The Databricks Lakehouse Platform is an open architecture that combines the best elements of data lakes and data warehouses. In this demo, we’ll show you how to build an end-to-end credit decisioning system for underbanked customers, delivering data and insights that would typically take months of effort on legacy platforms.

This demo covers the end-to-end lakehouse platform:

• Ingest both internal and partner data, and then transform them using Delta Live Tables (DLT), a declarative ETL framework for building reliable, maintainable and testable data processing pipelines
• Secure our ingested data to ensure governance and security on top of PII data
• Build a machine leanring model with Databricks AutoML to identify credit-worthy customers
• Leverage Databricks SQL and the warehouse endpoints to build a dashboard to analyze the ingested data and explain the machine learning model outputs
• Orchestrate all these steps with Databricks Workflows

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('lakehouse-fsi-credit')

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.