Writing · Series

The Data Pipeline Reliability series.

4 posts in this series, listed in publish order. Read top to bottom for the full arc, or jump in anywhere.

·9 min read · n8n , bubble , data-migration

Silent failures: the bug class no AI tool catches in your data pipeline

AI tools ship pipelines that pass every test and then quietly drop data in production. Here are 4 silent failures I diagnosed in a Bubble + n8n migration, and the fixes.

Silent failures: the bug class no AI tool catches in your data pipeline: cover image

·9 min read · n8n , bubble , idempotency

Idempotent data pipelines: the natural-key fingerprint pattern

Make any n8n or Bubble.io pipeline safely re-runnable: hash a stable subset of source fields, store the fingerprint, check before every write.

Idempotent data pipelines: the natural-key fingerprint pattern: cover image

·9 min read · n8n , n8n-cloud , workflow-architecture

n8n Cloud's 60-second timeout: the dispatcher-worker pattern that beats it

n8n Cloud caps Code nodes at 60 seconds and workflows at 40 minutes. Dispatcher-worker pattern gives every chunk a fresh execution clock.

n8n Cloud's 60-second timeout: the dispatcher-worker pattern that beats it: cover image

·9 min read · bubble , bubble-list-field , data-integrity

Why I stopped trusting Bubble.io's list fields and re-query the database instead

Bubble.io list fields don't auto-update when children point at parents. Re-query the database, write the actual list back. Here is the pattern.

Why I stopped trusting Bubble.io's list fields and re-query the database instead: cover image
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