English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 9 Hours | 4.03 GB
Apache Spark tutorial with 20+ hands-on examples of analyzing large data sets, on your desktop or on Hadoop with Scala!
New! Completely updated and re-recorded for Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.
“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 Datasets, DataFrames, and Datasets.
- Get a crash course in the Scala programming language
- Develop and run Spark jobs quickly using Scala, IntelliJ, and SBT
- 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, Machine Learning, 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.
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, DataSets, and DataFrames
- Traverse and analyze graph structures using GraphX
- Analyze massive data set with Machine Learning on Spark
1 Udemy 101 Getting the Most From This Course
2 Introduction, and installing the course materials, IntelliJ, and Scala
3 Introduction to Apache Spark
4 What’s New in Spark 3
Scala Crash Course [Optional]
5 [Activity] Scala Basics
6 [Exercise] Flow Control in Scala
7 [Exercise] Functions in Scala
8 [Exercise] Data Structures in Scala
Using Resilient Distributed Datasets (RDDs)
9 The Resilient Distributed Dataset
10 Ratings Histogram Example
11 Spark Internals
12 Key Value RDD’s, and the Average Friends by Age example
13 [Activity] Running the Average Friends by Age Example
14 Filtering RDD’s, and the Minimum Temperature by Location Example
15 [Activity] Running the Minimum Temperature Example, and Modifying it for Maximum
16 [Activity] Counting Word Occurrences using Flatmap()
17 [Activity] Improving the Word Count Script with Regular Expressions
18 [Activity] Sorting the Word Count Results
19 [Exercise] Find the Total Amount Spent by Customer
20 [Exercise] Check your Results, and Sort Them by Total Amount Spent
21 Check Your Results and Implementation Against Mine
SparkSQL, DataFrames, and DataSets
22 Introduction to SparkSQL
23 [Activity] Using SparkSQL
24 [Activity] Using DataSets
25 [Exercise] Implement the Friends by Age example using DataSets
26 Exercise Solution Friends by Age, with Datasets.
27 [Activity] Word Count example, using Datasets
28 [Activity] Revisiting the Minimum Temperature example, with Datasets
29 [Exercise] Implement the Total Spent by Customer problem with Datasets
30 Exercise Solution Total Spent by Customer with Datasets
Advanced Examples of Spark Programs
31 [Activity] Find the Most Popular Movie
32 [Activity] Use Broadcast Variables to Display Movie Names
33 [Activity] Find the Most Popular Superhero in a Social Graph
34 [Exercise] Find the Most Obscure Superheroes
35 Exercise Solution Find the Most Obscure Superheroes
36 Superhero Degrees of Separation Introducing Breadth-First Search
37 Superhero Degrees of Separation Accumulators, and Implementing BFS in Spark
38 [Activity] Superhero Degrees of Separation Review the code, and run it!
39 Item-Based Collaborative Filtering in Spark, cache(), and persist()
40 [Activity] Running the Similar Movies Script using Spark’s Cluster Manager
41 [Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
42 [Activity] Using spark-submit to run Spark driver scripts
43 [Activity] Packaging driver scripts with SBT
44 [Exercise] Package a Script with SBT and Run it Locally with spark-submit
45 Exercise solution Using SBT and spark-submit
46 Introducing Amazon Elastic MapReduce
47 Creating Similar Movies from One Million Ratings on EMR
49 Best Practices for Running on a Cluster
50 Troubleshooting, and Managing Dependencies
Machine Learning with Spark ML
51 Introducing MLLib
52 [Activity] Using MLLib to Produce Movie Recommendations
53 Linear Regression with MLLib
54 [Activity] Running a Linear Regression with Spark
55 [Exercise] Predict Real Estate Values with Decision Trees in Spark
56 Exercise Solution Predicting Real Estate with Decision Trees in Spark
Intro to Spark Streaming
57 The DStream API for Spark Streaming
58 [Activity] Real-time Monitoring of the Most Popular Hashtags on Twitter
59 Structured Streaming
60 [Activity] Using Structured Streaming for real-time log analysis
61 [Exercise] Windowed Operations with Structured Streaming
62 Exercise Solution Top URL’s in a 30-second Window
Intro to GraphX
63 GraphX, Pregel, and Breadth-First-Search with Pregel.
64 Using the Pregel API with Spark GraphX
65 [Activity] Superhero Degrees of Separation using GraphX
You Made It! Where to Go from Here
66 Learning More, and Career Tips
67 Bonus Lecture More courses to explore!