Taming Big Data with Apache Spark and Python – Hands On!

Taming Big Data with Apache Spark and Python – Hands On!
Taming Big Data with Apache Spark and Python – Hands On!

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 5 Hours | 1.41 GB
eLearning | Skill level: All Levels

Dive right in with 15+ hands-on examples of analyzing large data sets with Apache Spark, on your desktop or on Hadoop!

New! Updated for Spark 2.0.0

“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 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 15 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 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. 5 hours of video content is included, with over 15 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 Will I Learn?

  • 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 Warning about Java 9!.html
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 Simple Examples
7 Introduction to Spark
8 The Resilient Distributed Dataset (RDD)
9 Ratings Histogram Walkthrough
10 KeyValue RDD’s, and the Average Friends by Age Example
11 [Activity] Running the Average Friends by Age Example
12 Filtering RDD’s, and the Minimum Temperature by Location Example
13 [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums
14 [Activity] Running the Maximum Temperature by Location Example
15 [Activity] Counting Word Occurrences using flatmap()
16 [Activity] Improving the Word Count Script with Regular Expressions
17 [Activity] Sorting the Word Count Results
18 Tally up amount spent by customer using Spark.html
19 Sort your results by amount spent per customer.html

Advanced Examples of Spark Programs
20 [Activity] Find the Most Popular Movie
21 [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers
22 Find the Most Popular Superhero in a Social Graph
23 [Activity] Run the Script – Discover Who the Most Popular Superhero is!
24 Superhero Degrees of Separation Introducing Breadth-First Search
25 Superhero Degrees of Separation Accumulators, and Implementing BFS in Spark
26 [Activity] Superhero Degrees of Separation Review the Code and Run it
27 Item-Based Collaborative Filtering in Spark, cache(), and persist()
28 [Activity] Running the Similar Movies Script using Spark’s Cluster Manager
29 [Exercise] Improve the Quality of Similar Movies

Running Spark on a Cluster
30 Introducing Elastic MapReduce
31 [Activity] Setting up your AWS Elastic MapReduce Account and Setting Up PuTTY
32 Partitioning
33 Create Similar Movies from One Million Ratings – Part 1
34 [Activity] Create Similar Movies from One Million Ratings – Part 2
35 Create Similar Movies from One Million Ratings – Part 3
36 Troubleshooting Spark on a Cluster
37 More Troubleshooting, and Managing Dependencies

SparkSQL, DataFrames, and DataSets
38 Introducing SparkSQL
39 Executing SQL commands and SQL-style functions on a DataFrame
40 Using DataFrames instead of RDD’s

Other Spark Technologies and Libraries
41 Introducing MLLib
42 [Activity] Using MLLib to Produce Movie Recommendations
43 Analyzing the ALS Recommendations Results
44 Using DataFrames with MLLib
45 Spark Streaming and GraphX

You Made It! Where to Go from Here
46 Learning More about Spark and Data Science
47 Bonus Lecture Discounts on my other courses!

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