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.
• 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.

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.

Recommended Pages

• System context diagram
• Implementation planning decisions
• Runtime infrastructure and operating system environment
• Connector and ingestion patterns
• Suggested tooling by category
• Raw data store, Operational Data Store, Data Vault, and Data Mart layers
• Database and persistence recommendations
• Orchestration and recovery patterns
• Custom application integration patterns
• Dashboard, application, and visualization consumption patterns
• Platform positioning and differentiation
• Access, identity, and authorization model
• Observability and reliability model
• Environment strategy and GitOps deployment model

Ownership

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