Data Pipeline

A data pipeline is a chain of automated steps that takes data from where it originates to where it is used. NIST's Big Data Interoperability Framework describes the stages as the data analytics life cycle: "the set of processes that is guided by the organizational need to transform raw data into actionable knowledge, which includes data collection, preparation, analytics, visualization, and access" (NIST SP 1500-1r2, Volume 1, Definitions).

The stages

NIST's reference architecture (NIST SP 1500-6r2) walks through them:

1. Collection. This step "handles the interface with the Data Provider," and it may be a service "designed to pull data or receive pushes of data from the Data Provider."
2. Preparation. Validation, cleaning, and standardization; this is the transform step covered in detail under ETL.
3. Analytics. This step "implements the techniques to extract knowledge from the data based on the requirements of the vertical application."
4. Visualization and access, which deliver results to people and other systems.

Batch and streaming

Some pipelines run on a schedule; others process data as it arrives. NIST says streaming frameworks "are built to deal with data that requires processing as fast or faster than the velocity at which it arrives," and that they "implement some form of basic workflow processing for the streams." Delivery guarantees differ. Under the simplest, "at-most-once" class, messages or events may be "dropped if there is a failure in processing or communications or if they arrive out of order." Under "at-least-once" delivery, "messages or events may be processed more than once and also may arrive out of order." A pipeline can lose or duplicate records unless it is designed not to.

Provenance

NIST: "Provenance type of metadata provides the history of the data so users can correctly interpret the data, especially when the data is repurposed from its original collection process to extract additional value."

Examples

• The Library of Congress built "a new OCR reprocessing pipeline called NDNP-Open-OCR" to regenerate searchable text for digitized newspapers. See Optical Character Recognition.
• In machine learning, NIST describes a "training stage" and a "deployment stage" of "the machine learning pipeline" (NIST AI 100-2e2025). See Machine Learning.

Getting data from a source often runs through an API; see API. Automating the business steps around the data is covered under Workflow Automation.

Why it matters for land and mining claim records

A table built by a pipeline is only as current as its last run and only as complete as its source pull. Provenance is what lets a reader see which source, and which date, a given record came from. Without it, a missing or duplicated mining claim record cannot be traced back to the step that caused it.