Apache Spark with Java - Mastering Big Data!
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Apache Spark with Java - Mastering Big Data!

Learn analyzing large data sets with Apache Spark by 10+ hands-on examples. Take your big data skills to the next level.
Best Seller
4.9 (417 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
6,639 students enrolled
Last updated 9/2017
English
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Current price: $10 Original price: $200 Discount: 95% off
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Includes:
  • 3.5 hours on-demand video
  • 8 Articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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 data sets.
  • 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 about Spark SQL.
  • Share information across different nodes on a Apache Spark cluster by broadcast variables and accumulators.
  • Advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
  • Best practices of working with Apache Spark in the field.
View Curriculum
Requirements
  • A computer running Windows, OSX or Linux
  • Previous Java programming skills
  • Java 8 experience is preferred but NOT required
Description

What is this course about:

This course covers all the fundamentals about Apache Spark and teaches you everything you need to know about developing Spark applications. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adapt Apache Spark for building big data processing pipeline and data analytics applications.

This course covers 10+ hands-on big data examples. You will learn valuable knowledge about 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 much more.

What will you learn from this lecture:

In particularly, you will learn:

  • An overview of the architecture of Apache Spark.

  • Develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL.

  • Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.

  • Deep dive into advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.

  • 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.
  • Best practices of working with Apache Spark in the field.

  • Big data ecosystem overview.

Why shall we learn Apache Spark:

Apache Spark gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world.

Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks.

Apache Spark is the next-generation processing engine for big data.

Tons of companies are adapting Apache Spark to extract meaning from massive data sets, today you have access to that same big data technology right on your desktop.

Apache Spark is becoming a must tool for big data engineers and data scientists.

About the author:

Since 2015, James has been helping his company to adapt Apache Spark for building their big data processing pipeline and data analytics applications.

James' company has gained massive benefits by adapting Apache Spark in production. In this course, he is going to share with you his years of knowledge and best practices of working with Spark in the real field.

Why choosing this course?

This course is very hands-on, James has put lots 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.

James has uploaded all the source code to Github and you will be able to follow along with either Windows, MAC OS or Linux.

In the end of this course, James is confident that you will gain in-depth knowledge about Spark and general big data analysis and data manipulation skills. You'll be able to develop Spark application that analyzes Gigabytes scale of data both on your laptop, and in the cloud using Amazon's Elastic MapReduce service!

30-day Money-back Guarantee!

You will get 30-day money-back guarantee from Udemy for this course.

 If not satisfied simply ask for a refund within 30 days. You will get a full refund. No questions whatsoever asked.

Are you ready to take your big data analysis skills and career to the next level, take this course now!

You will go from zero to Spark hero in 4 hours.

Who is the target audience?
  • Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field.
  • Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL.
  • Data scientists or data engineers who want to advance their career by improving their big data processing skills.
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Curriculum For This Course
50 Lectures
03:17:51
+
Get Started with Apache Spark
8 Lectures 23:25


Install Java and Git
04:11

Set up Spark project with IntelliJ IDEA
07:22

Set up Spark project with Eclipse
02:04

Text lecture: Set up Spark project with Eclipse
00:01


Trouble shooting: running Hadoop on Windows
00:34
+
RDD
15 Lectures 48:42

Create RDDs
02:26

Text Lecture: Create RDDs
00:03

Map and Filter Transformation
08:38

Solution to Airports by Latitude Problem
01:31

FlatMap Transformation
06:27

Text Lectures: flatMap Transformation
00:01

Set Operation
07:37

Sampling With Replacement and Sampling Without Replacement
00:02

Solution for the Same Hosts Problem
00:29

Actions
08:06

Solution to Sum of Numbers Problem
01:43

Important Aspects about RDD
01:28

Summary of RDD Operations
02:23

Caching and Persistence
05:09
+
Spark Architecture and Components
2 Lectures 08:15
Spark Architecture
02:55

Spark Components
05:20
+
Pair RDD
9 Lectures 33:32
Introduction to Pair RDD
01:32

Filter and MapValue Transformations on Pair RDD
04:52

Reduce By Key Aggregation
05:14

Sample solution for the Average House problem
03:16

Group By Key Transformation
04:42

Sort By Key Transformation
02:49

Sample Solution for the Sorted Word Count Problem
02:00

Data Partitioning
04:12

Join Operations
04:55
+
Advanced Spark Topic
4 Lectures 13:39
Accumulators
05:30

Text Lecture: Accumulators
00:01

Solution to StackOverflow Survey Follow-up Problem
01:20

Broadcast Variables
06:48
+
Spark SQL
8 Lectures 43:52
Introduction to Spark SQL
03:48

Spark SQL in Action
14:43

Spark SQL practice: House Price Problem
01:52

Spark SQL Joins
06:20

Strongly Typed Dataset
08:31

Use Dataset or RDD
02:56

Dataset and RDD Conversion
02:58

Performance Tuning of Spark SQL
02:44
+
Running Spark in a Cluster
3 Lectures 25:48
Introduction to Running Spark in a Cluster
04:09

Package Spark Application and Use spark-submit
08:08

Run Spark Application on Amazon EMR (Elastic MapReduce) cluster
13:31
+
Additional Learning Materials
1 Lecture 00:37
Coupons to My Other Courses
00:37
About the Instructor
Tao W.
4.6 Average rating
6,856 Reviews
40,108 Students
4 Courses
Software engineer

Tao 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 a MS degree in Computer Science from 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.

James Lee
4.6 Average rating
6,856 Reviews
40,108 Students
4 Courses
Silicon Valley Software Engineer

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 on 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 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.

He enjoys working with Udemy because here he can share all his field knowledge and secrets with a broader audience. He hopes students will definitely benefit from his years of experience. The students will be thrilled of association with James and Udemy. And we are also excited to have you on board.

James Lee has a 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.

Level Up
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3 Courses
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We offer a range of courses that teach you from the fundamentals of programming to advanced topics in the areas of Big Data and DevOps, Data Science and Apache Spark, etc

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