Data flows
Data pipelines that survive change.
Zerg builds extraction, transformation, validation, and monitoring loops around data sources whose schemas and semantics keep shifting.
Evidence
schema
pipelines generated around live contracts
checks
quality gates attached to every flow
replay
alerts and replay paths included
The problem
The hard part is the interface between systems.
Data systems fail quietly. A renamed column, a missing enum, or a changed upstream export can poison dashboards before anyone sees a stack trace.
What we deploy
A loop that can keep operating after delivery.
- 01
Model the source systems, schema contracts, business rules, destination tables, validation queries, and failure handling.
- 02
Generate loaders, transforms, reconciliation checks, backfills, and reporting views with observability built in.
- 03
Use drift and data-quality signals to trigger repair work before stale data becomes a business decision.
Who this is for
Data teams, operations-heavy businesses, and analytics teams where stale or malformed data creates real downstream cost.
Surface area
Data flows