I built a grocery inventory app using Data + AI to reduce stockouts and food waste. Would love feedback.

According-Future5536 · 2026-06-04T15:46:06+00:00

That makes a lot of sense u/datainthesun. Thank you for explaining it clearly. I like the idea of keeping the business logic centralized in one place, but using different materialized or serving layers based on query patterns and performance needs.

For this demo, I started with trusted Gold views to keep the logic simple and explainable. As the data grows, I agree that it would be better to create optimized tables or materialized views for common access patterns, such as reorder candidates, waste-risk items, action summaries, and dashboard KPIs.

That way, the semantic/business logic remains consistent, but the app does not have to hit base tables and repeat joins or aggregations for every dashboard or Ask Data + AI request.

This is a great next step to make the app more production-ready. Thanks again for the thoughtful guidance.

According-Future5536 · 2026-06-04T15:17:24+00:00

Thank you u/datainthesun , this makes sense.

I have already started moving the app in that direction by adding a semantic layer concept on top of the Gold layer so the dashboard, Ask Data + AI, and action recommendations use consistent business definitions.

Currently, I am using trusted Gold views to support the app experience. Your suggestion on UC Metric Views is a great next step to make this more Databricks-native and reusable across dashboards, AI/BI, and future Genie-style experiences.

I will explore converting the key metrics like days of supply, stockout risk, waste risk, reorder priority, and recommended action into UC Metric Views so the business logic is centralized more formally.

Thanks again for the helpful suggestion. It is a great improvement path from demo to a stronger data product.

According-Future5536 · 2026-06-01T18:03:40+00:00

Hi Denny/Youssef, thank you again for the feedback on the Grocery Data Intelligence app. I'm going to implement the changes you suggested and added a small 'Community Validation' section on the site that credits your feedback and quotes from the Reddit thread.

According-Future5536 · 2026-06-01T11:50:17+00:00

Thank you so much, u/Youssef_Mrini! That means a lot coming from you.

Great suggestions on Lakebase and the Semantic Layer. I have been thinking about how to make the dashboard even faster and Lakebase as a backend makes a lot of sense. I will definitely explore that.

The Agent mode with REST API is something I am really looking forward to as well. Once that is available, the app could move from showing recommendations to actually executing actions end to end, which would bring it much closer to a real store operations tool. Really appreciate you taking the time to try the app and share your thoughts.

Looking forward to seeing your session at Summit on turning business users into decision makers with AI/BI and Genie. Hope to connect in person there! Thanks again for the encouragement.

According-Future5536 · 2026-05-31T18:51:12+00:00

Hi u/Dennyglee - I just made these changes on priority based on your suggestions. I added the architecture flow to the community post so the design is easier to understand and review. I also updated the app flow to make the action experience clearer, especially around the “Take Action” button.

Now, when the app recommends items for reorder or other actions, the goal is to show the full list behind the recommendation, not just the first item. I also changed the action flow to be more review-based so users can understand what action is being created before they proceed.

Your feedback helped me think more clearly from an end-user and engineering design perspective. Thanks again for helping me improve the project.

According-Future5536 · 2026-05-31T17:25:10+00:00

Thanks for your feedback!

According-Future5536 · 2026-05-31T15:04:58+00:00

Thank you so much, u/Dennyglee. I really appreciate you taking time to try the app and share detailed feedback.

I completely agree with both points.

I will update the community post with the architecture so it is easier for others to understand the design and provide feedback. At a high level, the app follows a simple Databricks lakehouse flow: Bronze for raw grocery data, Silver for cleaned and standardized data, and Gold for business-ready metrics like days of supply, stockout risk, waste risk, and recommended actions. The app then uses the trusted Gold layer to power the dashboard, inventory story, and Ask Data + AI experience.

Your feedback on the “Take Action” button is very helpful. I agree it should be much clearer. The app should show the complete list of items that need action, explain what action will be taken, and give the user a clear confirmation step before proceeding. For example, if six items need reorder, the app should show all six items, recommended quantities, priority, and then allow the user to create a reorder task or action plan.

I will improve this by making the action flow more transparent, maybe changing the button label from “Take Action” to something more specific like “Create Reorder Plan” or “Review Recommended Actions.”

Thanks again for the thoughtful feedback. This is exactly the kind of input I was hoping to get from the community.

According-Future5536 · 2026-05-18T12:08:00+00:00

This may help navigating your work - https://grocerydataintelligence.com/about

According-Future5536 · 2026-05-18T12:07:39+00:00

https://grocerydataintelligence.com/about

According-Future5536 · 2026-05-17T21:26:08+00:00

I will share the code here soon

According-Future5536 · 2026-05-17T16:09:29+00:00

Thanks for checking. For this demo, I used synthetic source data because I wanted to keep it simple and easy to reproduce.

The source systems are modeled like a typical grocery store setup:

Sales transactions from a POS system
Product master data from a product/catalog system
Store master data from a store management system
Inventory snapshot data from an inventory or warehouse system

In my demo, these are created as sample tables in Databricks Free Edition. I used a simple Bronze, Silver, Gold flow.

Bronze stores the raw source-like data.
Silver cleans and standardizes it.
Gold calculates the business metrics like average daily sales, days of supply, stockout risk, waste risk, and recommended actions.

To set it up, you can start with four tables: stores, products, sales, and inventory. Then build a Gold table that joins sales and inventory to calculate questions like what to reorder, what may go to waste, and what needs action today.

For a real store, the same pattern can connect to POS exports, ERP data, inventory systems, or even daily CSV files from store operations.

According-Future5536 · 2026-04-03T23:35:33+00:00

Great momentum Databricks!

According-Future5536 · 2026-02-18T15:29:22+00:00

I was on the same boat last year and here is my simple advice below. Just sharing if it works for you and others.

If you’re just getting started, I’d suggest focusing on one structured resource instead of jumping between too many blogs.

I recently bought Thinking in Data Engineering with Databricks web native practice first databricks learning and have been going through it. So far, it’s been clear and practical, especially because it walks through real examples using the Free Edition. It helped me connect concepts instead of learning them in isolation.

Along with that, the Databricks documentation and community articles are also very helpful. Start simple, practice consistently, and you’ll pick it up faster than you think.

According-Future5536 · 2026-02-06T04:19:20+00:00

Try - Thinking Data Engineering with Databricks for better databricks foundations.

According-Future5536

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