English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 7.5 Hours | 7.40 MB
Dive right in with 20+ hands-on examples of analyzing large data sets with Apache Spark, on your desktop or on Hadoop!
New! Updated for Spark 2.3.
“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 Amazon, EBay, NASA 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.
What you’ll learn
- Frame big data analysis problems as Apache Spark scripts
- Develop distributed code using the Scala programming language
- Optimize Spark jobs through partitioning, caching, and other techniques
- Build, deploy, and run Spark scripts on Hadoop clusters
- Process continual streams of data with Spark Streaming
- Transform structured data using SparkSQL and DataFrames
- Traverse and analyze graph structures
1 Tip Apply for a Twitter Developer Account now!
2 Udemy 101 Getting the Most From This Course
3 Warning about Java 11 and Spark 2.4!
4 Introduction, and Getting Set Up
5 [Activity] Create a Histogram of Real Movie Ratings with Spark!
Scala Crash Course [Optional]
6 [Activity] Scala Basics, Part 1
7 [Exercise] Scala Basics, Part 2
8 [Exercise] Flow Control in Scala
9 [Exercise] Functions in Scala
10 [Exercise] Data Structures in Scala
Spark Basics and Simple Examples
11 Introduction to Spark
12 [Activity] Improving the Word Count Script with Regular Expressions
13 [Activity] Sorting the Word Count Results
14 [Exercise] Find the Total Amount Spent by Customer
15 [Exercise] Check your Results, and Sort Them by Total Amount Spent
16 Check Your Results and Implementation Against Mine
17 The Resilient Distributed Dataset
18 Ratings Histogram Walkthrough
19 Spark Internals
20 Key Value RDD’s, and the Average Friends by Age example
21 [Activity] Running the Average Friends by Age Example
22 Filtering RDD’s, and the Minimum Temperature by Location Example
23 [Activity] Running the Minimum Temperature Example, and Modifying it for Maximum
24 [Activity] Counting Word Occurrences using Flatmap()
Advanced Examples of Spark Programs
25 [Activity] Find the Most Popular Movie
26 [Activity] Use Broadcast Variables to Display Movie Names
27 [Activity] Find the Most Popular Superhero in a Social Graph
28 Superhero Degrees of Separation Introducing Breadth-First Search
29 Superhero Degrees of Separation Accumulators, and Implementing BFS in Spark
30 Superhero Degrees of Separation Review the code, and run it!
31 Item-Based Collaborative Filtering in Spark, cache(), and persist()
32 [Activity] Running the Similar Movies Script using Spark’s Cluster Manager
33 [Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
34 [Activity] Using spark-submit to run Spark driver scripts
35 [Activity] Packaging driver scripts with SBT
36 Introducing Amazon Elastic MapReduce
37 Creating Similar Movies from One Million Ratings on EMR
39 Best Practices for Running on a Cluster
40 Troubleshooting, and Managing Dependencies
SparkSQL, DataFrames, and DataSets
41 Introduction to SparkSQL
42 [Activity] Using SparkSQL
43 [Activity] Using DataFrames and DataSets
44 [Activity] Using DataSets instead of RDD’s
Machine Learning with MLLib
45 Introducing MLLib
46 [Activity] Using MLLib to Produce Movie Recommendations
47 [Activity] Linear Regression with MLLib
48 [Activity] Using DataFrames with MLLib
Intro to Spark Streaming
49 Spark Streaming Overview
50 [Activity] Set up a Twitter Developer Account, and Stream Tweets
51 Structured Streaming
Intro to GraphX
52 GraphX, Pregel, and Breadth-First-Search with Pregel.
53 [Activity] Superhero Degrees of Separation using GraphX
You Made It! Where to Go from Here
54 Learning More, and Career Tips
55 Bonus Lecture Discounts on my other Big Data Data Science Courses.