How is The Timeseries Refinery different from InfluxDB or TimescaleDB?
InfluxDB and TimescaleDB are built for fast ingestion of points, and every query requires SQL, Flux or InfluxQL. What they keep is the current state of each series — past states are not reconstructible. The Timeseries Refinery is append-only at the version level: every insert creates a new version of the series, and all versions stay queryable. It also adds a traceable formula engine on top, usable by analysts entirely through the UI. It is a complete platform rather than a storage engine.
How is it different from Databricks or Dataiku?
Databricks and Dataiku are general-purpose data platforms: powerful, but they require a data engineering team to model and expose time series data before analysts can work. The Timeseries Refinery is specialised: a named series catalog, versioning and formulas are built in, and it is operational in hours — no data engineering team required.
Do analysts need to write code or know SQL?
No. Formulas, series management and dashboards are fully accessible through the web interface. Analysts who do code can use the Python API — every operation available in the UI is also available programmatically — but it is never required.
What is a versioned (bitemporal) time series?
A bitemporal series records two timelines: when a value applies, and when it became known. Every insert is kept, so you can query any series as it was known at any past date — for example, replay yesterday's price forecast exactly as it stood before this morning's revision. Computed series follow the same law: formulas evaluate against the versions known at the requested date.
Is The Timeseries Refinery really open source? Under what license?
Yes — the components are licensed under LGPL-3.0, and developed in the open in the Timeseries Refinery group on Heptapod. The free edition is published on PyPI; the pro components (dashboarding, series monitoring, access rights, MCP server) are open source as well, delivered with a subscription.
Can I self-host it, or is it SaaS only?
Both. Pythonian operates SaaS instances on EU-based cloud services with daily backups, or you deploy on your own infrastructure — MS Azure, AWS, Google Cloud Platform or plain Linux servers.
How long does deployment take?
The platform is Docker-based and operational in hours. No data engineering team is needed before analysts can start working with series.
Can AI assistants work with the data?
Yes. The platform includes an MCP server: any MCP-compatible AI assistant can search the catalog, query series, and build and evaluate formulas in natural language. The results follow the same rules as everything else — versioned and traceable.
Does it integrate with Excel?
Yes. The Excel client uploads and downloads series directly from the shared catalog, so teams keep their Excel workflows while the data itself stays versioned, named and traceable in the Refinery.
What data sources can it connect to?
An open source data catalog covers ENTSOE, ENTSOG, RTE, ECMWF, ERA5, Yahoo Finance and more. ARK connectors integrate commercial market data providers without development. Custom sources are integrated as Python tasks through the built-in scheduler.