Cloudera Developer Training for Apache Spark

Cloudera University’s three-day training course for Apache Spark enables participants to build complete, unified Big Data applications combining batch, streaming, and interactive analytics on all their data. With Spark, developers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.

Skills Gained

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • Using the Spark shell for interactive data analysis

  • The features of Spark’s Resilient Distributed Datasets

  • How Spark runs on a cluster

  • Parallel programming with Spark

  • Writing Spark applications

  • Processing streaming data with Spark

Course Outline


  • Spark Basics


    • What is Apache Spark?

    • Using the Spark Shell

    • Resilient Distributed Datasets (RDDs)

    • Functional Programming with Spark



  • Working with RDDs


    • RDD Operations

    • Key-Value Pair RDDs

    • MapReduce and Pair RDD Operations



  • The Hadoop Distributed File System


    • Why HDFS?

    • HDFS Architecture

    • Using HDFS



  • Running Spark on a Cluster


    • Overview

    • A Spark 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


    • RDD Lineage

    • Caching Overview

    • Distributed Persistence



  • Writing Spark Applications


    • Spark Applications vs. Spark Shell

    • Creating the SparkContext

    • Configuring Spark Properties

    • Building and Running a Spark Application

    • Logging



  • Spark, Hadoop, and the Enterprise Data Center


    • Overview

    • Spark and the Hadoop Ecosystem

    • Spark and MapReduce



  • Spark Streaming


    • Spark Streaming Overview

    • Example: Streaming Word Count

    • Other Streaming Operations

    • Sliding Window Operations

    • Developing Spark Streaming Applications



  • Common Spark Algorithms


    • Iterative Algorithms

    • Graph Analysis

    • Machine Learning



  • Improving Spark Performance


    • Shared Variables: Broadcast Variables

    • Shared Variables: Accumulators

    • Common Performance Issues



Audience

This course is best suited to developers and engineers. Course examples and exercises are presented in Python and Scala, so knowledge of one of these programming languages is required. Basic knowledge of Linux is assumed. Prior knowledge of Hadoop is not required.