02 — Capability
Data infrastructure that can explain itself.
Volume is the easy part. The hard part is being able to answer, months later, exactly how a number was produced and whether it can be trusted. We build platforms where that answer is one query away.
The stack
Four layers, each independently testable.
Ingestion
Batch loads and streaming capture from APIs, databases, object stores, and event brokers. Idempotent by design, so a replay never doubles a number.
Modelling
Warehouse or lakehouse layouts sized to the query patterns that matter. Transformations live in version control and are tested like application code.
Quality
Contracts on the inputs, assertions on the outputs, and failures that stop a pipeline rather than quietly publishing a wrong table.
Serving
Query layers for analysts, feature access for models, and extracts for downstream systems — reading from one governed source rather than four copies.
Non-negotiables
What we insist on, regardless of scale.
These are the properties that decide whether a platform is still usable in year three. They cost very little to build in at the start and are painful to add later.
- Lineage
- Every published field traces back to the rows that produced it
- Backfills
- Reprocessing is a routine operation, not an incident
- Cost
- Storage and compute are budgeted and monitored, not discovered on the invoice
- Access
- Row- and column-level controls enforced at the warehouse, not in the dashboard
Already have a pipeline that nobody trusts?
That is a common starting point and a solvable one. We audit what exists, quantify where it diverges from the source, and give you a migration path that does not require a freeze.