adhoc-exploration-notebooks

You can explore Databricks data locally by generating .ipynb notebooks configured for Databricks Connect, which initializes a Spark session and includes helper functions for table discovery and data quality checks in your local Jupyter environment.

Yes, this Skill generates dual-format notebooks (.py for Databricks and .ipynb for local Jupyter) that support widget fallback patterns and Spark session initialization, allowing you to run interactive data analysis seamlessly in both environments.

The best way to onboard is to generate exploration notebooks that list available tables, view schemas, inspect sample data, and perform initial data quality checks, helping you quickly understand the data landscape in your preferred environment.

Yes, you need Databricks Connect configured to run the generated .ipynb notebooks locally, as the notebooks rely on it to initialize the Spark session and interact with your Databricks data catalog and schemas.

You can perform data quality checks by using the helper functions included in the generated exploration notebooks, which provide utilities for table discovery, schema inspection, sample data viewing, and data comparison across your specified catalog and schema.