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Data Analytics with Hadoop: An Introduction for Data Scientists

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Description: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, operations, or software development usually associated with distributed computing, you'll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and 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, Hive, and 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.Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark's MLlibWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Data Analytics with Hadoop: An Introduction for Data Scientists. To get started finding Data Analytics with Hadoop: An Introduction for Data Scientists, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 288

Format: PDF, EPUB & Kindle Edition

Publisher: O'Reilly Media

Release: 2016

ISBN: 1491913762

Description: 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, operations, or software development usually associated with distributed computing, you'll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and 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, Hive, and 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.Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark's MLlibWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Data Analytics with Hadoop: An Introduction for Data Scientists. To get started finding Data Analytics with Hadoop: An Introduction for Data Scientists, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Data Analytics with Hadoop: An Introduction for Data Scientists

Data Analytics with Hadoop: An Introduction for Data Scientists

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