# Databricks beginner workflow: bakery data to an AI/BI dashboard

Check the data, rank sales, analyse a small review sample and build a dashboard without implying the data proves causation.

[HTML](https://lukejbyrne.com/library/guides/g3dv6dj3bpo/)
Content language: en
Author: Luke Byrne
Video published: 2026-09-24
Guide published: 2026-10-31
Sponsored coverage: Databricks

[Watch the video](https://www.youtube.com/watch?v=g3dv6dJ3bpo)
Video language: English

## Start with the notebook and inspect the data

Thanks to Databricks for sponsoring this video.

The tutorial uses simulated bakery data in Databricks Free Edition. Treat the notebook as a sequence of Python and SQL cells, with an inspectable output after each step.

1. Import the notebook through the route shown at 1:32.
2. Run the data checks before the ranking query: inspect the locations, sales values and available dates.
3. Rank locations by recorded sales value and create the bar chart. Read the labels and units back against the underlying rows.

## Test sentiment on one review first

The review sample contains 12 selected customer reviews. AI sentiment labels need checking against the original text.

1. Select the review sample and compare its date range with the sales data.
2. Run sentiment analysis on one review and inspect the label before processing the rest.
3. Label the remaining sample, review questionable cases and summarise the counts. Keep the original review text available for inspection.

## Build and verify the dashboard

The saved sales and sentiment results are brought together in an AI/BI dashboard. Their appearance on the same page does not establish a causal relationship.

1. Prepare the saved query results and import the dashboard layout shown at 14:14.
2. Check the chart values against the notebook outputs.
3. Test refresh, publishing and sharing with the intended audience. Recheck access rather than assuming a published dashboard is visible to everyone.

## The takeaway

Use the dashboard to explore the sample. Different sales and review periods, a small review sample and AI labels cannot explain what caused a location’s sales.

## Workflow resources

- [databricks.plug.dev](https://databricks.plug.dev/lmb7BSU)

## Chapters

- [0:00 What you'll build with Databricks Free Edition](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=0s)
- [1:32 Import the notebook and understand cells](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=92s)
- [2:50 Check the bakery sample data](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=170s)
- [5:06 Rank sales and create a bar chart](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=306s)
- [7:15 Select customer reviews for analysis](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=435s)
- [9:40 Check the sales and review date ranges](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=580s)
- [11:00 Test AI sentiment analysis on one review](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=660s)
- [12:17 Label and summarise the remaining reviews](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=737s)
- [14:14 Prepare and import the AI/BI dashboard](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=854s)
- [16:54 Interpret the sales and sentiment results](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=1014s)
- [17:58 Refresh, publish and share the dashboard](https://www.youtube.com/watch?v=g3dv6dJ3bpo&t=1078s)

## Sources and limitations

Written follow-along guide prepared 31 October 2026 from this exact published video’s description and chapter sequence. The checklists are editorial adaptations, not verbatim transcript quotations. Demonstrated results are attributed to the video; the full final video was not independently transcribed or watched for this article, and the product was not retested. For installation commands, later interface changes and current offers, use the original resources linked here.

[Original video](https://www.youtube.com/watch?v=g3dv6dJ3bpo)