Inventory Intelligence

This template builds a full retail inventory management system on the Databricks stack: a React app where store managers monitor stock health, review AI-generated replenishment recommendations, and approve purchase orders — all powered by a live medallion pipeline and pluggable demand forecast job.

Data Flow

Sales and stock data flow from Lakebase Postgres through the lakehouse, get enriched by a demand forecast model, and are served back to the app through reverse sync:

1. OLTP writes land in Lakebase Postgres (stores, products, stock levels, sales transactions, replenishment orders).
2. Lakehouse Sync replicates every change into Unity Catalog as CDC history tables (bronze layer).
3. A Lakeflow Declarative Pipeline transforms CDC history into current-state silver tables and gold materialized views (inventory overview, low stock alerts, sales velocity).
4. A Lakeflow Job runs on a schedule, loads the silver sales history, and runs a pluggable demand forecast model to produce 30-day unit forecasts and replenishment recommendations in a Delta gold table.
5. Sync Tables (reverse sync) replicate the gold tables back into Lakebase for low-latency reads.
6. The Inventory Intelligence App (Databricks App) reads from both OLTP and synced gold tables to show dashboards, store drill-downs, a replenishment queue, and optional Genie analytics.

Design

The app should have a beautiful, polished design — clean typography, consistent spacing, and a professional retail aesthetic. Use shadcn/ui components as the foundation, Tailwind for all styling, and brand colors throughout. Dashboards should feel data-rich but uncluttered; the replenishment queue should make approval workflows feel effortless.

What to Adapt

Provisioning (Unity Catalog schemas, Lakebase REPLICA IDENTITY), seeding, pipeline deploys, reverse sync, and app deploy are documented in the repository's template/README.md alongside the code.

To make this template your own:

• Catalog: Set the catalog variable in each pipeline's databricks.yml to your Unity Catalog catalog name.
• Lakebase: Point the app's databricks.yml at your own Lakebase project, branch, and database.
• Tables: The seed script creates the OLTP schema with 5 stores, 25 products, and 90 days of sales history. After seeding, configure Lakehouse Sync to replicate the inventory schema tables.
• Sync Tables: Manually create the three reverse sync configurations (see the README for the exact table mappings).
• Forecast Model: Set the forecast_model variable in the demand forecast pipeline to weighted_moving_average (default), exponential_smoothing, prophet, or model_serving.
• Genie Space: Create a Genie space over your gold tables and set the genie_space_id in the app bundle to activate the Analytics tab.