Data Analytics with Hadoop: An Introduction for Data Scientists.
About The Book.
All Indian Reprints of O'Reilly are printed in Grayscale.
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.
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, miningand 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, clusteringand collaborative filtering with Spark’s MLlib
