Apache Spark with Java - Learn Spark from a Big Data Guru [Video]

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Apache Spark with Java - Learn Spark from a Big Data Guru [Video]

James Lee , Tao W

Monday, April 9, 2018

New Release!

Learn to analyze large data sets with Apache Spark by 10+ hands-on examples. Take your big data skills to the next level.

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Video Details

ISBN 139781788994330

Course Length3 hours and 20 minutes

Video Description

This course covers all the fundamentals of Apache Spark with Java and teaches you everything you need to know about developing Spark applications with Java. At the end of this course, you will have gained an in-depth knowledge pf Apache Spark, general big data analysis and manipulations skills. With these new skills you'll be able to help your company to adapt Apache Spark for building a big data processing pipeline and data analytics applications. This course covers 10+ hands-on big data examples. You will learn valuable knowledge on how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache web logs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom, and much more.

Style and Approach

This course is very hands-on, James has put in lots of effort to provide you with not only the theory but also real-life examples of developing Spark applications that you can try out on your own laptop.

Get Started with Apache Spark

The Course Overview

Introduction to Spark

Install Java and Git

Set up Spark project with IntelliJ IDEA

Set up Spark project with Eclipse

Run our first Spark job

RDD

RDD Basics

Create RDDs

Map and Filter Transformation

Solution to Airports by Latitude Problem

FlatMap Transformation

Set Operation

Actions

Solution to Sum of Numbers Problem

Important Aspects about RDD

Summary of RDD Operations

Caching and Persistence

Spark Architecture and Components

Spark Architecture

Spark Components

Pair RDD

Introduction to Pair RDD

Create Pair RDDs

Filter and MapValue Transformations on Pair RDD

Reduce By Key Aggregation

Sample solution for the Average House problem

Group by Key Transformation

Sort by Key Transformation

Sample Solution for the Sorted Word Count Problem

Data Partitioning

Join Operations

Advanced Spark Topic

Accumulators

Solution to StackOverflow Survey Follow-up Problem

Broadcast Variables

Spark SQL

Introduction to Spark SQL

Spark SQL in Action

Spark SQL practice: House Price Problem

Spark SQL Joins

Strongly Typed Dataset

Use Dataset or RDD

Dataset and RDD Conversion

Performance Tuning of Spark SQL

Running Spark in a Cluster

Introduction to Running Spark in a Cluster

Package Spark Application and Use spark-submit

Run Spark Application on Amazon EMR (Elastic MapReduce) cluster

What You Will Learn

An overview of the architecture of Apache Spark.
Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large datasets.
Develop Apache Spark 2.0 applications using RDD transformations and actions, and Spark SQL.
Scale up Spark applications on a Hadoop YARN cluster through Amazon's Elastic MapReduce service.
Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Spark SQL.
Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators.
Learn advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
Learn best practices for working with Apache Spark in the field.

Authors

James Lee

James Lee is a passionate software wizard working at one of the top Silicon Valley-based startups specializing in big data analysis. In the past, he has worked at big companies such as Google and Amazon. In his day job, he works with big data technologies such as Cassandra and ElasticSearch, and he is an absolute Docker technology geek and IntelliJ IDEA lover with a strong focus on efficiency and simplicity. Apart from his career as a software engineer, he is keen on sharing his knowledge with others and guiding them, especially for startups and programming. He has been teaching courses and conducting workshops on Java programming / IntelliJ IDEA since he was 21. James Lee has an MS degree in Computer Science from McGill University and many years of experience as a teaching assistant for various computer science classes. James Lee also enjoys skiing and swimming, and he is a passionate traveler.

Tao W

Tao W is a passionate software engineer who works in a leading big data analysis company in Silicon Valley. Previously, Tao has worked in big IT companies such as IBM and Cisco. Tao has an MS degree in Computer Science from the University of McGill and many years of experience as a teaching assistant for various computer science classes. When Tao is not working, Tao enjoys reading and swimming, and he is a passionate photographer.

Get Started with Apache Spark

The Course Overview

Introduction to Spark

Install Java and Git

Set up Spark project with IntelliJ IDEA

Set up Spark project with Eclipse

Run our first Spark job

RDD

RDD Basics

Create RDDs

Map and Filter Transformation

Solution to Airports by Latitude Problem