Cleaning Data with PySpark

Working with data can be difficult. It can be frustrating to work with millions or billions of rows. It is possible that you received data processing code from a laptop with very clean data. It is possible that you were responsible for moving basic data processing processes from prototype to production. You might have worked with real-world data. This could include missing fields, unusual formatting or data orders of magnitude larger. Even if you are not an expert on the topic, this course will teach you how to prepare data processes using Python and Apache Spark. This course will help you understand terminology and best practices to create a reliable, manageable and easy-to-understand data processing platform.