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    Data Wrangling and Modelling with PySpark

    Apache Spark is an open-source distributed engine for querying and processing data. In this three-day hands-on workshop, you will learn how to leverage Spark from Python to process large amounts of data.

    After a presentation of the Spark 2.0 architecture, we’ll begin manipulating Resilient Distributed Datasets (RDDs) and work our way up to Spark DataFrames. The concept of lazy execution is discussed in detail and we demonstrate various transformations and actions specific to RDDs and DataFrames. You’ll learn how DataFrames can be manipulated using SQL queries.

    We’ll show you how to apply supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You’ll also see unsupervised machine learning models such as PCA and K-means clustering.

    By the end of this workshop, you will have a solid understanding of how to process data using PySpark and you will understand how to use Spark’s machine learning library to build and train various machine learning models.