Big data and analytics (Record no. 10067)
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| 000 -LEADER | |
|---|---|
| fixed length control field | nam a22 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20191114150712.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 191114b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788126579518 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | AIKTC-KRRC |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | ENG |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | DDC23 |
| Classification number | 006.342 |
| Item number | ACH/CHE |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 7066 |
| Personal name | Acharya, Seema |
| 245 ## - TITLE STATEMENT | |
| Title | Big data and analytics |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Wiley India |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xx, 364p. |
| Other physical details | | Binding - Paperback | |
| Dimensions | 24*18 cm |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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 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! |
| Expansion of summary note | <br/><br/>Table Of Content:<br/>Chapter 1 Types of Digital Data<br/><br/>What’s in Store?<br/><br/>1.1 Classification of Digital Data<br/><br/>Chapter 2 Introduction to Big Data<br/><br/>What’s in Store?<br/><br/>2.1 Characteristics of Data<br/><br/>2.2 Evolution of Big Data<br/><br/>2.3 Definition of Big Data<br/><br/>2.4 Challenges with Big Data<br/><br/>2.5 What is Big Data?<br/><br/>2.6 Other Characteristics of Data Which are not Definitional Traits of Big Data<br/><br/>2.7 Why Big Data?<br/><br/>2.8 Are We Just an Information Consumer or Do We also Produce Information?<br/><br/>2.9 Traditional Business Intelligence (BI) versus Big Data<br/><br/>2.10 A Typical Data Warehouse Environment<br/><br/>2.11 A Typical Hadoop Environment<br/><br/>2.12 What is New Today?<br/><br/>2.13 What is Changing in the Realms of Big Data?<br/><br/>Chapter 3 Big Data Analytics<br/><br/>What’s in Store?<br/><br/>3.1 Where do we Begin?<br/><br/>3.2 What is Big Data Analytics?<br/><br/>3.3 What Big Data Analytics Isn’t?<br/><br/>3.4 Why this Sudden Hype Around Big Data Analytics?<br/><br/>3.5 Classification of Analytics<br/><br/>3.6 Greatest Challenges that Prevent Businesses from Capitalizing on Big Data<br/><br/>3.7 Top Challenges Facing Big Data<br/><br/>3.8 Why is Big Data Analytics Important?<br/><br/>3.9 What Kind of Technologies are we Looking Toward to Help Meet the Challenges Posed by Big Data?<br/><br/>3.10 Data Science<br/><br/>3.11 Data Scientist…Your New Best Friend!!!<br/><br/>3.12 Terminologies Used in Big Data Environments<br/><br/>3.13 Basically Available Soft State Eventual Consistency (BASE)<br/><br/>3.14 Few Top Analytics Tools<br/><br/>Chapter 4 The Big Data Technology Landscape<br/><br/>What’s in Store?<br/><br/>4.1 NoSQL (Not Only SQL)<br/><br/>4.2 Hadoop<br/><br/>Remind Me<br/><br/>Point Me (Books)<br/><br/>Connect Me (Internet Resources)<br/><br/>Test Me<br/><br/>Chapter 5 Introduction to Hadoop<br/><br/>What’s in Store?<br/><br/>5.1 Introducing Hadoop<br/><br/>5.2 Why Hadoop?<br/><br/>5.3 Why not RDBMS?<br/><br/>5.4 RDBMS versus Hadoop<br/><br/>5.5 Distributed Computing Challenges<br/><br/>5.6 History of Hadoop<br/><br/>5.7 Hadoop Overview<br/><br/>5.8 Use Case of Hadoop<br/><br/>5.9 Hadoop Distributors<br/><br/>5.10 HDFS (Hadoop Distributed File System)<br/><br/>5.11 Processing Data with Hadoop<br/><br/>5.12 Managing Resources and Applications with Hadoop YARN (Yet Another Resource Negotiator)<br/><br/>5.13 Interacting with Hadoop Ecosystem<br/><br/>Chapter 6 Introduction to MongoDB<br/><br/>What’s in Store?<br/><br/>6.1 What is MongoDB?<br/><br/>6.2 Why MongoDB?<br/><br/>6.3 Terms Used in RDBMS and MongoDB<br/><br/>6.4 Data Types in MongoDB<br/><br/>6.5 MongoDB Query Language<br/><br/>Chapter 7 Introduction to Cassandra<br/><br/>What’s in Store?<br/><br/>7.1 Apache Cassandra – An Introduction<br/><br/>7.2 Features of Cassandra<br/><br/>7.3 CQL Data Types<br/><br/>7.4 CQLSH<br/><br/>7.5 Keyspaces<br/><br/>7.6 CRUD (Create, Read, Update, and Delete) Operations<br/><br/>7.7 Collections<br/><br/>7.8 Using a Counter<br/><br/>7.9 Time to Live (TTL)<br/><br/>7.10 Alter Commands<br/><br/>7.11 Import and Export<br/><br/>7.12 Querying System Tables<br/><br/>7.13 Practice Examples<br/><br/>Chapter 8 Introduction to MAPREDUCE Programming<br/><br/>What’s in Store?<br/><br/>8.1 Introduction<br/><br/>8.2 Mapper<br/><br/>8.3 Reducer<br/><br/>8.4 Combiner<br/><br/>8.5 Partitioner<br/><br/>8.6 Searching<br/><br/>8.7 Sorting<br/><br/>8.8 Compression<br/><br/>Chapter 9 Introduction to Hive<br/><br/>What’s in Store?<br/><br/>9.1 What is Hive?<br/><br/>9.2 Hive Architecture<br/><br/>9.3 Hive Data Types<br/><br/>9.4 Hive File Format<br/><br/>9.5 Hive Query Language (HQL)<br/><br/>9.6 RCFile Implementation<br/><br/>9.7 SerDe<br/><br/>9.8 User-Defined Function (UDF)<br/><br/>Chapter 10 Introduction to Pig<br/><br/>What’s in Store?<br/><br/>10.1 What is Pig?<br/><br/>10.2 The Anatomy of Pig<br/><br/>10.3 Pig on Hadoop<br/><br/>10.4 Pig Philosophy<br/><br/>10.5 Use Case for Pig: ETL Processing<br/><br/>10.6 Pig Latin Overview<br/><br/>10.7 Data Types in Pig<br/><br/>10.8 Running Pig<br/><br/>10.9 Execution Modes of Pig<br/><br/>10.10 HDFS Commands<br/><br/>10.11 Relational Operators<br/><br/>10.12 Eval Function<br/><br/>10.13 Complex Data Types<br/><br/>10.14 Piggy Bank<br/><br/>10.15 User-Defined Functions (UDF)<br/><br/>10.16 Parameter Substitution<br/><br/>10.17 Diagnostic Operator<br/><br/>10.18 Word Count Example using Pig<br/><br/>10.19 When to use Pig?<br/><br/>10.20 When not to use Pig?<br/><br/>10.21 Pig at Yahoo!<br/><br/>10.22 Pig versus Hive<br/><br/>Chapter 11 JasperReport using Jaspersoft<br/><br/>What’s in Store?<br/><br/>11.1 Introduction to JasperReports<br/><br/>11.2 Connecting to MongoDB NoSQL Database<br/><br/>11.3 Connecting to Cassandra NoSQL Database<br/><br/>Chapter 12 Introduction to Machine Learning<br/><br/>What’s in Store?<br/><br/>12.1 Introduction to Machine Learning<br/><br/>12.2 Machine Learning Algorithms<br/><br/>Chapter 13 Few Interesting Differences<br/><br/>What’s in Store?<br/><br/>13.1 Difference between Data Warehouse and Data Lake<br/><br/>13.2 Difference between RDBMS and HDFS<br/><br/>13.3 Difference between HDFS and HBase<br/><br/>13.4 Hadoop MapReduce versus Pig<br/><br/>13.5 Difference between Hadoop MapReduce and Spark<br/><br/>13.6 Difference between Pig and Hive<br/><br/>Chapter 14 Big Data Trends in 2019 and Beyond<br/><br/>What’s in Store?<br/><br/>14.1 Rise of the New Age “Data Curators”<br/><br/>14.2 CDOs are Stepping Up<br/><br/>14.3 Dark Data in the Cloud<br/><br/>14.4 Streaming the IoT for Machine Learning<br/><br/>14.5 Edge Computing<br/><br/>14.6 Open Source<br/><br/>14.7 Hadoop is Fundamental and will Remain So!<br/><br/>14.8 Chatbots will Get Smarter<br/><br/>14.9 Container(ed) Revolution<br/><br/>14.10 Commoditization of Visualization |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 10452 |
| Personal name | Chellappan, Subhashini |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Reference | School of Engineering & Technology | School of Engineering & Technology | Reference Section | 25/11/2019 | 2 | 471.20 | 006.342 ACH/CHE | E15160 | 25/06/2025 | 589.00 | 25/11/2019 | Books |