Spark for Beginners

Apache Spark is an open source, scalable, massively parallel, in-memory execution environment for running analytics applications. Think of it as an in-memory layer that sits above multiple data stores, where data can be loaded into memory and analyzed in parallel across a cluster.

SKU: BD-0010 Category: Big Data Tag: Big Data

Description

Much like MapReduce, Spark works to distribute data across a cluster, and process that data in parallel. The difference is, unlike MapReduce—which shuffles files around on disk—Spark works in memory, making it much faster at processing data than MapReduce.

Why We Learn Spark:

To have an increase in their access to Big Data, to make use of existing Big Data investments, pace up with growing Enterprise Adoption and because over all, there is has recently been an increase in demand for Spark Developers which equals an increase in pay.

Course Audience:

Developers who wish to explore Data Science and Big Data

Course Duration: 4 days

Tools Needed:

The Sylabus

Introduction to the Course

What you’ll need
Completion targets

Scala and Spark Overview

History of Spark and Scala
Recent changes to Scala

Scala IDE Options and Overview

ScalaIDE overview
Computer set-up time!

Scala and Spark Set-up and Installation

Windows introduction
Windows Scala and Spark installation
Atom Windows installation
Linux and Mac installation

Scala Programming: Level One

Arithmetic and Numbers
Values and Variables
Booleans and Comparison Operators
Strings and Basic Regex
Tuples
Scala Basics – Assessment Test Exercises
Scala Basics Assessment Test Questions
Scala Basics – Assessment Test Solutions

Collections

Intro to Collections
Lists
Arrays
Scala lists vs Arrays
Sets
Maps

Scala Programming: Level Two

Flow Control
For Loops
While Loops
Functions

Introduction to Machine Learning

Machine Learning with Spark
IntelliJ IDEA Installation Overview

Spark DataFrames with Scala

Introduction to Spark DataFrames
DataFrames Overview
Spark DataFrame Operations
GroupBy and Aggregate Functions
Missing data
Date and Timestamps

Regression with Spark

Introduction to Linear Regression
Introduction to Regression Section
Linear Regression Documentation Example
Alternate Linear Regression Data CSV File
csv
csv
Linear Regression Walkthrough Part 1
Linear Regression Walkthrough Part 2

Classification with Spark

Introduction to Classification
Classification Documentation Example
Spark Classification – Logistic Regression Example – Part 1
Spark Classification – Logistic Regression Example – Part 2

Model Evaluation

Model Evaluation Overview
Spark Model Evaluation – Documentation Example
Spark – Model Evaluation – Regression Example

Clustering with Spark

Introduction to Clustering with Spark
KMeans Theory Lecture
Example of KMeans with Spark

PCA with Spark

PCA Theory Overview
PCA with Spark – Project Exercise
PCA with Spark – Documentation Example

DataBricks and Spark

Databricks Overview
Introduction to Spark Recommendation Systems
Spark Recommender System Implementation
Zeppelin Notebooks on AWS Elastic MapReduce

Additional information

Course Duration	4 Day
Lab Count	12 Labs
Location	On-site

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