The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you’ll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you’ll learn how to execute end-to-end workflows using PySpark.
Right through the course, you’ll be using PySpark to perform data analysis. You’ll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you’ll explore the transformations and actions that can be performed on the data using Spark RDDs and Dataframes. You’ll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You’ll use the Databricks environment to run the Spark scripts and explore it as well.
Finally, you’ll have a taste of Spark with AWS cloud. You’ll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data.
By the end of this course, you’ll be able to understand and implement the concepts of PySpark and AWS to solve real-world problems.
The code bundles are available here: https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with…
Learn the importance of Big Data
Explore the Spark and Hadoop architecture and ecosystem
Learn about PySpark Dataframes and PySpark DataFrames actions
Use PySpark DataFrames transformations
Apply collaborative filtering to develop a recommendation system using ALS models