Six engineering pillars define what makes a data platform truly modern: a lakehouse foundation, decoupled storage and compute, real-time and batch unified, active metadata, observability, and AI-ready semantics, available on cloud or on-prem with the same depth on every platform.
What makes a data platform "modern"
Here's our specific definition, six engineering pillars that, taken together, separate platforms that scale-and-evolve from platforms that lock you in.
Open table formats. ACID, schema, time travel.
Independent scaling. Multi-engine query.
One processing layer — no separate batch and streaming stacks to reconcile.
Lineage automated. Domain-owned data.
Pipeline SLAs. Audit-grade.
Consistent metrics. Vector + feature stores.
Six capabilities · End-to-end
The six capability areas we engineer across every modern data platform we deploy, whether cloud or on-prem, open-source or vendor-managed. The shape is the same; the tooling adapts.
The engineering organization
The engineers behind every platform we deploy. Four domains, each owning its roadmap, capability bench, and reference architectures.
Cloud-native data platform engineering.
On-prem scale, migration, modernization.
ML and agentic systems, in production.
Databricks-native delivery, end-to-end.
Let's talk.
Tell us what's in your data and AI stack, what's stalled, and what would change if it worked. We'll share what we've shipped against similar patterns in production, and what makes sense as a first step.
Our Hyperscaler & Strategic Partners
