topic Genie Use Cases & easy adaptibility in Data Engineering

Genie Use Cases & easy adaptibility

suryaprayaga — Thu, 24 Sep 2026 12:48:50 GMT

Recently I started socializing the use of Genie and Databricks to even a laymen (and ofcourse laywomen) who have never heard what coding is in their life. I am proud that in my company nearly 200 people as of now are using Databricks for some or other use case in their daily life. Right from basic data extraction and transformation in Google Sheets to taking screenshots from websites to building NextGen creative ads to building Data Science models are built using Databricks and especially with Jobs & Pipelines in Databricks.

And yes out of 200 people only 30% know Databricks and remaining are now very proficient in using Databricks eventhough they are not much sure what's going on in the backgroud - just with the power of Genie.

Ofcourse my goal is to solve as many use cases as possible with Genie in the near future while pursuing the journey of becoming a Databricks partner.

Once again kudos to Databricks for your excellent packaged tool - Genie.

Re: Genie Use Cases & easy adaptibility

DoTA — Sun, 04 Oct 2026 02:00:04 GMT

Hi, congratulations on getting to 200 people, and especially on getting non-coders productive. From our own experience of scaling an internal data and AI platform to a couple thousand users, the things that mattered most once adoption passed the first couple of hundred were not the tool itself:

1. Trust in answers. Non-technical users cannot spot a wrong join or a wrong filter, so the quality of the Genie space matters more than the model. Curate each space around one business domain, write column comments and sample questions in the users' own vocabulary, and keep a set of benchmark questions with known answers that you re-run after every change to the space or the underlying tables.

2. Access by default, not by exception. Because people do not know what is happening in the background, permissions have to be right at the data layer. Use Unity Catalog grants, row filters and column masks so a Genie space can never show someone data they could not query directly, and review who can edit a space separately from who can use it.

3. Cost visibility per team. At 200 users the warehouse bill becomes a real number. Decide early which SQL warehouse backs which space, and tag or separate them so you can attribute spend to departments rather than to one shared line.

4. A feedback loop. Ask users to flag wrong answers, and have one owner per space triage those weekly. Most improvement came from fixing metadata and instructions, not from anything clever.

Curious which use case is the most common among the users who are new to Databricks, and whether you have had any wrong-answer incidents yet.

Re: Genie Use Cases & easy adaptibility

suryaprayaga — Sun, 04 Oct 2026 05:43:51 GMT

Thanks for your reply @DoTA . The most common use case that we use Genie for is to build jobs that are run as notebooks in Databricks for taking screenshots by most of our AdOps teams. We have multiple people using Genie for this very use case.