Application data
Records that are structured, plus the fields that never quite fit a column.
One request reads rows, keys and joins alongside nested documents, instead of loading one side into the other.
- Rows for core records
- Documents for changing shape
- One statement across both
Data Infrastructure → Real-time data
What is happening now, and what led here.
Telemetry and events are stored with the application data, so history and current state are read from the same layer.
- Events and measurements
- Windowed reads
- No export between systems
Real-time Analytics → Operational reporting
Answers asked of live data rather than a nightly copy.
Aggregates run over the operational tables, so the numbers you report and the numbers you serve come from one place.
- Aggregates in place
- Document fields filterable
- One copy of the truth
Operational Analytics → Search and retrieval
Find what is similar, not only what matches.
Retrieval over the same layer avoids shipping documents into a second store to be searched: similarity becomes an access path over the data that already serves the application.
- Similarity search path
- Documents stay queryable
- One layer to keep in step
AI Retrieval → Connected data
What is reachable, and what depends on what.
Questions about relationships rather than records: expressing edges directly removes the reconstruction at query time.
- Nodes and edges as structures
- Traversal without rebuild
- Over data already in the layer
Knowledge Systems → Large objects
Files, media and artifacts that need to be found by query.
Objects are usually stored somewhere and described somewhere else. Keeping both in the layer means an asset is found by a query, not a convention.
- Big immutable objects
- Queryable metadata
- No separate catalogue
Document Intelligence →