# Apache Spark 3 with Scala: Hands On with Big Data!

#### Frank Kane, Founder of Sundog Education, ex-Amazon

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53 Lessons (7h 24m)
• 1. Introduction, and Getting Set Up

16:19
• 2. [Activity] Create a Histogram of Real Movie Ratings with Spark!

14:39

0:16
• 4. [Activity] Scala Basics, Part 1

12:52
• 5. [Exercise] Scala Basics, Part 2

9:41
• 6. [Exercise] Flow Control in Scala

7:18
• 7. [Exercise] Functions in Scala

8:47
• 8. [Exercise] Data Structures in Scala

16:38
• 9. Introduction to Spark

8:40
• 10. The Resilient Distributed Dataset

11:04
• 11. Ratings Histogram Walkthrough

7:33
• 12. Spark Internals

4:42
• 13. Key / Value RDD's, and the Average Friends by Age example

12:21
• 14. [Activity] Running the Average Friends by Age Example

7:58
• 15. Filtering RDD's, and the Minimum Temperature by Location Example

6:43
• 16. [Activity] Running the Minimum Temperature Example, and Modifying it for Maximum

10:10
• 17. [Activity] Counting Word Occurrences using Flatmap()

8:59
• 18. [Activity] Improving the Word Count Script with Regular Expressions

6:41
• 19. [Activity] Sorting the Word Count Results

8:10
• 20. [Exercise] Find the Total Amount Spent by Customer

3:37
• 21. [Exercise] Check your Results, and Sort Them by Total Amount Spent

4:26
• 22. Check Your Results and Implementation Against Mine

3:26
• 23. [Activity] Find the Most Popular Movie

4:29
• 24. [Activity] Use Broadcast Variables to Display Movie Names

8:52
• 25. [Activity] Find the Most Popular Superhero in a Social Graph

14:10
• 26. Superhero Degrees of Separation: Introducing Breadth-First Search

6:52
• 27. Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark

5:53
• 28. Superhero Degrees of Separation: Review the code, and run it!

10:41
• 29. Item-Based Collaborative Filtering in Spark, cache(), and persist()

8:16
• 30. [Activity] Running the Similar Movies Script using Spark's Cluster Manager

14:13
• 31. [Exercise] Improve the Quality of Similar Movies

2:41
• 32. [Activity] Using spark-submit to run Spark driver scripts

6:58
• 33. [Activity] Packaging driver scripts with SBT

13:14
• 34. Introducing Amazon Elastic MapReduce

7:11
• 35. Creating Similar Movies from One Million Ratings on EMR

11:33
• 36. Partitioning

5:07
• 37. Best Practices for Running on a Cluster

5:31
• 38. Troubleshooting, and Managing Dependencies

9:08
• 39. Introduction to SparkSQL

7:08
• 40. [Activity] Using SparkSQL

7:00
• 41. [Activity] Using DataFrames and DataSets

6:38
• 42. [Activity] Using DataSets instead of RDD's

7:23
• 43. Introducing MLLib

9:18
• 44. [Activity] Using MLLib to Produce Movie Recommendations

14:35
• 45. [Activity] Linear Regression with MLLib

5:55
• 46. [Activity] Using DataFrames with MLLib

8:30
• 47. Spark Streaming Overview

9:53
• 48. [Activity] Set up a Twitter Developer Account, and Stream Tweets

12:44
• 49. Structured Streaming

4:17
• 50. GraphX, Pregel, and Breadth-First-Search with Pregel.

10:38
• 51. [Activity] Superhero Degrees of Separation using GraphX

8:59
• 52. Learning More, and Career Tips

4:15
• 53. Let's Stay in Touch

0:46

New! Updated for Spark 3.0!

“Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including AmazonEBayNASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think, and you'll be learning from an ex-engineer and senior manager from Amazon and IMDb.

Spark works best when using the Scala programming language, and this course includes a crash-course in Scala to get you up to speed quickly. For those more familiar with Python however, a Python version of this class is also available: "Taming Big Data with Apache Spark and Python - Hands On".

Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples, and then scale them up to run on cloud computing services in this course.

• Learn the concepts of Spark's Resilient Distributed Datastores

• Get a crash course in the Scala programming language

• Develop and run Spark jobs quickly using Scala

• Translate complex analysis problems into iterative or multi-stage Spark scripts

• Scale up to larger data sets using Amazon's Elastic MapReduce service

• Understand how Hadoop YARN distributes Spark across computing clusters

• Practice using other Spark technologies, like Spark SQL, DataFrames, DataSets, Spark Streaming, and GraphX

By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes.

We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to SpiderMan? You'll find the answer.

This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. 7.5 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX.

Enroll now, and enjoy the course!

"I studied Spark for the first time using Frank's course "Apache Spark 2 with Scala - Hands On with Big Data!". It was a great starting point for me,  gaining knowledge in Scala and most importantly practical examples of Spark applications. It gave me an understanding of all the relevant Spark core concepts,  RDDs, Dataframes & Datasets, Spark Streaming, AWS EMR. Within a few months of completion, I used the knowledge gained from the course to propose in my current company to  work primarily on Spark applications. Since then I have continued to work with Spark. I would highly recommend any of Franks courses as he simplifies concepts well and his teaching manner is easy to follow and continue with!  " - Joey Faherty