Big data analytics : (Record no. 24547)

000 -LEADER
fixed length control field 04009nam a22002657a 4500
003 - CONTROL NUMBER IDENTIFIER
control field BD-DhULA
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240128123445.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240128b -uk||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781785884696
040 ## - CATALOGING SOURCE
Original cataloging agency BD-DhULA
Transcribing agency BD-DhULA
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number 23
Classification number 005.7
Item number ANB
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 4773
Personal name Ankam, Venkat
245 ## - TITLE STATEMENT
Title Big data analytics :
Remainder of title a handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters /
Statement of responsibility, etc. by Venkat Ankam
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Birmingham, UK :
Name of publisher, distributor, etc. Packt Publishing,
Date of publication, distribution, etc. c2016.
300 ## - PHYSICAL DESCRIPTION
Extent xv, 300 p. :
Other physical details ill. ;
Dimensions 24 cm.
500 ## - GENERAL NOTE
General note Includes Index.
520 ## - SUMMARY, ETC.
Summary, etc. A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clustersAbout This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components - Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components - HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science.
526 ## - STUDY PROGRAM INFORMATION NOTE
Program name CSE
590 ## - LOCAL NOTE (RLIN)
Local note(Name of catalogher) 424018
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4770
Topical term or geographic name as entry element Data mining
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4774
Topical term or geographic name as entry element Data processing
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 1531
Topical term or geographic name as entry element CSE
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 1532
Topical term or geographic name as entry element Computer science and engineering
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type BOOK
Suppress in OPAC No
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
No No   No Yes ULAB Library ULAB Library Text Book Area - CSE Dept. 21/01/2024 Book Finder 4934.39   005.7 ANB 21528 28/01/2024 1 28/01/2024 BOOK
No No   No Yes ULAB Library ULAB Library Text Book Area - CSE Dept. 21/01/2024 Book Finder 4934.39   005.7 ANB 21529 28/01/2024 2 28/01/2024 BOOK


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