Spark Programming in Python for Beginners with Apache Spark 3

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67 on-demand videos & exercises
Level: Beginner
English
6hrs 35mins
Access on mobile, web and TV

Who's this course for?

This course is designed for software engineers willing to develop a data engineering pipeline and application using Apache Spark; for data architects and data engineers who are responsible for designing and building the organization’s data-centric infrastructure, for managers and architects who do not directly work with Spark implementation but work with the people who implement Apache Spark at the ground level.  This course does not require any prior knowledge of Apache Spark or Hadoop; only programming knowledge using Python programming language is required.

What you'll learn

  • Learn Apache Spark Foundation and Spark architecture.
  • Learn data engineering and data processing in Spark Work with data sources and sinks.
  • Work with data frames and Spark SQL.
  • Use PyCharm IDE for Spark development and debugging.
  • Learn unit testing, managing application logs, and cluster deployment.

Key Features

  • Build your own data engineering solutions using Spark structured API in Python.
  • Gain an in-depth understanding of the Apache Hadoop architecture, ecosystem, and practices.
  • Learn to apply Spark programming basics.

Course Curriculum

What to know about this course

If you are looking to expand your knowledge in data engineering or want to level up your portfolio by adding Spark programming to your skillset, then you are in the right place. This course will help you understand Spark programming and apply that knowledge to build data engineering solutions. This course is example-driven and follows a working session-like approach. We will be taking a live coding approach and explaining all the concepts needed along the way. 

In this course, we will start with a quick introduction to Apache Spark, then set up our environment by installing and using Apache Spark. Next, we will learn about Spark execution model and architecture, and about Spark programming model and developer experience. Next, we will cover Spark structured API foundation and then move towards Spark data sources and sinks.  Then we will cover Spark Dataframe and dataset transformations. We will also cover aggregations in Apache Spark and finally, we will cover Spark Dataframe joins. 

By the end of this course, you will be able to build data engineering solutions using Spark structured API in Python.  All the resources for the course are available at https://github.com/PacktPublishing/Spark-Programming-in-Python-for-Beginners-with-Apache-Spark-3

About the Author

ScholarNest

ScholarNest is a small team of people passionate about helping others learn and grow in their careers by bridging the gap between their existing and required skills.    Together, they have over 40+ years of experience in IT as a developer, architect, consultant, trainer, and mentor. They have worked with international software services organizations on various data-centric and Big Data projects.    It is a team of firm believers in lifelong continuous learning and skill development. To popularize the importance of continuous learning, they started publishing free training videos on their YouTube channel. They conceptualized the notion of continuous learning, creating a journal of our learning under the Learning Journal banner.