Research

The Data Operating System

What investment firms get on day one with Fencore DataHub — and how long it typically takes to handcraft an equivalent in-house, even with AI coding help.

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10–15 mo

Happy-path build to match the ops and governance layer

~2×

Time to go-live after you include gathering requirements and changing them later

Day one

What a platform like DataHub delivers on first go-live

Snowflake, Fabric, and Databricks move and store data. They do not give you a data operating system: approvals desks, alert triage, config promotion between environments, pipeline designers, and governance that follows the business — role-based access to data in a UI operators can use, and lineage that tracks values through transforms, not just tables. This analysis maps DataHub capabilities to the business outcomes they deliver — and indicative happy-path build effort to rebuild them in-house with AI-assisted development. Discovering the real operating requirements, and changing them after users first get their hands on the system, often roughly doubles time to production.

Beyond the warehouse
A lakehouse is not a substitute for multi-eye approvals, central alert management, configuration deployment with rollback, or governed master-data workflows.

Day-one operating layer
DataHub ships the desks and controls operations actually run: approvals, alerts, deployment with rollback, pipeline design, plus field-level access control and cell-level lineage across pipelines, alerts, and dashboards — not the partial picture you get when logic lives in SQL or Python outside the warehouse catalog.

Realistic in-house effort
Happy-path coding of that layer is typically 10–15 months for a small team with AI help — assuming the requirement is already known and stable. Discovery and post-go-live change often roughly double calendar time to a system operations will actually use.

Read the full capability and effort analysis online.

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