Data Pipelines and Provenance
A data pipeline moves and transforms data through defined stages; provenance records where every value came from.
Data in motion
Between a sensor reading and a plotted result lie many steps: acquisition, calibration, cleaning, transformation, analysis. A data pipeline organizes these into defined, repeatable stages so the path from raw signal to conclusion is explicit rather than ad hoc.
Why provenance matters
Provenance is the recorded lineage of a piece of data: which raw inputs, which processing steps, which software versions produced it. Without provenance, a suspicious number cannot be traced back to its source, and a corrected input cannot be propagated forward with confidence.
What good provenance captures
- The origin of every input and its calibration.
- Each transformation applied, and its parameters.
- The code version and environment used at each stage.
- Timestamps and, where relevant, the operator or process responsible.
Reproducibility through pipelines
A well-defined pipeline is reproducible by construction: rerun it on the same inputs and get the same outputs. This turns data analysis from a manual craft into an engineered process that a second person can audit and repeat, which is a precondition for defending any result.
In a fusion plant
Every experimental shot and every simulation produces data that will inform design and operations for years. Building pipelines with provenance from the start means that when a later question arises — why does this component have these loads? — the full chain from source data to conclusion is available to answer it.