Data Analytics

Reporting straight from production databases works while volumes are small, but it competes with the application for resources and breaks whenever the schema changes. A warehouse such as Snowflake, BigQuery or managed PostgreSQL separates analytical load from transactional load and lets you model history properly. We assess volume, query patterns and refresh expectations before recommending either route.

By fixing definitions before pipelines. Each metric is documented in one place with its calculation and an owner, so an 'active customer' means the same thing in finance and in sales. Pipelines then enforce it: tests on row counts, nulls, uniqueness and referential integrity run on every load, and failures block publication rather than quietly producing a dashboard that is wrong.

That is most of the work. We build ingestion from databases, SaaS APIs, files and event streams into a staging layer, then model it with shared keys so a customer or an order can be followed across systems. Airflow handles scheduling, retries and dependencies, and lineage is recorded so any figure on a dashboard can be traced back to its source record.

Your team, with what they need to do it. Pipelines are version-controlled code rather than clicks inside a vendor tool, transformations are tested and documented, and dashboards sit on governed models instead of ad hoc extracts. We run knowledge transfer on the models and the orchestration, and an optional support arrangement is available if you would rather we stayed involved for a defined period.