Spark and Python for Big Data with PySpark
Learn the latest Big Data Technology – Spark! And learn to use it with one of the most popular programming languages, Python!
One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!
Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!
This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that we’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you’ll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!
We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion!
If you’re ready to jump into the world of Python, Spark, and Big Data, this is the course for you!
Introduction to Course
Setting up Python with Spark
Let's explain the set-up for the course!
Local VirtualBox Set-up
Let's walk through the local installation of Ubuntu
AWS EC2 PySpark Set-up
Let's show you how to use Amazon Web Services' EC2 Instances for Spark!
AWS EMR Cluster Setup
Python Crash Course
Spark DataFrame Basics
Learn the basics of Spark DataFrames!
Learn some basic operations with Spark 2.0