Course provided by Udemy

Study type: Online

Starts: Anytime

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Overview

Do you want to learn how to handle massive amounts of data at scale?

Learn Apache Spark 3 and pass the Databricks Certified Associate Developer for Apache Spark 3.0

Hi, My name is Wadson, and I’m a Databricks Certified Associate Developer for Apache Spark 3.0

In today’s data-driven world, Apache Spark has become the standard big-data cluster processing framework.

Apache Spark is used for Data Engineering, Data Science, and Machine Learning.

I will teach you everything you need to know about getting started with Apache Spark.

You will learn the Architecture of Apache Spark and use it’s Core APIs to manipulate complex data.
You will write queries to perform transformations such as Join, Union, GroupBy, and more.

This course is for beginners.
You do not need previous knowledge of Apache Spark.

There are Notebooks available to download so that you can follow along with me in the videos.
The Notebooks contains all the source code I use in the course.
There are also Quizzes to help you assess your understanding of the topics.

Expected Outcomes

  1. How to prepare for the Databricks Certified Associate Developer For Apache Spark 3 Certification Exam
  2. The Architecture of an Apache Spark Application
  3. Learn how Apache Spark runs on a cluster of computer
  4. Learn the Execution Hierarchy of Apache Spark
  5. Create DataFrame from files and Scala Collections
  6. Spark DataFrame API and SQL functions
  7. Learn the different techniques to select the columns of a DataFrame
  8. How to define the schema of a DataFrame and set the data types of the columns
  9. Apply various methods to manipulate the columns of a DataFrame
  10. How to filter your DataFrame based on specifics rules
  11. Learn how to sort data in a specific order
  12. Learn how to sort rows of a DataFrame in a specific order
  13. How to arrange the rows of DataFrame as groups
  14. How to handle NULL Values in a DataFrame
  15. How to use JOIN or UNION to combine two data sets
  16. How you can save the result of complex data transformations to an external storage system
  17. The different deployment modes of an Apache Spark Application
  18. working with UDFs and Spark SQL functions
  19. How to use Databricks Community Edition to write Apache Spark Code