Apache PySpark by Example

Apache PySpark by Example

1h 58mIntermediate2019-01-31

Authors

Jonathan Fernandes

Jonathan Fernandes

Consultant focusing on data science, AI, and big data

Course details

Want to get up and running with Apache Spark as soon as possible? If you're well versed in Python, the Spark Python API (PySpark) is your ticket to accessing the power of this hugely popular big data platform. This practical, hands-on course helps you get comfortable with PySpark, explaining what it has to offer and how it can enhance your data science work. To begin, instructor Jonathan Fernandes digs into the Spark ecosystem, detailing its advantages over other data science platforms, APIs, and tool sets. Next, he looks at the DataFrame API and how it's the platform's answer to many big data challenges. Finally, he goes over Resilient Distributed Datasets (RDDs), the building blocks of Spark.

Learning objectives
Benefits of the Apache Spark ecosystem
Working with the DataFrame API
Working with columns and rows
Leveraging built-in Spark functions
Creating your own functions in Spark
Working with Resilient Distributed Datasets (RDDs)

Skills covered

Spark DataFramesData EngineeringData ScienceOne-Off

Concepts

Introduction

  • Apache PySpark
  • What you should know

Introduction to Apache Spark

  • The Apache Spark ecosystem
  • Why Spark
  • Spark origins and Databricks
  • Spark components
  • Partitions, transformations, lazy evaluations, and actions

Technical Setup

  • Set up the lab environment
  • Download a dataset
  • Importing

Working with the DataFrame API

  • The DataFrame API
  • Working with DataFrames
  • Schemas
  • Working with columns
  • Working with rows
  • Challenge
  • Solution

Functions

  • Built-in functions
  • Working with dates
  • User-defined functions
  • Working with joins
  • Challenge
  • Solution

Resilient Distributed Datasets (RDDs)

  • RDDs
  • Working with RDDs

Conclusion

  • Next steps
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