Design a data platform your analytics can actually rely on — modelled properly, tested in the pipeline and documented end to end.
Business metrics defined once in the model instead of re-implemented per dashboard.
Version-controlled transformations with automated tests and full column-level lineage.
Freshness, volume and validity checks that alert before a report goes out wrong.
Partitioning, clustering and workload isolation tuned so cost tracks usage.
Access control, PII handling and retention designed into the platform, not retrofitted.
Models shaped for BI and ML consumption so downstream teams stop rebuilding joins.
Common questions we hear before starting a migration or architecture engagement — click a question to reveal a short, clear answer.
If your workload is mostly structured BI, a warehouse is simpler and cheaper to run. A lakehouse earns its complexity when you have semi-structured data, ML workloads or very large volumes. We size both against your actual data during discovery.
Usually not. Many engagements re-model and re-pipeline what you already have. We only recommend a platform change when the current one is the actual constraint.
Almost always because the same metric is calculated separately in each report. Moving those definitions into a governed model layer is the fix, and it is a core part of what we deliver.
Tests run inside the pipeline — freshness, row-count, uniqueness, referential and business-rule checks — so a failure stops the run and alerts the owner rather than silently publishing wrong data.
Yes. Where you already have tooling in place we build on it rather than replacing it, provided it is a reasonable fit for the target architecture.
PII is classified during source discovery, then handled with masking, column-level permissions and role-based access designed into the model. Retention rules are implemented in the platform.
Often, yes — most cost overruns come from unpartitioned scans and uncontrolled ad-hoc queries. We tune partitioning, clustering and workload isolation, and set up cost monitoring so regressions are visible.
Yes — event ingestion via Kafka or the cloud-native equivalent, landing into the same model as batch sources so downstream consumers see one consistent structure.
A focused first domain typically goes live in 6–10 weeks. Broader platforms are delivered domain by domain so value arrives before the whole programme finishes.
That is the intent. Everything is version-controlled and documented, and handover includes workshops on the model, the pipelines and the alerting.
