- 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 and DataFrames
- Traverse and analyze graph structures using GraphX
- Some prior programming or scripting experience is required. A crash course in Scala is included, but you need to know the fundamentals of programming in order to pick it up.
- You will need a desktop PC and an Internet connection. The course is created with Windows in mind, but users comfortable with MacOS or Linux can use the same tools.
- The software needed for this course is freely available, and I’ll walk you through downloading and installing it.
“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 Datastores
- Get a crash course in the Scala programming language
- Develop and run Spark jobs quickly using Scala
- 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, 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 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. 7.5 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.
Enjoy the course!
- Software engineers who want to expand their skills into the world of big data processing on a cluster
- If you have no previous programming or scripting experience, you’ll want to take an introductory programming course first.