Apache Spark and Scala

Course Curriculum

Chapter 1: Module-1 Introduction to Big data, Hadoop and Spark

Lecture 1: 1.1 Overview of Big Data

Lecture 2: 1.2 Introduction to Apache Hadoop

Lecture 3: 1.3 Hadoop Distributed File System

Lecture 4: 1.4 Hadoop Map Reduce

Lecture 5: 1.5 Introduction to Apache Spark

Lecture 6: 1.6 Characteristics of Apache Spark

Lecture 7: 1.7 Users and Use Cases of Apache Spark

Lecture 8: 1.8 Job Execution Flow and Spark Execution

Lecture 9: 1.9 Spark Unified Stack

Lecture 10: 1.10 Complete Picture of Apache Spark

Lecture 11: 1.11 Why Spark with Scala

Lecture 12: 1.12 Apache spark Architecture

Chapter 2: Module 2: Introduction to Scala Programming Language

Lecture 1: 2.1 Introduction to Scala

Lecture 2: 2.2 Scala Basic Syntax

Lecture 3: 2.3 Scala Class and Objects

Lecture 4: 2.4 If else Statements in Scala

Lecture 5: 2.5 Loops in Scala

Chapter 3: Module 3: Advanced Scala Programming

Lecture 1: 3.1 Functions and Procedures in Scala

Lecture 2: 3.2 Access Modifiers

Lecture 3: 3.3 Strings and Arrays

Lecture 4: 3.4 Scala Collections

Lecture 5: 3.5 Scala Traits

Lecture 6: 3.6 Pattern Matching

Lecture 7: 3.7 Scala Extractors

Lecture 8: 3.8 Scala Exception Handling

Lecture 9: 3.9 Scala Files IO

Chapter 4: Module 4: Apache Spark RDDs

Lecture 1: 4.1 Programming with RDDs

Lecture 2: 4.2 Starting with Spark

Lecture 3: 4.3 Creating RDDs

Lecture 4: 4.4 RDD Operations

Lecture 5: 4.5 Lifecycle of Spark

Chapter 5: Module 5: Apache Spark RDDs II

Lecture 1: 5.1 Spark Caching

Lecture 2: 5.2 Common Transformations and Actions

Lecture 3: 5.3 Spark Functions

Lecture 4: 5.4 Some more Spark functions

Chapter 6: Module 6: Working with Key-Value pairs

Lecture 1: 6.1 Key Value Pairs

Lecture 2: 6.2 Aggregate Functions

Lecture 3: 6.3 Working with Aggregate Functions

Lecture 4: 6.4 Joins in Spark

Lecture 5: 6.5 Practical on Word count example

Chapter 7: Module 7: Advanced Spark Programming

Lecture 1: 7.1 Spark Shared Variables

Lecture 2: 7.2 Spark and Fault Tolerance

Lecture 3: 7.3 Broadcast variables

Lecture 4: 7.4 Numeric RDD Operations

Lecture 5: 7.5 Per-Partition Operations

Chapter 8: Module 8: Running Spark jobs on Cluster

Lecture 1: 8.1 Spark Runtime Architecture

Lecture 2: 8.2 Spark Driver

Lecture 3: 8.3 Executors

Lecture 4: 8.4 Cluster Managers

Lecture 5: 8.5 Cluster Managers II

Chapter 9: Module 9: Spark SQL

Lecture 1: 9.1 Introduction to Spark SQL

Lecture 2: 9.2 Starting Point-SQL Context

Lecture 3: 9.3 Hive with Spark SQL

Lecture 4: 9.4 Spark SQL Caching

Chapter 10: Module 10: Spark Streaming

Lecture 1: People.json, Employee.json

Chapter 11: Module 11: Machine Learning in Spark

Lecture 1: 11.1 machine learning with mllib

Lecture 2: 11.2 MLib Data Types

Lecture 3: 11.3 labeled point data types

Lecture 4: 11.4 Local Matrices in mllib

Lecture 5: 11.5 MLib Algorithms

Lecture 6: 11.6 Classification and Regression

Lecture 7: 11.7 Clustering

Chapter 12: Module 12: GraphX in Spark

Lecture 1: 12.1 GraphX Introduction

Lecture 2: 12.2 Creating Graphs

Lecture 3: 12.3 Graph Operators

Lecture 4: 12.4 Subgraph Transformation

Lecture 5: 12.5 Computation with map reduce triplets