Section 1: SCALA –
Introduction to functional programming

 Getting started with
SCALA

 SCALA Basics

 REPL

 Variables

 Expressions

 Functions

 Run a sample program

 Type lattices and
inferences

 How to access Spark
Databox cloud lab?

 Step by Step
instruction to access cloud Big data Lab.

Section 2: SCALA
environment setup

 Windows and UNIX
setup

Section 3: Functional
Programming

 Functional
programming – introduction

 Sample program

 OOPS – concepts

 FPP – concepts

 Difference between
OOPS and FPP

Section 4:
Collections

 Mapping

 Iterations

 Filtering

 Counting

 Regular expressions

 Maps, Sets, group By,
Options, flatten, flat Map

 Word count, IO
operations, file access, flatMap

Section 5:
Object-Oriented Programming

 Classes and
Properties

 Objects, Packaging,
and Imports

 Traits Objects,
classes, inheritance, Lists with multiple related types, apply

 Exceptions handling

 Exception keywords

Section 6:
Integrations

 SBT – introduction

 Integrating SCALA
with Eclipse IDE

 Integrating SBT with
Eclipse

Section 7: CORE
SPARKS

 Batch vs. real-time
data processing

 Sparks – introduction

 Spark versus Hadoop

 Spark – Architecture

 programming Spark
with Scala

 Creating Spark
context using Spark shell

 RDD programming

 Operations in RDD

 Transformations

 Actions

 Loading and saving
data

 RDD – key-value pair

 Broadcast variables

Section 8:
Persistence

 Configuring and
running Spark cluster

 Multi-Node Spark
Cluster

 Managing Spark
Cluster

 Running Spark jobs
and running in the cluster mode

 Spark application
development using eclipse

 Performance tuning
and debugging Spark application

Section 9: Spark
Streaming

 Spark streaming –
Introduction

 Spark Streaming –
Architecture

 Distributed Log Files
in Real-Time processing

 Discretized streams
RDD

 Transformations and
Actions on Streaming Data

 Flume and Kafka
integration

 Stream jobs
integration

Section 10: Spark SQL

 Apache Spark SQL –
Introduction

 SQL context

 Importing and saving
data

 Text files, JSON, and
Parquet Files processing

 DataFrames

 User-defined
functions

 What is HIVE? –
Introduction

 Local Hive Metastore
server

Section 11: Real-time
project

 Apache Spark project
environment setup

 Real-time Apache
Spark project

 Project demonstration

 Expert evaluation and
feedback

Section 12: You made
it!!

 Spark Databox Apache
Spark course certification

 Interview preparation

 Mock interviews

 Resume preparation

 Knowledge sharing
with industry experts

 Counseling to guide
you to the right path in Apache Spark development career

Apache Spark

Introduction to Apache Spark

Why Spark

Batch Vs. Real Time Big Data
Analytics

Batch Analytics – Hadoop Ecosystem
Overview

Real Time Analytics Options

Streaming Data – Storm

In Memory Data – Spark, What is
Spark?

Spark benefits to Professionals

Limitations of MR in Hadoop

Components of Spark

Spark Execution Architecture

Benefits of Apache Spark

Hadoop vs Spark

Introduction to Scala

Features of Scala

Basic Data Types of Scala

Val vs Var

Type Inference

REPL

Objects & Classes in Scala

Functions as Objects in Scala

Anonymous Functions in Scala

Higher Order Functions

Lists in Scala

Maps

Pattern Matching

Traits in Scala

Collections in Scala

Spark Core Architecture

Spark & Distributed Systems

Spark for Scalable Systems

Spark Execution Context

What is RDD

RDD Deep Dive

RDD Dependencies

RDD Lineage

Spark Application In Depth

Spark Deployment

Parallelism in Spark

Caching in Spark

Spark Internals

Spark Transformations

Spark Actions

Spark Cluster

Spark SQL Introduction

Spark Data Frames

Spark SQL with CSV

Spark SQL with JSON

Spark SQL with Database

Spark Streaming

Features of Spark Streaming

Micro Batch

Dstreams

Transformations on Dstreams

Spark Streaming Use Case 1

Spark Streaming Use Case 2

Spark Streaming Use Case 3

Spark GraphX Programming

Introduction to Graph Parallel
Systems

Introduction to GraphX

Features of GraphX

GraphX Deep Dive

Graph Builder

Introducing Mllib

Using Mllib for Movie
Recommendations

Analyzing Recommendation Results
using Spark