§ 01Context
Our client — a leading IT service provider whose name is under NDA — runs incident management across a large, multi-region IT estate. They needed an advanced analytics platform that could both surface what's happening across that estate and translate it into an ROI story for the business. The original build sat entirely on Bubble; it worked, but it buckled under the data volume they actually operate at.
§ 02Challenge
Bubble is wonderful as an application layer, but it's not a data warehouse. The initial platform struggled with files over 100,000 rows — analyses that should run in seconds were timing out, and the ROI calculator couldn't be trusted on real-world exports. The brief was sharp:
- Keep the Bubble frontend the team already liked.
- Move the heavy data processing somewhere it belongs.
- Don't break the user experience mid-migration.
Bubble for the surface. A real database underneath. The user shouldn't feel the seam.— Engagement kickoff
§ 03Solution
We delivered a two-phase build. Phase one stayed entirely on Bubble so the client had a live platform to iterate against. Phase two carved out the analysis engine and rebuilt it on Directus — an API-first data platform — then re-wired the Bubble frontend to call it. The seam is invisible; the speedup isn't.
What Directus changed
- Scale. Files north of 100k rows are processed in seconds, not minutes.
- Trust. ROI numbers are computed on the full dataset, not a sampled slice.
- Headroom. New data sources plug into Directus first, surface in Bubble second.
§ 04Versions
Delivered in two phases so the client had working software from month one, and a scalable platform by month six.
Entirely on Bubble
Frontend and backend both on Bubble. Fast to ship, good for smaller datasets, perfect as a proof of concept for the client team to validate the analytics model.
Bubble frontend · Directus engine
Data processing moved to Directus — an API-first data platform built for volume. Bubble continues to own the interface; Directus does the heavy lifting behind it.
Same UI. New spine. 100k+ rows in seconds, not minutes.
— Phase 02 close-out
§ 05Result
The upgraded platform hit all four outcomes the engagement set out to deliver:
- Data processing. The Directus engine parses files > 100,000 rows and returns rapid, accurate analytics.
- User experience. The Bubble frontend is unchanged to the user — the migration is invisible except that things got faster.
- Scalability. The architecture handles future growth in both dataset size and user count without re-plumbing.
- ROI calculation. The client can now run ROI on real data — which directly improves incident-management decisions across the estate.
— End of case. Low-code hitting a ceiling? Let's talk about a migration path.