DEV 360 - Apache Spark Essentials


About this course

This introductory course enables developers to get started developing big data applications with Apache Spark. In the first part of the course, you will use Spark’s interactive shell to load and inspect data. The course then describes the various modes for launching a Spark application. You will then go on to build and launch a standalone Spark application. The concepts are taught using scenarios that also form the basis of hands-on labs.

Right for you?

  • For application developers

Prerequisites for success in the course:

  • Required
    • Basic to intermediate Linux knowledge, including:
      • The ability to use a text editor, such as vi
      • Familiarity with basic command-line options such a mv, cp, ssh, grep, cd, useradd
    • Knowledge of application development principles
    • A Linux, Windows or MacOS computer with the MapR Sandbox installed (On-demand course)
    • Connection to a Hadoop cluster via SSH and web browser (for the ILT and vILT course)
  • Recommended

What’s next?


This course helps prepare you for the MCSD – MapR Certified Spark Developer certification exam.


Lesson 1:
Introduction to Apache Spark
  • Describe the features of Apache Spark
  • Advantages of Spark
  • How Spark fits in with the Big Data application stack
  • How Spark fits in with Hadoop
  • Define Apache Spark components
Lesson 2:
Load and Inspect Data in Apache Spark
  • Describe different ways of getting data into Spark
  • Create and use Resilient Distributed Datasets (RDDs)
  • Apply transformation to RDDs
  • Use actions on RDDs
  • Lab: Load and inspect data in RDD
  • Cache intermediate RDDs
  • Use Spark DataFrames for simple queries
  • Lab: Load and inspect data in DataFrames
Lesson 3:
Build a Simple Apache Spark Application
  • Define the lifecycle of a Spark program
  • Define the function of SparkContext
  • Lab: Create the application
  • Define different ways to run a Spark application
  • Run your Spark application
  • Lab: Launch the application
  • Supplemental Lab