Service
Batch, streaming and event-driven pipelines that hold up in production — with the data contracts, quality checks and observability that keep them holding up.
Who it is for
Problems we solve
How we help
Typical deliverables
Design-and-build, or a remediation engagement on pipelines that already exist and can no longer be trusted.
Technology fit
We are independent: no reseller agreements and no partner quotas. If your estate points a different way, we will say so.
Governance & production readiness
A pipeline is a governance surface whether or not you treat it as one. Lineage, quality and access are properties of the design, not a dashboard added afterwards.
Example outcomes
Anonymised and sector-level. Client names are withheld by design — the constraint and the architecture are the parts that carry any information.
Aviation
The constraint. Fuel transaction data feeding reconciliation, billing and reporting — where an error is a financial error, and must be traceable.
Batch and streaming pipelines with lineage, data quality checks and monitoring, replacing a reconciliation process that had been running on spreadsheets and trust.
Regulated data
The constraint. Governance that had been written down but never enforced, across an estate holding personal data.
Metadata cataloguing, lineage, RBAC, retention and automated data quality checks with named owners and a remediation path — so that a governance claim became something a regulator could be shown rather than told.
Questions
ETL transforms data before loading it; ELT loads it first and transforms it inside the warehouse. ELT is the modern default because cloud warehouses make transformation cheap and it keeps the raw data available when a definition changes — which it will. ETL still wins where data must be filtered or masked before it lands.
When a decision genuinely depends on freshness measured in seconds. Streaming is materially harder to build, test and operate, and most reporting requirements described as real-time turn out to mean hourly. We will ask what actually changes if the data is an hour old, and if the answer is nothing, we will recommend batch.
With declared data contracts and automated checks that fail the build rather than warn quietly, each with a named owner and a remediation path. A quality check nobody owns is a check nobody fixes, and a pipeline that degrades silently is worse than one that stops.
Systems communicate by emitting events rather than calling each other directly, so producers and consumers can change independently. It helps when you have many integrations, variable load, or a need for auditability — every state change leaves a record. It hurts when a simple synchronous call would have done.
A first call is technical, not a sales call. Bring a problem, an architecture, or a stalled initiative.
30 minutes, no pitch deck.