System Tables: Billing Forecast, Usage Analytics, and Access Auditing With Databricks Unity Catalog

Databricks Unity Catalog is the world's first AI-powered governance solution for the lakehouse. It empowers enterprises to seamlessly govern their structured and unstructured data, ML models, notebooks, dashboards, and files on any cloud or platform. 

Through Delta Sharing, Databricks Unity Catalog offers direct access to many of the lakehouse activity logs exposed in Delta as System Tables. System Tables are the cornerstone of lakehouse observability and enable at-scale operational intelligence on numerous key business questions. In this demo, we'll show how Unity Catalog System Tables can be used to:

• Monitor your consumption and leverage the lakehouse AI capabilities to forecast your future usage, triggering alerts when billing goes above your criterias
• Monitor accesses to your data assets 
• Monitor and understand your platform usage

To install the demo, get a free Databricks workspace and execute the following two commands in a Python notebook

%pip install dbdemos

import dbdemos
dbdemos.install('uc-04-system-tables', catalog='main', schema='billing_forecast')

Dbdemos is a Python library that installs complete Databricks demos in your workspaces.

Dbdemos will load and start notebooks, Delta Live Tables pipelines, clusters, Databricks SQL dashboards, warehouse models … See how to use dbdemos

Dbdemos is distributed as a GitHub project.

For more details, please view the GitHub README.md file and follow the documentation.
Dbdemos is provided as is. See the License and Notice for more information.
Databricks does not offer official support for dbdemos and the associated assets.
For any issue, please open a ticket and the demo team will have a look on a best-effort basis.