25-second overview
Video transcript
- At 0:00: Every morning, grocery stores face the same question: what do we reorder, what do we discount, what do we promote?
- At 0:04: Stockouts lose sales. Overstock turns into waste. Both quietly eat margin.
- At 0:09: Grocery Data Intelligence ingests raw point-of-sale and inventory data into a Bronze → Silver → Gold lakehouse on Databricks Free Edition.
- At 0:14: A Gold risk view scores every product in every store and ranks the actions that matter today.
- At 0:19: Store managers open one dashboard, see the day's plan, and act in minutes — not hours.
- At 0:23: Smarter inventory. Less waste. Happier shelves.
Audio companion — full walkthrough
For when you'd rather listen than read — what this app does, how it's built, and how a store uses it every morning.
0:00/6:59
voice enhanced by ai
A grocery data product
built on the Lakehouse
A single-builder experiment that turns raw store data into a daily plan — what to reorder, what to discount, what to promote — for every store, every morning.
Built by Brahmareddy · Databricks Community Esteemed Contributor
Thank you, Databricks leaders, for your time and feedback
When I shared this project on r/databricks, I never expected senior Databricks leaders to read it carefully and write back. Denny Lee, Youssef Mrini, and Sunmin Lee — thank you. Your feedback genuinely shaped the product.
- 4
- Improvements shipped
- <1s
- Page loads, cached
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- Reddit upvotes
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- Reddit comments
“I really appreciate you making this app. I completely agree with you that having data and AI work together to build something actionable is exactly where things should go.”
Denny Lee
PM Director, Developer Relations at Databricks
Co-author of "Learning Spark" and "Delta Lake: The Definitive Guide" · Apache Spark Contributor · Delta Lake Maintainer
“The idea is brilliant and the execution is great. I really like how you used everything that was available to you starting with Databricks Free Edition to Genie Code. Good Job again.”
Youssef Mrini
Solutions Architect at Databricks
Data+AI Summit 2026 Speaker · Reddit Top 1% Poster
“super cool, very impressive”
Sunmin Lee
DatabricksSenior Solutions Engineer at Databricks
Databricks
What your feedback made better
Action flow redesigned for transparency
3 clear actions per itemThanks to Denny Lee
The single "Take Action" button was replaced with intent-specific reviews. Every item shows quantity, priority, and a confirmation step.
Full architecture written up for the community
Bronze → Silver → GoldThanks to Denny Lee
Posted the end-to-end medallion design to Databricks Community so peers can review the modeling choices and Gold views.
Semantic layer + Genie-style Ask Data
AI/BI Genie-styleThanks to Youssef Mrini
Ask Data + AI runs over approved Gold views — live for some intents, powered by a Databricks-hosted Foundation Model.
Sub-second page loads, end to end
<1s cached · ~1.5s coldPerformance outcome
SWR caching, in-flight request dedup, and a token-gated cache-warm endpoint.
See the full discussion on r/databricks
Original post, expert replies, and follow-up questions
Most grocery analytics stop at the dashboard.
The store manager still has to translate charts into a morning routine: what to pull, what to mark down, what to push. That gap is where shrink and lost sales actually happen.
Grocery Data Intelligence closes that gap. It models the same lakehouse data every chain already has — POS, inventory, expiry, sales velocity — through a clean Bronze → Silver → Gold pipeline, and surfaces the Gold layer as a daily inventory story and a per-product action list.
Architecture
The full technical build — Bronze → Silver → Gold
Live product
Open today's inventory story
Have feedback? Reply on the Reddit thread.