Databricks Apps – Financial Forecasting

An open-source Databricks App for financial forecasting, what-if scenario testing, and revenue runway modeling.
This project demonstrates how to combine Databricks Lakehouse, Delta tables, and interactive web applications to enable real-time business forecasting.

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Features

• Excel ingestion: Import existing income, expense, and CRM export files.
• Automated ETL: Clean, transform, and store data as Delta tables.
• Sales forecasting: Estimate future revenue based on CRM opportunity probabilities.
• Expense modeling: Track operational, fixed, and variable costs.
• Runway analysis: Project how long the business can operate under different growth assumptions.
• Scenario testing: Adjust variables such as deal close rate, churn, or cost increase to simulate outcomes.
• Interactive dashboard: Built using Plotly Dash (or Streamlit) as a Databricks App.

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Architecture

Excel → Databricks Ingestion (Auto Loader) → Delta Tables → Forecast Engine → App UI → Insights

• Data Layer – Delta Lake for unified, versioned storage.
• Computation Layer – PySpark + MLflow for forecasting models.
• Application Layer – Databricks App (Plotly Dash / Streamlit) for interactive scenario input and visualization.

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Example Use Cases

• Finance teams forecasting revenue based on CRM pipeline probabilities.
• Startup founders calculating cash runway and burn rate.
• Analysts modeling "what-if" growth or expense scenarios.

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Folder Structure

databricks-apps-financial-forecasting/
│
├── data/                # Example input Excel files (income, expenses, CRM)
├── notebooks/           # Databricks notebooks (ETL, model training, forecasting)
├── app/                 # Dash/Streamlit app for scenario simulation
├── models/              # Trained forecasting models (if any)
├── dashboards/          # Visualization templates
└── README.md

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Setup

1. Clone this repo:

   git clone https://github.com/<your-org>/databricks-apps-financial-forecasting.git