Lesson 1: Introduction to the Spark Environment
Lesson 1, “Introduction to the Spark Environment,” introduces Spark and provides context for the history and motivation for the framework. This lesson covers how to install and set up Spark locally, work with the Spark REPL and Jupyter notebook, and the basics of programming with Spark.
Lesson 2: Spark Programming APIs
Lesson 2, “Spark Programming APIs,” covers each of the various Spark programming interfaces. This lesson highlights the differences between and the tradeoffs of the Python (PySpark), R (SparkR), and SQL (Spark SQL and DataFrames) APIs as well as typical workflows for which each is best suited.
Lesson 3: Your First Spark Application
Lesson 3, “Your First Spark Application,” walks you through a case study with DonorsChoose.org data showing how Spark fits into the typical data science workflow. This lesson covers how to perform exploratory data analysis at scale, apply natural language processing techniques, and write an implementation of the k-means algorithm for unsupervised learning on text data.
Lesson 4: Spark Internals
Lesson 4, “Spark Internals,” peels back the layers of the framework and walks you through how Spark executes code in a distributed fashion. This lesson starts with a primer on distributed systems theory before diving into the Spark execution context, the details of RDDs, and how to run Spark in cluster mode on Amazon EC2. The lesson finishes with best practices for monitoring and tuning the performance of a Spark application.
Lesson 5: Advanced Applications
Lesson 5, “Advanced Applications,” takes you through a KDD cup competition, showing you how to leverage Spark’s higher level machine learning libraries (MLlib and spark.ml). The lesson covers the basics of machine learning theory, shows you how to evaluate the performance of models through cross validation, and demonstrates how to build a machine learning pipeline with Spark. The lesson finishes by showing you how to serialize and deploy models for use in a production setting.
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