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Data pipelines

Move records, not mountains

Sync rows between stores, fan out events from queues, and keep warehouses fresh, with retries and logging built in.

The problem

The space between 'a cron script' and 'a data platform' is where most teams live: too small for Airflow, too important to lose silently.

After the flow

Pipelines that retry themselves, log every payload, and page you only when something actually breaks.

flows / signups-to-warehouselive
New row in signups
Enrich via API
Upsert to warehouse
Failures to #data

Three flows teams actually run

Kafka fan-out

Events from a topic are filtered, transformed, and delivered to three downstream systems with per-step retries.

Nightly reconciliation

Stripe payouts are compared against the ledger and discrepancies become a morning report, not a quarter-end surprise.

Sheet to source of truth

Edits in the ops team's Google Sheet validate and upsert into Postgres, so the sheet stays useful and the database stays right.

More patterns live in the docs, or describe yours to GoRunner AI and skip the reading.

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