One trustworthy view of your data, no matter how many systems produce it

The engineering involves ETL or ELT pipelines built on tools fitted to your stack — SQL-centric transformation, Python services, or streaming pipelines where latency matters — landing in a warehouse or operational store your reporting tools can reach. Schema mapping is the heart of it: reconciling keys across systems, deduplicating entities, converting units, and encoding business rules like which system wins when two sources disagree. Manufacturing data adds its own texture: historian time series that must align with transactional lot records, shift calendars, and machine states that need interpolating onto production events.

In production, pipelines run under orchestration with lineage and freshness monitoring: you can see when each dataset last updated and trace any value to its source. Data quality checks — row counts, referential integrity, out-of-range detection — run on every load and fail loudly rather than passing bad data through. Success is measured in report reconciliation time going to zero and decisions being argued on interpretation instead of arithmetic.

Before pipelines are built, discovery produces an integration map: every source system, the datasets that leave it, the destination they land in, the refresh cadence, and the person who owns the data on each side. That last column matters most. A pipeline without a named owner for its source becomes an orphan the first time a schema changes. Where a source is a vendor-managed SaaS or a locked-down application, we extract only through the interfaces the vendor supports — APIs, scheduled exports, read replicas — so nothing we do jeopardizes their support or their upgrade path.

Each pipeline is tested on its own before being trusted in a report. Extractions are reconciled against source-system counts, transformations are unit tested against hand-verified examples, and the landed data is compared with the report your team already believes. Cutover is phased: new datasets run in parallel with the existing reporting until the numbers agree or the disagreements are explained, and only then does the old process stop. Rollback is simply reverting to the previous report while the pipeline is fixed. The delivered documentation lists every feed, its schedule, its checks, and who to call when one fails.