Data Storage in PySpark: save vs saveAsTable

When it comes to saving DataFrames in PySpark, the choice between ‘save’ and ‘saveAsTable’ is more significant than it might initially appear. Although they perform similar tasks—saving your DataFrame to a location—these methods are quite different. This article dives into their differences, the scenarios where each is most effective, and the implications for data storage and retrieval. To understand the difference between these to methods, it’s helpful to first understand the concept of a table in Spark. <h1>Understanding Tables in Spark</h1> In traditional databases, tables are physical objects. They are structured data containers with predefined schemas that hold your data, stored as physical files on disk storage. But in Spark, the concept of a table is slightly different. <a href="https://medium.com/@tomhcorbin/data-storage-in-pyspark-save-vs-saveastable-8787e9370dde">Click Here</a>