This is the code repository for PySpark for Beginners [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- Learn about Apache Spark and the Spark 2.0 architecture
- Build and interact with Spark DataFrames using Spark SQL
- Read, transform, and understand data and use it to train machine learning models
- Build machine learning models with MLlib and ML
To fully benefit from the coverage included in this course, you will need:
If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this course is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.
This course has the following software requirements:
Python 3
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent