? Learn why and how you can efficiently use Python to process data and build machine
learning models in Apache Spark 2.0.
? Develop and deploy efficient, scalable real-time Spark solutions.
? Take your understanding of using Spark with Python to the next level with this jump start
guide.

Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This course will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this course, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/PySpark-for-Beginners

What you will learn

1. Learn about Apache Spark and the Spark 2.0 architecture 2. Build and interact with Spark DataFrames using Spark SQL 3. Read, transform, and understand data and use it to train machine learning models 4. Build machine learning models with MLlib and ML