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Hadoop Developer: Spark 2.x

Overview

This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. It is based on the Spark 2.x release. The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g. RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface.It includes in-depth coverage of Spark SQL, DataFrames, and DataSets, which are now the preferred programming API. This includes exploring possible performance issues and strategies for optimization. The course also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and integrating with the Kafka server

Prerequisites

Students should be familiar with programming principles and have previous experience in software
development using Scala. Previous experience with data streaming, SQL, and HDP is also helpful, but not
required.

Target Audience

Software engineers that are looking to develop in-memory applications for time sensitive and highly iterative
applications in an Enterprise HDP environment.

1
Day

Scala Ramp Up, Introduction to Spark

Objectives

  • Scala Introduction
  • Working with: Variables, Data Types, and Control Flow
  • The Scala Interpreter
  • Collections and their Standard Methods (e.g. map())
  • Working with: Functions, Methods, and Function Literals
  • Define the Following as they Relate to Scale: Class, Object, and Case Class
  • Overview, Motivations, Spark Systems
  • Spark Ecosystem
  • Spark vs. Hadoop
  • Acquiring and Installing Spark
  • The Spark Shell, SparkContext

Labs

  • Setting Up the Lab Environment
  • Starting the Scala Interpreter
  • A First Look at Spark
  • A First Look at the Spark Shell

RDDs and Spark Architecture, Spark SQL, DataFrames and DataSets

Shuffling, Transformations and Performance, Performance Tuning

Creating Standalone Applications and Spark Streaming

Live Training

Live Training Self Paced Blended
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