Examples

Ingesting SAWS rainfall stations and SA-Flood-Radar products into an analytics database

Pulling DWS river-level telemetry into a reservoir-management dashboard

Normalising climate-model outputs into scenarios usable by an internal modelling pipeline

Integrating SCADA historian data into regulatory reporting flows

Who it’s for

Organisations that have more data than they can usefully use. Telemetry sitting in a historian that nobody can easily query. Rainfall feeds that the modelling team re-ingests by hand for every project. Gauged flow data that takes a fortnight to reach a report.

What you get

An ingestion pipeline that pulls from your sources on the schedule they actually publish.
Cleaned, versioned data in a store your team can query — typically PostgreSQL/TimescaleDB, Parquet files in object storage, or a light data warehouse.
Monitoring so you know when a source is late or broken, rather than discovering it two weeks later.
Documentation: where data comes from, how it is transformed, how to add a new source.
Optional API layer if downstream apps need it.

How we work

Most data-pipeline work is a retainer arrangement rather than a one-off scoped project, because sources change, formats change, and a pipeline without ongoing attention rots. Retainers are capped and transparent; the alternative is a project that looks finished at go-live and breaks two quarters later.

Source inventory. What you have, what you want, what is actually available from each source (they are rarely the same thing).
Prototype pipeline on a narrow slice. One source to one destination, end-to-end, in weeks not months.
Expand coverage and add monitoring. As confidence grows, add the rest of the sources. Monitoring and alerting are first-class, not afterthoughts.
Retainer or handover. Handover is possible if you have the in-house team to run this. Otherwise a small retainer covers source changes, monitoring response, and new-source onboarding.

Stack & interoperability

Orchestration: Airflow, Prefect, or plain cron — whichever matches your ops model.
Transformation: Python (pandas, polars, xarray for raster data), SQL.
Storage: PostgreSQL + TimescaleDB, Parquet in object storage, netCDF for raster time series.
Sources we have integrated with before: SAWS, DWS hydrological telemetry, SA-Flood-Radar, ERA5, CMIP climate data, SCADA historians (OPC-UA or vendor APIs), vendor telemetry hardware.

What this is not

Not a data lake in a box. We build specific pipelines for specific purposes. If you want a generic platform, there are vendors for that.
Not a substitute for owning your data. We build the pipelines; your team owns the data.

Data Pipelines & Integration