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Data Analytics with Hadoop (Paperback)
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Data Analytics with Hadoop (Paperback)
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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 computing Use
design patterns and parallel analytical algorithms to create
distributed data analysis jobs Learn about data management, mining,
and warehousing in a distributed context using Apache Hive and
HBase Use Sqoop and Apache Flume to ingest data from relational
databases Program complex Hadoop and Spark applications with Apache
Pig and Spark DataFrames Perform machine learning techniques such
as classification, clustering, and collaborative filtering with
Spark's MLlib
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