DATA ANALYTICS WITH HADOOP: AN INTRODUCTION FOR DATA SCIENTISTS

By:

BENGFORT, BENJAMIN

Contributor(s):

KIM, JENNY

Material type: Text

TextPublication details: Navi Mumbai Shroff Publishers and Distributors Pvt. Ltd 2019Description: xvi, 268pISBN:

9789352133741

Subject(s):

DATA MINING, FILE ORGANIZATION (COMPUTER SCIENCE), APACHE HADOOP

DDC classification:

006.312/BEN/KIM

Summary: This book ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operationsor software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop providesand higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hiveand HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.

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Holdings
Item type	Current library	Call number	Status	Notes	Date due	Barcode
Books	Symbiosis Institute of Computer Studies and Research Programming Language	006.312/BEN/KIM (Browse shelf(Opens below))	Available	DATA MINING, FILE ORGANIZATION (COMPUTER SCIENCE), APACHE HADOOP		SICSR-B-19624

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This book ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operationsor software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop providesand higher order data workflows this framework can produce.
Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hiveand HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.

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