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Introduction to Apache Spark

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Course Summary

Home > Courses >Big Data Training >Apache Spark >Introduction to Apache Spark

The Introduction to Apache Spark training course is designed to demonstrate the necessary skills to work with Apache Spark, an open-source engine for data in the Hadoop ecosystem optimized for speed and advanced analytics.

The course begins by examining how to use Spark as an alternative to traditional MapReduce processing. Next, it explores how Spark supports streamed data processing and iterative algorithms. The course concludes with a lesson on how Spark enables jobs to run faster than traditional Hadoop MapReduce.

Purpose
Learn how to use Apache Spark as an alternative to traditional MapReduce processing.
Audience
Developers working on projects that use traditional Hadoop MapReduce.
Role
Software Developer
Skill Level
Introduction
Style
Hack-a-thon - Learning Spikes - Workshops
Duration
3 Days
Related Technologies
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  • Productivity Objectives:
    • Describe how Apache Spark and Hadoop fit together
    • List three motivations for using Spark
    • Understand Resilient Distributed Datasets (RDDs)
    • Implement an application using the key Spark concepts
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about our training

  • keywordicon1
    Real-World Content

    Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.

  • keywordicon2
    Expert Practitioners

    Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.

  • keywordicon3
    Experiential Learning

    More coding than lecture, coupled with architectural and design discussions.

  • keywordicon4
    Fully Customized

    One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Introduction to Apache Spark training course, you'll learn:

  • Spark Basics
    • What is Apache Spark?
    • Using the Spark Shell
    • Resilient Distributed Datasets (RDDs)
    • Functional Programming with Spark
  • The Hadoop Distributed File (HDFS) System
    • Why HDFS?
    • HDFS Architecture
    • Using HDFS
  • Spark and Hadoop
    • Spark and the Hadoop Ecosystem
    • Spark and MapReduce
  • RDDs
    • RDD Operations
    • KeyValue Pair RDDs
    • MapReduce and Pair RDD Operations
  • Running Spark on a Cluster
    • Standalone Cluster
    • The Spark Standalone Web UI
  • Parallel Programming with Spark
    • RDD Partitions and HDFS Data Locality
    • Working With Partitions
    • Executing Parallel Operations
  • Caching and Persistence
    • Distributed Persistence
    • Caches
  • Writing Spark Applications
    • SparkContext
    • Spark Properties
    • Building and Running a Spark Application
    • Logging
  • Spark Streaming
    • Streaming Overview
    • Sliding Window Operations
    • Spark Streaming Applications
Real-world content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.

Expert Practitioners

Industry experts that bring their battle scars into the classroom.

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.

Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

Elite Instructor Program

We recently launched our internal Elite Instructor Program. The community driven instructor program is designed to support instructors in transforming students’ lives by consistently showing a world-class level of engagement, ability, and teaching prowess. Reach out today to learn more about our instructors.

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“I appreciated the instructor’s technique of writing live code examples rather than using fixed slide decks to present the material.”

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