Efficient Schema Extraction in Databricks with Pyspark: A Step-by-Step Guide

Extracting Schema information from Databricks would seem to be a very simple solution, isn’t it? I mean there is INFORMATION_SCHEMA to use. That is unfortunately only applicable only to Unity Catalog i.e. a databricks metastore that can share data across multiple Azure Databricks workspaces. Unfortunately no such catalog exists in our project. Best recourse for this is by using Python Spark SQL libraries.


In one of the recent tasks, I had to update a large SQL query comprised of 10+ Databricks tables with additional attributes and replace some existing ones. The only problem is most of the attributes had no aliases to them. So, there was no way to know which attribute is from which table. It’s one of the most irritating and annoying things that bothers me to no end and feel like…


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