Analytics that ships the day it is spec'd
Replaced a bespoke ETL stack with a typed, schema-driven pipeline so a new metric goes from request to production dashboard in an afternoon.
- Analytics
- Schema-driven
- TypeScript
- CDK
- 6 wks → 1 day
- New metric lead time
- −74%
- Pipeline LOC
- 100%
- Schema coverage
A new metric is authored as a typed schema definition and committed; CI validates it and generates the transform pipeline, which deploys through dev → staging → prod. Publishing a metric is a pull request, not a ticket — which is how a six-week turnaround became about a day.
Every new chart at Meridian was a small project: a data engineer hand-wrote extraction, a backend engineer reshaped it, a frontend engineer rebuilt the types, and the three drifted apart the moment anything changed. A simple new metric took six weeks, and breakages were found in production by customers.
We made the schema the program. Each metric is declared once as a typed definition; from that single source we generate the ingestion contract, the storage projection, the API types, and the client-side hooks. A change to a definition fails the build everywhere it is now inconsistent — so drift is caught at compile time, not by a customer. The runtime is a thin serverless pipeline on EventBridge and DynamoDB, deployed as a single CDK app per environment.
A new metric is now an afternoon: declare it, open a pull request, and the pipeline, storage, and dashboard follow from the type. The hand-written pipeline code shrank by nearly three-quarters, schema coverage is total, and the recurring class of production type-mismatch bugs simply stopped happening.
Let’s write the next system into being
Tell us what you’re building. We’ll reply within two business days with a frank read on scope, shape, and whether we’re the right studio for it.