Mô tả

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 and specifically PySpark. 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 and pyspark

  • 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. Enroll now!


  • " I studied "Taming Big Data with Apache Spark and Python" with Frank Kane, and helped me build a great platform for Big Data as a Service for my company. I recommend the course!  " - Cleuton Sampaio De Melo Jr.

Bạn sẽ học được gì

Use DataFrames and Structured Streaming in Spark 3

Use the MLLib machine learning library to answer common data mining questions

Understand how Spark Streaming lets your process continuous streams of data in real time

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

Understand how Spark SQL lets you work with structured data

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

Yêu cầu

  • Access to a personal computer. This course uses Windows, but the sample code will work fine on Linux as well.
  • Some prior programming or scripting experience. Python experience will help a lot, but you can pick it up as we go.

Nội dung khoá học

8 sections

Getting Started with Spark

9 lectures
Introduction
01:46
How to Use This Course
01:41
Udemy 101: Getting the Most From This Course
02:10
Important note
00:24
IMPORTANT! DO NOT USE JAVA 16 OR NEWER WITH THIS COURSE
00:14
[Activity]Getting Set Up: Installing Python, a JDK, Spark, and its Dependencies.
14:54
Alternate MovieLens download location
00:05
[Activity] Installing the MovieLens Movie Rating Dataset
03:35
[Activity] Run your first Spark program! Ratings histogram example.
06:12

Spark Basics and the RDD Interface

15 lectures
What's new in Spark 3?
06:48
Introduction to Spark
10:11
The Resilient Distributed Dataset (RDD)
12:35
Ratings Histogram Walkthrough
13:27
Key/Value RDD's, and the Average Friends by Age Example
16:08
[Activity] Running the Average Friends by Age Example
05:40
Filtering RDD's, and the Minimum Temperature by Location Example
08:11
[Activity]Running the Minimum Temperature Example, and Modifying it for Maximums
05:06
[Activity] Running the Maximum Temperature by Location Example
03:19
[Activity] Counting Word Occurrences using flatmap()
07:24
[Activity] Improving the Word Count Script with Regular Expressions
04:42
[Activity] Sorting the Word Count Results
07:46
[Exercise] Find the Total Amount Spent by Customer
04:01
[Excercise] Check your Results, and Now Sort them by Total Amount Spent.
05:09
Check Your Sorted Implementation and Results Against Mine.
02:44

SparkSQL, DataFrames, and DataSets

9 lectures
Introducing SparkSQL
09:29
[Activity] Executing SQL commands and SQL-style functions on a DataFrame
07:46
Using DataFrames instead of RDD's
07:39
[Exercise] Friends by Age, with DataFrames
01:45
Exercise Solution: Friends by Age, with DataFrames
07:54
[Activity] Word Count, with DataFrames
09:37
[Activity] Minimum Temperature, with DataFrames (using a custom schema)
10:27
[Exercise] Implement Total Spent by Customer with DataFrames
02:08
Exercise Solution: Total Spent by Customer, with DataFrames
04:07

Advanced Examples of Spark Programs

12 lectures
[Activity] Find the Most Popular Movie
04:16
[Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers
10:34
Find the Most Popular Superhero in a Social Graph
03:15
[Activity] Run the Script - Discover Who the Most Popular Superhero is!
08:00
[Exercise] Find the Most Obscure Superheroes
02:16
Exercise Solution: Most Obscure Superheroes
04:13
Superhero Degrees of Separation: Introducing Breadth-First Search
07:56
Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark
06:44
[Activity] Superhero Degrees of Separation: Review the Code and Run it
09:35
Item-Based Collaborative Filtering in Spark, cache(), and persist()
06:00
[Activity] Running the Similar Movies Script using Spark's Cluster Manager
13:43
[Exercise] Improve the Quality of Similar Movies
03:05

Running Spark on a Cluster

8 lectures
Introducing Elastic MapReduce
05:09
[Activity] Setting up your AWS / Elastic MapReduce Account and Setting Up PuTTY
09:58
Partitioning
04:21
Create Similar Movies from One Million Ratings - Part 1
05:10
[Activity] Create Similar Movies from One Million Ratings - Part 2
12:50
Create Similar Movies from One Million Ratings - Part 3
03:52
Troubleshooting Spark on a Cluster
03:43
More Troubleshooting, and Managing Dependencies
06:02

Machine Learning with Spark ML

6 lectures
Introducing MLLib
06:04
[Activity] Using Spark ML to Produce Movie Recommendations
09:54
Analyzing the ALS Recommendations Results
04:12
[Activity] Linear Regression with Spark ML
13:25
[Exercise] Using Decision Trees in Spark ML to Predict Real Estate Prices
05:34
Exercise Solution: Decision Trees with Spark
06:19

Spark Streaming, Structured Streaming, and GraphX

5 lectures
Spark Streaming
08:04
[Activity] Structured Streaming in Python
08:47
[Exercise] Use Windows with Structured Streaming to Track Most-Viewed URL's
05:49
Exercise Solution: Using Structured Streaming with Windows
06:37
GraphX
02:11

You Made It! Where to Go from Here.

2 lectures
Learning More about Spark and Data Science
03:43
Bonus Lecture: More courses to explore!
00:58

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