Spark for Developers Training Course

Course Code

sparkdev

Duration

21 hours (usually 3 days including breaks)

Requirements

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)

Overview

OBJECTIVE:

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.  The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.

AUDIENCE :

Developers / Data Analysts

Course 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. MLlib
    • MLlib intro
    • MLlib 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

Testimonials

Richard is very calm and methodical, with an analytical insight - exactly the qualities needed to present this sort of course

Kieran Mac Kenna - BAE Systems Applied Intelligence

We know know a lot more about the whole environment

John Kidd - Cardano Risk Management

The trainer made the class interesting and entertaining which helps quite a bit with all day trainings

Ryan Speelman -

I think the trainer had an excellent style of combining humor and real life stories to make the subjects at hand very approachable. I would highly recommend this professor in the future.

I think the trainer had an excellent style of combining humor and real life stories to make the subjects at hand very approachable. I would highly recommend this professor in the future.

Ernesto did a great job explaining the high level concepts of using Spark and it's various modules.

Michael Nemerouf -

Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £3750

Private Remote

From £3300 (98)

Public Classroom

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