Sean Owen and Juliet Hougland offer a practical overview of the basics of using Python data tools with a Hadoop cluster—using an interactive demo format with accompanying online materials and data. Sean and Juliet cover HDFS connectivity and dealing with raw data files, running SQL queries with a SQL-on-Hadoop system like Apache Hive or Apache Impala (incubating), and the basics of accessing data with Spark, creating new Spark DataFrames, and implementing the two most common modeling workflows: fitting a model on a single node using scikit, saving the model, and performing embarrassingly parallel model application and fitting a model to distributed data using Spark MLlib.
Juliet Hougland is a data scientist at Cloudera and contributor/committer/maintainer for the Sparkling Pandas project. Her commercial applications of data science include developing predictive maintenance models for oil and gas pipelines at Deep Signal and designing and building a platform for real-time model application, data storage, and model building at WibiData. Juliet was the technical editor for Learning Spark by Karau et al. and Advanced Analytics with Spark by Ryza et al. She holds an MS in applied mathematics from the University of Colorado, Boulder and graduated Phi Beta Kappa from Reed College with a BA in math-physics.
Sean Owen is director of data science at Cloudera in London. Before Cloudera, he founded Myrrix Ltd. (now the Oryx project) to commercialize large-scale real-time recommender systems on Hadoop. He is an Apache Spark committer, was a committer and VP for Apache Mahout, and is the coauthor of Advanced Analytics on Spark and Mahout in Action. Previously, Sean was a senior engineer at Google.
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