Tinybird × Factorial
Tinybird x Factorial: 12 real-time features a small team could not ship before
12 new user-facing features shipped in six months
Stale batch data to real-time user-facing analytics
Members read free
Unlock the full breakdown
Enter your email to open every case study in the library: the full screenshot, why it works, and the one thing worth stealing. One email unlocks all of them.
Free. No spam. Unsubscribe anytime.
You're in — enjoy the library.
Why it works
The story earns New Capability because the before-state was not slowness but impossibility: the batch pipeline structurally could not deliver the data freshness or query latency required for user-facing features, so those features simply were not built. The mechanism is specific enough to follow: CDC via Confluent, Kafka as a buffer, Tinybird for enrichment and materialization, APIs wired directly into the product. The secondary Efficiency signal is real (two engineers, sub-50ms queries, one month to production) but the headline leads with net-new product features unlocked.
Steal this
Anchor the 'impossible before' claim with a concrete architectural reason (schedule-driven batch cannot meet sub-second latency) rather than just asserting the old tool was slow. That specificity makes the New Capability framing credible.
Click to enlarge ↗ This is editorial commentary and curation. The case study, screenshot, and all metrics are Tinybird's published work; we link to the source and lead with our analysis.