Enterprise Data Platform

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Architecture ​

Enterprise Data Platform is not a single tool. It is a collection of tools paired with a shared data lifecycle for collecting, processing, historizing, and publishing cross-system operational data.

Tool Categories ​

  • Connectors and data movement tools bring data in from source systems and external services.
  • Orchestration and workflow tools schedule, coordinate, monitor, and recover platform workflows.
  • Storage, transformation, quality, catalog, and governance tools turn raw data into trusted products.
  • Secrets-management and policy tools keep credentials, access decisions, and publication gates explicit.
  • Consumer tools, APIs, and custom applications expose governed data to users and workflows.

Data Lifecycle ​

Data is collected across systems into a raw data store. From there, it is normalized, correlated, and enriched into an Operational Data Store. Dimensional modeling and historical processing are then applied into a Data Vault. The Data Vault is pared down into focused Data Marts for reporting, dashboards, applications, and downstream analytics.

Trust services run alongside this lifecycle. OpenMetadata documents assets and lineage, Great Expectations records validation evidence, OPA evaluates policy decisions, and OpenBao provides a local pattern for keeping service credentials and API secrets out of source control.

Architecture Notes ​

Use this section to explain how the platform is assembled, how data moves through each layer, and where teams should look before changing shared platform behavior.

Ownership ​

Create a clear owner map for platform services, datasets, deployment pipelines, and operational runbooks. Good ownership docs make incidents shorter and onboarding calmer.