Original LinkedIn Post
The Future of Tableau Prep: When AI Understands Business Logic Better Than UI Clicks , use gemini or claude code to generate or modify
Most people see Tableau Prep as a pure "drag-and-drop" tool. We spend our days manually connecting nodes, dragging lines, and clicking through menus.
But I just conducted an experiment that completely shifted my perspective on data work: Every Tableau Prep flow is actually just a JSON file in disguise.
The Experiment: Instead of opening the software to manually edit a complex flow, I gave a simple business command to an AI: > "I no longer need the 'Geographic Area' join in this process. Remove it and clean up the flow."
The Result was mind-blowing: The AI didn't just "guess"?it understood the business intent. It then automatically wrote a Python script to navigate the underlying JSON, "surgically" extracted the unwanted nodes, and perfectly re-wired the upstream and downstream logic.
After repacking the modified folder into a ZIP and renaming it back to .tfl, I opened it in Tableau Prep. The flow was updated precisely.
Why this matters: This proves that AI is now capable of understanding business logic and architecting data pipelines directly.
We are entering a new era of data engineering: * Future: AI handles the construction of the flow, while we focus on defining the core business logic.
This is a massive leap in efficiency. It allows us to stop being "pipeline laborers" and start being true Data Architects. The value isn't in how you connect the dots, but in knowing why they should be connected in the first place.
If AI could handle 80% of your manual ETL dragging, what would you do with all that saved time? Let's discuss below! ?
#Tableau #TableauPrep #DataAnalytics #AI #Automation #BusinessLogic #prep #gemini