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

Benjamin Bengfort is a data scientist with a passion for massive machine learning involving gigantic natural language corpora, and has been leveraging that passion to develop a keen understanding of recommendation algorithms at Cobrain in Bethesda, MD where he serves as the Chief Data Scientist. With a professional background in military and intelligence, and an academic background in economics and computer science, he brings a unique set of skills and insights to his work. Ben believes that data is a currency that can pave the way to discovering insights and solve complex problems. He is also currently pursuing a PhD in Computer Science at the University of Maryland. Jenny Kim is an experienced data scientist who works in both commercial software efforts as well as in academia. She has significant experience in working with large scale data, machine learning, and Hadoop implementations in production and research environments. Jenny (with Benjamin Bengfort) built a large scale recommender system that used a web crawler to gather ontological information about apparel products and produce recommendations from transactions. Currently she teaches Introduction to Hadoop and Advanced Hadoop courses on Statistics.com.