Press "Enter" to skip to content

Read and Write Data with PySpark

Dustin Vannnoy has two of the three R’s down:

Every Spark pipeline involves reading data from a data source or table. For data engineers we usually end the pipelines by writing the transformed data. In this tutorial we walk through some of the most common format and cloud storage locations for reading and writing with Spark. We’ll save some of the advanced Delta Lake capabilities for another tutorial.

Click through to see how to read from and write to CSV, JSON, and Parquet formats. Dustin has examples of working with Azure Blob Storage, S3, and Google Cloud Storage, and even some database examples with JDBC.