Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru

Focused View

Tao W.,James Lee,Level Up,Jiarui Zhou

3:24:10

8 View

1 - Get Started with Apache Spark

1 - Course Overview.mp4

04:08

2 - How to Take this Course and How to Get Support.mp4

01:24

3 - Text Lecture How to Take this Course and How to Get Support.html

4 - Introduction to Spark.mp4

02:21

5 - Apache-Spark-with-Java-Slides.pdf

5 - Sides.html

6 - Java 9 Warning.html

7 - Install Java and Git.mp4

04:11

8 - Source Code.html

9 - Set up Spark project with IntelliJ IDEA.mp4

07:22

10 - Set up Spark project with Eclipse.mp4

02:04

11 - Text lecture Set up Spark project with Eclipse.html

12 - Run our first Spark job.mp4

02:43

13 - Trouble shooting running Hadoop on Windows.html

2 - RDD

14 - RDD Basics.mp4

02:40

15 - Create RDDs.mp4

02:26

16 - Text Lecture Create RDDs.html

17 - Map and Filter Transformation.mp4

08:38

18 - Solution to Airports by Latitude Problem.mp4

01:31

19 - FlatMap Transformation.mp4

06:27

20 - Text Lectures flatMap Transformation.html

21 - Set Operation.mp4

07:37

22 - Sampling With Replacement and Sampling Without Replacement.html

23 - Solution for the Same Hosts Problem.html

24 - Actions.mp4

08:06

25 - Solution to Sum of Numbers Problem.mp4

01:43

26 - Important Aspects about RDD.mp4

01:28

27 - Summary of RDD Operations.mp4

02:23

28 - Caching and Persistence.mp4

05:09

3 - Spark Architecture and Components

29 - Spark Architecture.mp4

02:55

30 - Spark Components.mp4

05:20

4 - Pair RDD

31 - Introduction to Pair RDD.mp4

01:32

32 - Create Pair RDDs.mp4

03:54

33 - Filter and MapValue Transformations on Pair RDD.mp4

04:52

34 - Reduce By Key Aggregation.mp4

05:14

35 - Sample solution for the Average House problem.mp4

03:16

36 - Group By Key Transformation.mp4

04:43

37 - Sort By Key Transformation.mp4

02:49

38 - Sample Solution for the Sorted Word Count Problem.mp4

02:00

39 - Data Partitioning.mp4

04:12

40 - Join Operations.mp4

04:56

41 - Extra Learning Material How are Big Companies using Apache Spark.html

5 - Advanced Spark Topic

42 - Accumulators.mp4

05:30

43 - Text Lecture Accumulators.html

44 - Solution to StackOverflow Survey Followup Problem.mp4

01:20

45 - Broadcast Variables.mp4

06:48

6 - Spark SQL

46 - Introduction to Spark SQL.mp4

03:48

47 - Spark SQL in Action.mp4

14:43

48 - Spark SQL practice House Price Problem.mp4

01:52

49 - Spark SQL Joins.mp4

06:21

50 - Strongly Typed Dataset.mp4

08:31

51 - Use Dataset or RDD.mp4

02:56

52 - Dataset and RDD Conversion.mp4

02:58

53 - Performance Tuning of Spark SQL.mp4

02:44

54 - Extra Learning Material Avoid These Mistakes While Writing Apache Spark Program.html

7 - Running Spark in a Cluster

55 - Introduction to Running Spark in a Cluster.mp4

04:09

56 - Package Spark Application and Use sparksubmit.mp4

08:08

57 - Run Spark Application on Amazon EMR Elastic MapReduce cluster.mp4

13:32

8 - Additional Learning Materials

58 - Future Learning.mp4

02:46

59 - Text Lecture Future Learning.html

60 - Coupons to Our Other Courses.html

Description

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

What You'll 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.

Who is this for?

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.

What You Need to Know?

A computer running Windows, OSX or Linux

Previous Java programming skills

Java 8 experience is preferred but NOT required

More details

Description
What is this course about:

This course covers all the fundamentals about Apache Spark with JavaÂ and teaches you everything you need to know about developingÂ Spark applications with Java. AtÂ 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. TogetherÂ we will learn examples such asÂ aggregatingÂ NASA Apache web logs from different sources; we will explore the price trend by looking at theÂ real estate data in California; we will write Spark applications to find out the median salary ofÂ developers in different countries throughÂ the Stack OverflowÂ survey data; we will develop a system to analyzeÂ how maker spaces are distributed across different regions in theÂ United Kingdom. Â And much much more.

What willÂ you learn from this lecture:

In particularly, you will learn:

An overview of the architecture of Apache Spark.
Develop Apache Spark 2.0 applications with JavaÂ 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 forÂ big data.

Tons of companies are adapting Apache Spark to extract meaning from massive data sets, today you haveÂ 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 choosingÂ 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.

Â 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 analysisÂ skills and career to the next level, take this course now!

You will go fromÂ zero to Spark heroÂ in 4 hours.
Who this course is for:
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.

User Reviews

Rating

average 0

Total votes0

Focused display

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 Tao.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

Instructor's Courses

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

Instructor's Courses

Skilled programmers remain in high demand in this digitally-focused world. Level-up offers practical and engaging learning solution that is revolutionizing professional online training. Level-up provides courses delivered by top industry experts and well-designed real-life course projects We teach technology the way it is used in the industry world. 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 The Level-up Udemy courses are your gateway to high-quality software courses from industry experts and influencers.

Jiarui Zhou

Instructor's Courses

Jiarui Zhou, a 17-year-old student from Abbey Park high school, is passionate about Computer Science and Computer Engineering. With an advanced proficiency in Python and a solid grasp of other languages, including Java, Jiarui embodies a blend of youthful enthusiasm and technical expertise. His academic journey reflects a deep commitment to exploring the ever-evolving landscape of technology and a keen interest in applying his skills in real-world scenarios.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.