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Spark for Developers

Upcoming Classes

Ideal for small teams and individuals

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Spark For Developers

Overview:

This course will introduce Apache Spark. The students will learn how  Spark fits  into the Big Data ecosystem, and how to use Spark for data analysis.

What You Will Learn

  • Spark Shell
  • Spark internals,
  • Spark APIs,
  • Spark SQL,
  • Spark streaming,
  • Spark MLLib
  • Spark Graphx

Audience :

Developers / Data Analysts

Duration :

3 days

Pre-requisites

  • familiarity with either Java / Scala / Python language (our labs in Scala and Python)
  • basic understanding of Linux development environment (command line navigation / editing files using VI or nano)

What to Bring:

 

Detailed Outline:

  1. Scala primer
    • A quick introduction to Scala
    • Labs : Getting know Scala
  2. Spark Basics
    • Background and history
    • Spark and Hadoop
    • Spark concepts and architecture
    • Spark eco system (core, spark sql, mlib, streaming)
    • Labs : Installing and running Spark
  3. First Look at Spark
    • Running Spark in local mode
    • Spark web UI
    • Spark shell
    • Analyzing dataset – part 1
    • Inspecting RDDs
    • Labs: Spark shell exploration
  4. RDDs
    • RDDs concepts
    • Partitions
    • RDD Operations / transformations
    • RDD types
    • Key-Value pair RDDs
    • MapReduce on RDD
    • Caching and persistence
    • Labs : creating & inspecting RDDs;   Caching RDDs
  5. Spark API programming
    • Introduction to Spark API / RDD API
    • Submitting the first program to Spark
    • Debugging / logging
    • Configuration properties
    • Labs : Programming in Spark API, Submitting jobs
  6. Spark SQL
    • SQL support in Spark
    • Dataframes
    • Defining tables and importing datasets
    • Querying data frames using SQL
    • Storage formats : JSON / Parquet
    • Labs : Creating and querying data frames; evaluating data formats
  7. Mlib
    • mlib intro
    • mlib algorithms
    • Labs : Writing mlib applications
  8. GraphX
    • GraphX library overview
    • GraphX APIs
    • Labs : Processing graph data using Spark
  9. Spark Streaming
    • Streaming overview
    • Evaluating Streaming platforms
    • Streaming operations
    • Sliding window operations
    • Labs : Writing spark streaming applications
  10. Spark and Hadoop
    • Hadoop Intro (HDFS / YARN)
    • Hadoop + Spark architecture
    • Running Spark on Hadoop YARN
    • Processing HDFS files using Spark
  11. Spark Performance and Tuning
    • Broadcast variables
    • Accumulators
    • Memory management & caching
  12. Spark Operations
    • Deploying Spark in production
    • Sample deployment templates
    • Configurations
    • Monitoring
    • Troubleshooting


Upcoming Trainings

  • Please select a session and register.
  • No payment necessary for registration.
  • Payment is due 5 days before the class to secure the spot.