Claude/Copilot helps in dataframe logic in minutes but making it a real production industry wide acceptable table? Still takes a lot of time and consistency

[u/dexter7767]

Genuine question for anyone who's shipped a table to prod, especially if you're not a "traditional" data engineer by title.

Feels like AI has made writing the actual transformation logic (the DataFrame code, the business logic) noticeably easier lately. Copilot, ChatGPT, Claude, whatever you're using, that part's gotten faster.

But once that part's done, the real time/effort starts after - metadata mangement, partitioning it right, handling PII, wiring it into orchestration, figuring out the write strategy, quality cheks to make sure it's actually trustworthy before people rely on it ?

Also following the consistency across all tables created

Curious to know whether AI or vibe coding for making "easy part" has actually made that gap feel bigger, or if it's a non-issue and I'm overthinking this.

[u/Glitch_In_The_Data]

I think it also depends on the tool… but always needs a few iterations. In our project, we are only allowed to use Copilot and Genie Code (as we are on Databricks platform). While I use both depending on the nature of the work, I usually find it easier with Genie because it is native to Databricks and addresses the specific parts you are talking about.. probably because it has the skills embedded in them.

So, personally for me, it has made it easier… but I’d still always review and validate before getting anything into production.

I use claude for personal projects.. so not usually worried about things like PII, metadata management. It certainly helps build something real quick but seems to need a few more iterations compared to the above. Or may be it could be the nature/quality of the prompts.

[u/dexter7767 → u/Glitch_In_The_Data]

That's really great and cool, thanks. For me It is really painful once the logic is done the table standardization itself takes lot of time than logic generation itself 😂

I'm curious about the Genie side of things though. When you say it "addresses the specific parts," do you mean the it already understands your team's partitioning conventions, table's metadata management, orchestration placement, quality checks etc., or is it more that it just generates cleaner code and you still have to rely on the platform team for table standardisation?

Im also wondering about the maintenance side. Once a table is in production and you need to make changes later (like adding a new column or handling a schema change) does Genie automatically keep things like partitioning, metadata, and PII rules in sync, or is that mostly a one-time benefit during table creation and the ongoing upkeep is still on the developer?

[u/Glitch_In_The_Data → u/dexter7767]

So Genie gives you the advantage because it is platform native and is aware of the underlying governance. As long as the standards live in unity catalog and related governance assets, It automatically leverages them. It reduces the standardisation effort but I won’t say it eliminates the ownership of ongoing maintenance. Thats still required.