Book Image

PySpark for Beginners [Video]

Book Image

PySpark for Beginners [Video]

Overview of this book

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
Table of Contents (6 chapters)
Chapter 5
Introducing MLlib
Content Locked
Section 2
Getting to Know Your Data
In order to build a statistical model in an informed way, an intimate knowledge of the dataset is necessary. Without knowing the data it is possible to build a successful model, but it is then a much more arduous task, or it would require more technical resources to test all the possible combinations of features. Therefore, after spending the required 80% of the time cleaning the data, we spend the next 15% getting to know it! - Look at the descriptive statistics - Learn about correlations - See statistical testing