 Hive Query Language (HQL) 9.6 RCFile Implementation 9.7 SerDe 9.8 User-Defined Function (UDF) Chapter 10 Introduction to Pig 10.1 What is Pig? 10.2 The Anatomy of Pig 10.3 Pig on Hadoop 10.4 Pig Philosophy 10.5 Use Case for Pig: ETL Processing 10.6 Pig Latin Overview 10.7 Data Types in Pig 10.8 Running Pig 10.9 Execution Modes of Pig 10.10 HDFS Commands 10.11 Relational Operators 10.12 Eval Function 10.13 Complex Data Types 10.14 Piggy Bank 10.15 User-Defined Functions (UDF) 10.16 Parameter Substitution 10.17 Diagnostic Operator 10.18 Word Count Example using Pig 10.19 When to use Pig? 10.20 When not to use Pig? 10.21 Pig at Yahoo! 10.22 Pig versus Hive Chapter 11 Jasper Report using Jasper soft 11.1 Introduction to Jasper Reports 11.2 Connecting to MongoDB NoSQL Database 11.3 Connecting to Cassandra NoSQL Database Chapter 12 Introduction to Machine Learning 12.1 Introduction to Machine Learning 12.2 Machine Learning Algorithms Remind Me Point Me (Book) Connect Me (Internet Resources) Test Me Assignment for Hands-On Practice Glossary Index


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Product Details
  • ISBN-13: 9788126554782
  • Publisher: Wiley India Pvt Ltd
  • Binding: Paperback
  • No of Pages: 356
  • ISBN-10: 8126554789
  • Publisher Date: April, 2015
  • Language: English

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