| 000 | nam a22 4500 | ||
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| 999 |
_c10067 _d10067 |
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| 005 | 20191114150712.0 | ||
| 008 | 191114b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9788126579518 | ||
| 040 | _cAIKTC-KRRC | ||
| 041 | _aENG | ||
| 082 |
_2DDC23 _a006.342 _bACH/CHE |
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| 100 |
_97066 _aAcharya, Seema |
||
| 245 | _aBig data and analytics | ||
| 250 | _a2nd | ||
| 260 |
_aNew Delhi _bWiley India _c2019 |
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| 300 |
_axx, 364p. _bPaperback _c24*18 cm |
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| 520 |
_aBIG 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 but in what insights can be drawn from this data to facilitate better and faster decisions. This book Big Data and Analytics 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! _b Table Of Content: Chapter 1 Types of Digital Data What’s in Store? 1.1 Classification of Digital Data Chapter 2 Introduction to Big Data What’s in Store? 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 What’s in Store? 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 What’s in Store? 4.1 NoSQL (Not Only SQL) 4.2 Hadoop Remind Me Point Me (Books) Connect Me (Internet Resources) Test Me Chapter 5 Introduction to Hadoop What’s in Store? 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 What’s in Store? 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 What’s in Store? 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 What’s in Store? 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 What’s in Store? 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 What’s in Store? 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 JasperReport using Jaspersoft What’s in Store? 11.1 Introduction to JasperReports 11.2 Connecting to MongoDB NoSQL Database 11.3 Connecting to Cassandra NoSQL Database Chapter 12 Introduction to Machine Learning What’s in Store? 12.1 Introduction to Machine Learning 12.2 Machine Learning Algorithms Chapter 13 Few Interesting Differences What’s in Store? 13.1 Difference between Data Warehouse and Data Lake 13.2 Difference between RDBMS and HDFS 13.3 Difference between HDFS and HBase 13.4 Hadoop MapReduce versus Pig 13.5 Difference between Hadoop MapReduce and Spark 13.6 Difference between Pig and Hive Chapter 14 Big Data Trends in 2019 and Beyond What’s in Store? 14.1 Rise of the New Age “Data Curators” 14.2 CDOs are Stepping Up 14.3 Dark Data in the Cloud 14.4 Streaming the IoT for Machine Learning 14.5 Edge Computing 14.6 Open Source 14.7 Hadoop is Fundamental and will Remain So! 14.8 Chatbots will Get Smarter 14.9 Container(ed) Revolution 14.10 Commoditization of Visualization |
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| 650 | 0 |
_94622 _aComputer Engineering |
|
| 700 |
_910452 _aChellappan, Subhashini |
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| 942 |
_2ddc _cBK |
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