Apache Spark and Scala

Apache Spark and Scala, available at $39.99, has an average rating of 3.6, with 67 lectures, based on 144 reviews, and has 656 subscribers.

You will learn about Understand the limitations of Hadoop mapreduce and how Spark overcomes these limitations Gain expertise in Scala programming language and its characteristics Able to work with RDDs and create applications in Spark A thorough understanding about Spark SQL by using SQL queries in Spark This course is ideal for individuals who are Students who aspire to gain a deep understanding of Apache Spark or Professionals looking for a career in real time big data analytics or Big Data and Hadoop Developers who want to analyze data faster It is particularly useful for Students who aspire to gain a deep understanding of Apache Spark or Professionals looking for a career in real time big data analytics or Big Data and Hadoop Developers who want to analyze data faster.

Enroll now: Apache Spark and Scala

Summary

Title: Apache Spark and Scala

Price: $39.99

Average Rating: 3.6

Number of Lectures: 67

Number of Published Lectures: 67

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the limitations of Hadoop mapreduce and how Spark overcomes these limitations

Gain expertise in Scala programming language and its characteristics

Able to work with RDDs and create applications in Spark

A thorough understanding about Spark SQL by using SQL queries in Spark

Who Should Attend

Students who aspire to gain a deep understanding of Apache Spark

Professionals looking for a career in real time big data analytics

Big Data and Hadoop Developers who want to analyze data faster

Target Audiences

Students who aspire to gain a deep understanding of Apache Spark

Professionals looking for a career in real time big data analytics

Big Data and Hadoop Developers who want to analyze data faster

This course on Apache Spark and Scala aims at providing an advanced expertise in big data Hadoop ecosystem. This course will provide a standard skillset which helps one become a specialist on the top of Big data Hadoop developer.?

The course starts with a detailed description on limitations of mapreduce and how Spark can help overcome them.?Further it covers a deeper dive into the Scala programming language.

Moving on it covers Spark as a standalone cluster and an understanding of Resiliient?Distributed Datasets.

The course also covers concepts of Spark SQL using SQL queries through SQL context and Hive Queries through Hive context.

This course certainly provides material required for building a career path from Big data Hadoop developer to BIg data Hadoop architect.

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

Instructors

Insculpt Technologies
Engraving Intelligence

Rating Distribution

1 stars: 13 votes

2 stars: 22 votes

3 stars: 29 votes

4 stars: 40 votes

5 stars: 40 votes

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