Introduction to Apache Spark for Developers and Engineers

Introduction to Apache Spark for Developers and Engineers, available at $19.99, has an average rating of 4.05, with 55 lectures, 13 quizzes, based on 123 reviews, and has 621 subscribers.

You will learn about Identify and understand the concepts of Big Data Clearly describe Apache Spark Understand and explain the various components of the Spark framework Differentiate between Spark and Hadoop MapReduce Download, install and use Spark on a local machine Identify and understand the main Scala programming language concepts Develop basic Spark applications Explain and use Spark Resilient Distributed Datasets This course is ideal for individuals who are Big Data Developers or Data Engineers or Big Data Consultants or Data Scientists It is particularly useful for Big Data Developers or Data Engineers or Big Data Consultants or Data Scientists.

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Summary

Title: Introduction to Apache Spark for Developers and Engineers

Price: $19.99

Average Rating: 4.05

Number of Lectures: 55

Number of Quizzes: 13

Number of Published Lectures: 55

Number of Published Quizzes: 13

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 68

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Identify and understand the concepts of Big Data
Clearly describe Apache Spark
Understand and explain the various components of the Spark framework
Differentiate between Spark and Hadoop MapReduce
Download, install and use Spark on a local machine
Identify and understand the main Scala programming language concepts
Develop basic Spark applications
Explain and use Spark Resilient Distributed Datasets

Who Should Attend

Big Data Developers
Data Engineers
Big Data Consultants
Data Scientists

Target Audiences

Big Data Developers
Data Engineers
Big Data Consultants
Data Scientists

What is Apache Spark?

Apache Spark is the next generation open source Big Data processing engine. Spark is designed to provide fast processing of large datasets and high performance for a wide range of applications. Spark enables in-memory cluster computing which greatly improves the speed of iterative algorithms and interactive data mining tasks.

Course Outcomes

‘Introduction to Apache Spark’ includes illuminating video lectures, practical hands-on Scala and Spark exercises, a guide to local installation of Spark, and quizzes. In this course, we guide students through:

An explanation of the Spark framework
The basics of programming in Scala, Spark’s native language
An outline of how to work with Spark’s primary abstraction, resilient distributed datasets (RDDs).

Upon completion of the course, students will be able to explain core concepts relating to Spark, understand the fundamentals of coding in Scala, and execute basic programming and data manipulation in Spark. This course will take approximately 8 hours to complete.

Recommended Experience

Programming Languages recommended for this course:

Scala (course exercises are in Scala)
Java
Python

Recommended for:

Data scientists and engineers
Developers
Individuals with a basic understanding of: Apache Hadoop, Big Data, programming languages (Scala, Java, or Python)

For students unfamiliar with Big Data and Hadoop, the course will provide a brief overview of each topic.

Why Adastra Academy?

Adastra Academy is a leading source of training and development for Information Management professionals and individuals interested in Data Management and Analytics technology. Our dedication to identifying and mastering emerging technologies guarantees our students are the first to have access to these quality courses. For an exceptional learning experience, our programs include hands-on labs and real world examples allowing students to easily apply their new knowledge.

Course Curriculum

Chapter 1: Overview of Big Data

Lecture 1: 1.1 Section 1 Introduction and topics

Lecture 2: 1.2 Overview of Big Data and Hadoop

Lecture 3: 1.3 Big Data Features and Traditional Datawarehousing Charactaristics

Lecture 4: 1.4 Use Case: Adastra's Big Data Reference Architecture

Lecture 5: 1.5 Section Conclusion

Chapter 2: What is Apache Spark

Lecture 1: 2.1 Introduction and topcis

Lecture 2: 2.2 Apache Spark Overview

Lecture 3: 2.3 Spark's History

Lecture 4: 2.4 Why Use Spark

Lecture 5: 2.5 Section Conclusion

Chapter 3: Spark Infrastructure

Lecture 1: 3.1 Introduction and Topics

Lecture 2: 3.2 Spark Deployment Modes

Lecture 3: 3.3 Hands-on Exercise: Installing Stand-Alone Spark

Lecture 4: 3.4 Hands-on Exercise: Install Stand-Alone Spark on your computer

Lecture 5: 3.6 The Spark Framework

Lecture 6: 3.7 Spark Application Concepts

Lecture 7: 3.8 Section Conclusion

Chapter 4: The Scala Programming Language

Lecture 1: 4.1 Introduction and topics

Lecture 2: 4.2 Scala Introduction & Language Features

Lecture 3: 4.3 Scala Language Basics-Base Types

Lecture 4: 4.4 Hands-on Examples: Scala Base Types

Lecture 5: 4.5 Scala Language Basics-Operators

Lecture 6: 4.6 Hands-on Examples: Scala Operators

Lecture 7: 4.7 Scala Language Constructs-Variables

Lecture 8: 4.8 Hands-on Examples: Scala Variables

Lecture 9: 4.10 Scala Language Constructs-Arrays

Lecture 10: 4.11 Hands-on Examples: Scala Arrays

Lecture 11: 4.12 Scala Language Constructs-Lists

Lecture 12: 4.13 Hands-On Exercise: Scala Lists

Lecture 13: 4.14 Scala Language Constructs-Collections

Lecture 14: 4.16 Scala Language Constructs-IF Expressions

Lecture 15: 4.17 Hands-On Excercise: Scala IF Expressions

Lecture 16: 4.18 Scala Language Constructs-MATCH-CASE Expressions

Lecture 17: 4.19 Hands-On Excercise: Scala MATCH-CASE Expressions

Lecture 18: 4.20 Scala Language Constructs-WHILE & FOR Loop Expressions

Lecture 19: 4.21 Hands-On Excercise: Scala WHILE & FOR Loop Expressions

Lecture 20: 4.23 Scala Language Basics-Functions

Lecture 21: 4.24 Hands-On Excercise: Scala Functions

Lecture 22: 4.26 Scala Language Basics-Anonymous Functions

Lecture 23: 4.27 Hands-on Examples: Anonymous Functions

Lecture 24: 4.29 Scala Functions – quiz solution

Lecture 25: 4.30 Section Conclusion

Chapter 5: Resilient Distributed Datasets

Lecture 1: 5.1 Introduction and sections

Lecture 2: 5.2 Resilient Distributed Datasets-Overview

Lecture 3: 5.3 Resilient Distributed Datasets

Lecture 4: 5.4 Hands-On Exercise: RDDs Lazy Evaluation & Actions

Lecture 5: 5.6 Resilient Distributed Datasets-How to Create

Lecture 6: 5.7 Hands-On Exercise: Creating an RDD from a Collection

Lecture 7: 5.9 Pair Resilient Distributed Datasets

Lecture 8: 5.10 Hands-On Exercise: Pair RDDs

Lecture 9: 5.12 Resilient Distributed Datasets-Persistence

Lecture 10: 5.13 Resilient Distributed Datasets-Shared Variables

Lecture 11: 5.14 Hands-on Examples: Distributed Shared Variables

Lecture 12: 5.16 "Advanced" data processing with Spark – quiz solution

Lecture 13: 5.17 Section Conclusion

Instructors

Adastra Academy
Emerging Data Management and Analytics Technology Educators

Rating Distribution

1 stars: 3 votes
2 stars: 6 votes
3 stars: 30 votes
4 stars: 41 votes
5 stars: 43 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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