English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7 Hours | 3.60 GB
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 3, more hands-on exercises, and a stronger focus on DataFrames and Structured Streaming.
“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.
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. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb.
- Learn the concepts of Spark’s DataFrames and Resilient Distributed Datastores
- Develop and run Spark jobs quickly using Python
- 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
- Learn about other Spark technologies, like Spark SQL, 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.
This course uses the familiar Python programming language; if you’d rather use Scala to get the best performance out of Spark, see my “Apache Spark with Scala – Hands On with Big Data” course instead.
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 The Incredible Hulk? 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 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.
Wrangling big data with Apache Spark is an important skill in today’s technical world.
What you’ll learn
- Use DataFrames and Structured Streaming in Spark 3
- Frame big data analysis problems as Spark problems
- Use Amazon’s Elastic MapReduce service to run your job on a cluster with Hadoop YARN
- Install and run Apache Spark on a desktop computer or on a cluster
- Use Spark’s Resilient Distributed Datasets to process and analyze large data sets across many CPU’s
- Implement iterative algorithms such as breadth-first-search using Spark
- Use the MLLib machine learning library to answer common data mining questions
- Understand how Spark SQL lets you work with structured data
- Understand how Spark Streaming lets your process continuous streams of data in real time
- Tune and troubleshoot large jobs running on a cluster
- Share information between nodes on a Spark cluster using broadcast variables and accumulators
- Understand how the GraphX library helps with network analysis problems
Table of Contents
Getting Started with Spark
1 Introduction
2 How to Use This Course
3 Udemy 101 Getting the Most From This Course
4 [Activity]Getting Set Up Installing Python, a JDK, Spark, and its Dependencies.
5 [Activity] Installing the MovieLens Movie Rating Dataset
6 [Activity] Run your first Spark program! Ratings histogram example.
Spark Basics and the RDD Interface
7 What’s new in Spark 3
8 Introduction to Spark
9 The Resilient Distributed Dataset (RDD)
10 Ratings Histogram Walkthrough
11 KeyValue RDD’s, and the Average Friends by Age Example
12 [Activity] Running the Average Friends by Age Example
13 Filtering RDD’s, and the Minimum Temperature by Location Example
14 [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums
15 [Activity] Running the Maximum Temperature by Location Example
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 [Excercise] Check your Results, and Now Sort them by Total Amount Spent.
21 Check Your Sorted Implementation and Results Against Mine.
SparkSQL, DataFrames, and DataSets
22 Introducing SparkSQL
23 [Activity] Executing SQL commands and SQL-style functions on a DataFrame
24 Using DataFrames instead of RDD’s
25 [Exercise] Friends by Age, with DataFrames
26 Exercise Solution Friends by Age, with DataFrames
27 [Activity] Word Count, with DataFrames
28 [Activity] Minimum Temperature, with DataFrames (using a custom schema)
29 [Exercise] Implement Total Spent by Customer with DataFrames
30 Exercise Solution Total Spent by Customer, with DataFrames
Advanced Examples of Spark Programs
31 [Activity] Find the Most Popular Movie
32 [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers
33 Find the Most Popular Superhero in a Social Graph
34 [Activity] Run the Script – Discover Who the Most Popular Superhero is!
35 [Exercise] Find the Most Obscure Superheroes
36 Exercise Solution Most Obscure Superheroes
37 Superhero Degrees of Separation Introducing Breadth-First Search
38 Superhero Degrees of Separation Accumulators, and Implementing BFS in Spark
39 [Activity] Superhero Degrees of Separation Review the Code and Run it
40 Item-Based Collaborative Filtering in Spark, cache(), and persist()
41 [Activity] Running the Similar Movies Script using Spark’s Cluster Manager
42 [Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
43 Introducing Elastic MapReduce
44 [Activity] Setting up your AWS Elastic MapReduce Account and Setting Up PuTTY
45 Partitioning
46 Create Similar Movies from One Million Ratings – Part 1
47 [Activity] Create Similar Movies from One Million Ratings – Part 2
48 Create Similar Movies from One Million Ratings – Part 3
49 Troubleshooting Spark on a Cluster
50 More Troubleshooting, and Managing Dependencies
Machine Learning with Spark ML
51 Introducing MLLib
52 [Activity] Using Spark ML to Produce Movie Recommendations
53 Analyzing the ALS Recommendations Results
54 [Activity] Linear Regression with Spark ML
55 [Exercise] Using Decision Trees in Spark ML to Predict Real Estate Prices
56 Exercise Solution Decision Trees with Spark
Spark Streaming, Structured Streaming, and GraphX
57 Spark Streaming
58 [Activity] Structured Streaming in Python
59 [Exercise] Use Windows with Structured Streaming to Track Most-Viewed URL’s
60 Exercise Solution Using Structured Streaming with Windows
61 GraphX
You Made It! Where to Go from Here
62 Learning More about Spark and Data Science
63 Bonus Lecture More courses to explore!
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