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, youâ??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. Youâ??ll also learn about the analytical processes and data systems available to build and empower data products that can handleâ??and actually requireâ??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 Sparkâ??s MLlib

Також купити книгу Data Analytics with Hadoop: An Introduction for Data Scientists, Benjamin Bengfort, Jenny Kim Ви можете по посиланню