tStore
Welcome to the journey from Visual KPI to the all new Transpara real-time operational intelligence platform. The new platform is already being battle-tested with major customers (let us know if you want to take part), but is also still in development. The documentation will be updated over time as new features are released. Stay tuned for more updates!
tStore is the Transpara Platform's time-series data engine. It provides flexible, high-performance storage for streaming and historical data, acting both as an analytics cache and an optional primary data source.
Built on TimescaleDB, tStore is optimized for long-running, high-frequency, and large-volume calculations. It enables the platform to deliver fast, consistent results, whether working with real-time sensor streams or batch-imported datasets.
What it does
tStore is responsible for:
- Storing time-series and performance data from multiple sources.
- Caching results from real-time calculations and analytics.
- Serving as an optional long-term database when live access is unavailable or impractical.
- Reconstructing historical views using static files, spreadsheets, or other archived data.
How it works
tStore operates as a scalable storage layer within the platform. It receives inputs from:
- Live data sources (via pass-through interfaces and extractors).
- Processed results from tCalc.
- Uploaded or batch-imported files (CSV, JSON, text logs).
Data is organized into high-resolution time-series tables, indexed for fast querying. The system automatically aggregates data based on frequency, use case, and retention settings, allowing analytics to scale without manual tuning.
tStore integrates directly with:
- tCalc for on-demand and event-driven computations.
- tModel to ensure stored data is contextually mapped to your semantic model.
- tStudio for managing storage settings, retention, and upload options.
Main features
| Feature | Description |
|---|---|
| Built on TimescaleDB | Combines SQL flexibility with time-series performance. |
| Optional persistence | Store data when needed; skip it when live data is sufficient. |
| Analytics cache | Accelerates repeated calculations by storing prior results. |
| History from files | Import CSV, Excel, JSON, or text to recreate past performance data. |
| Automatic aggregation | Supports downsampling and long-period queries without performance loss. |
| High availability | Can be scaled and replicated in container-based deployments. |
| Supports structured queries | Easily accessed by tCalc, dashboards, or external tools. |
Architecture diagram
Figure: Role of tStore in the Transpara architecture (Insert relevant section of the April 2025 or Architecture diagram showing tStore and its connections to tCalc, tModel, and interfaces)
When to use tStore
Use tStore when you need to:
- Store and analyze large volumes of time-series data over time.
- Reuse previously calculated results for performance optimization.
- Preserve and visualize historical performance from non-streaming sources.
- Support dashboards that must display recent trends or full history.
What's next?
Learn how real-time logic is applied using tCalc, or go back to the Core Modules page.