Darin McBeth creates a meta-noterbook to keep track of notebooks:
Elsevier has been a customer of Databricks for about six years. There are now hundreds of users and tens of thousands of notebooks across their workspace. To some extent, Elsevier’s Databricks users have been a victim of their own success, as there are now too many notebooks to search through to find some earlier work.
The Databricks workspace does provide a keyword search, but we often find the need to define advanced search criteria, such as creator, last updated, programming language, notebook commands and results.
Interestingly, we managed to achieve this functionality using a 100% notebook-based solution with Databricks functionalities. As you will see, this makes it easy to set up in a customer’s Databricks environment.
Read on to see how.