BigData warehouse: ingest from sources (CDC, APIs, events), process via batch/stream pipelines, serve curated data to analytics dashboards.
Data Processing Platform
Reference architecture for a modern data platform — collects data from operational sources, lands it in a lakehouse, transforms it via batch and streaming pipelines, governs it, and serves it to analysts and BI dashboards.
This example project demonstrates the IOModel approach to Architecture as Code by expressing a complete reference architecture as a tree of YAML files alongside MDX documentation. Open the Explore tab to navigate the model interactively.
What you will find here
- Architecture — full system context, data architecture, integrations, deployment, quality attributes, plus the Actors and Modules reference pages.
- Workflows — main end-to-end flows backed by live sequence diagrams in the model.
- Scenarios — concrete edge cases used as design and test references.
- Features — spec-first feature development with model embeds.
Top-level systems
Ingestion
CDC, batch APIs and event streams into the lakehouse.
Storage
Bronze lake, gold warehouse and metadata catalog.
Processing
Batch and streaming pipelines orchestrated end-to-end.
Serving
Query engine and semantic layer for consumers.
Analytics UI
Dashboards, notebooks and exploration workspace.
Governance
Catalog, lineage, quality and access control.
How the model is structured
The model uses three nesting layers:
- Top layer — systems / subsystems / domains and actors of the product. Cross-system links describe high-level integrations.
- Container layer — each system decomposes into service-containers (services, apps, libraries). Containers can link to other containers across systems.
- Component layer — each container holds components (libraries, internal modules). Component links are internal-only and never cross container boundaries.
Every object has a Guide on its Overview tab; services with APIs include OpenAPI specs; storage components include ERD diagrams.
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