Systems come and go; your data model should not. DataOps designs ERP-agnostic canonical models, federated mesh architectures, and curated Bronze, Silver, and Gold layers that turn a sprawl of legacy systems into one governed foundation for reporting and AI.
The pattern proven at $40B scale: a shared model at the center, regional platforms serving the business, and every layer curated before anyone reports on it.
One ERP-agnostic model across 8 business domains, so every source system speaks the same language when it lands.
A central Databricks lakehouse holding shared standards and models, with regional Snowflake environments serving data close to the business.
Bronze, Silver, and Gold layers that take data from raw to certified, 400M records curated on the pattern to date.
Snowpark and Matillion pipelines feeding the platform, plus automated customer data exchange replacing manual file handoffs.
One standard model every source maps into. We consolidated 7 ERPs into 1 across 8 domains, so reporting survives any system swap.
Central standards, regional autonomy: a Databricks lakehouse at the core with regional Snowflake environments serving the business.
Medallion layers that move data from raw to certified, proven on 400M records, so consumers only ever touch trusted data.
28 defunct systems decommissioned and 15 archived, with Access and Excel processes retired into governed Power BI.
Governed, automated exchange with customers that replaces manual extracts and one-off file handoffs.
Snowpark and Matillion pipelines engineered for reliability, so the platform runs without an army of manual jobs behind it.
Data architecture is the design of how data is modeled, stored, moved, and served across an enterprise: the models that define what data means, the platforms that hold it, the pipelines that move it, and the curation layers that make it trustworthy enough to report and build AI on.
A canonical data model is a single standard model that every source system maps into, so reporting and analytics no longer care which ERP a record came from. At a $40B supplier, we consolidated 7 ERPs into 1 canonical model across 8 business domains.
A federated data mesh combines a central platform with regional autonomy: shared standards, models, and governance at the center, with regions serving their own data close to the business. We have delivered this with a central Databricks lakehouse and regional Snowflake environments.
We inventory the full landscape, then give every system a verdict: decommission, archive, or migrate into the modern platform. At a $40B supplier that meant 28 defunct systems decommissioned, 15 archived, and Access and Excel processes moved into governed Power BI.
Tell us what your system landscape looks like today. We'll show you the canonical model underneath it, and the path from legacy sprawl to one governed platform.
