Data Pipeline
An automated sequence of steps that moves and transforms data from sources to a usable form.
Definition
A data pipeline is an automated workflow that ingests data from sources, transforms it (cleaning, joining, aggregating), and delivers it to a destination such as a database or model. The classic pattern is extract, transform, load (ETL).
The distinction between batch and streaming pipelines shapes the architecture: batch jobs process data in scheduled chunks, while streaming systems handle records continuously as they arrive. Many modern systems combine both to balance latency against throughput and cost.
The value of a pipeline lies in its reliability and reproducibility: given the same inputs it should yield the same outputs, failures should be visible, and re-runs should not corrupt data. The batch-versus-streaming distinction shapes the architecture, with batch systems processing scheduled chunks and streaming systems handling records as they arrive. Because analysis and machine learning are only as trustworthy as their inputs, disciplined pipelines are foundational to credible results.
Properties of a good pipeline
- Reproducible: the same inputs yield the same outputs.
- Observable: failures are detected and logged.
- Idempotent: safe to re-run without corrupting data.
- Versioned so results can be traced to inputs.
Why it matters
Reliable analysis and machine learning depend on reliable data delivery. A well-built pipeline turns messy, scattered raw data into consistent inputs, and its reproducibility is what makes downstream results trustworthy.
Fusion connection
Kronos data pipelines carry raw simulation output through cleaning and feature extraction into the datasets that train surrogate models, with every stage logged for reproducibility.