Apache Spark Primer Course Details:

Apache Spark is an important component in the Hadoop Ecosystem as a cluster computing engine used for Big Data. Building on top of the Hadoop YARN and HDFS ecosystem, Spark offers faster in-memory processing for computing tasks when compared to Map/Reduce. It can be programmed in Java, Scala, Python, and R along with SQL-based front-ends.

This course introduces Scala, Python, or R developers to the world of Spark programming. It begins with an overview of the ecosystem and hands-on experience with the platform such as working with the Spark Shell, using RDDs, and DataFrames. You’ll later explore a wider-scoped introduction to NoSQL, Spark Streaming, Spark SQL, Spark MLLib, and how the pieces are put together in a larger application.

    No classes are currenty scheduled for this course.

    Call (919) 283-1653 to get a class scheduled online or in your area!

Overview of Spark

  • Hadoop Ecosystem
  • Hadoop YARN vs. Mesos
  • Spark vs. Map/Reduce
  • Spark: Lambda Architecture
  • Spark in the Enterprise Data Science Architecture

Spark Component Overview

  • Spark Shell
  • RDDs: Resilient Distributed Datasets
  • Data Frames
  • Spark 2 Unified DataFrames
  • Spark Sessions
  • Functional Programming
  • Spark SQL
  • MLib
  • Structured Streaming
  • Spark R
  • Spark and Python

RDDs: Resilient Distributed Datasets

  • Coding with RDDs
  • Transformations
  • Actions
  • Lazy Evaluation and Optimization
  • RDDs in Map/Reduce
  • Exercise: Working with RDDs


  • RDDs vs. DataFrames
  • Unified DataFrames (UDF) in Spark 2.x
  • Partitioning
  • Exercise: Working with Unified DataFrames

Advanced Spark Overview

  • NoSQL
  • Spark SQL
  • Spark Streaming
  • Spark ML Lib

*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.

Join an engaging hands-on learning environment, where you’ll learn:

  • The essentials of Spark architecture and applications
  • How to execute Spark Programs
  • How to create and manipulate both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
  • How Spark core components come together for complete applications

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

Before attending this course, you should have:

  • Experience programming in either Java, Python, R, or Scala (only one language needed)
  • Basic understanding of SQL


Data Scientists, Data Engineers, Software Engineers, Architects, and Developers.

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