Taming Big Data with Apache Spark 4 and Python - Hands On!

New! Updated for Spark 4's newest features

“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

• Practice using Spark's latest features, including Pandas-On-Spark, Spark Connect, and User-Defined Table Functions (UDTFs).

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. 8 hours of video content is included, with over 40 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 Structured 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.

  
  

• "Awesome course on running big data jobs on Apache Spark using Python. As usual, Frank explains things very clearly and points out various items to watch out for and make sure you have set up correctly. There are many ways that a Spark job can fail or have issues, such as running out of memory, and Frank does a great job of pointing many of those out." -James Gershfiel

  
  

• "Easy steps so even a beginner should be able to install Spark and run the examples right away. Good examples and fun to do. Giving a nice set of useful examples as a toolbox." - HansEV

  
  

• "Great course to get you going with Apache Spark and Python! Frank’s delivery is very thorough yet unpretentious; his explanations for each new concept that he introduces is down to earth and easy to follow." - Amiri McCain