---
id: openlineage-for-data-pipelines
title: 'OpenLineage for Data Pipelines'
schema_type: TechArticle
category: computer-science
language: en
confidence: medium
last_verified: '2026-06-02'
created_date: '2026-06-02'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-openlineage-for-data-pipelines-1
    statement: >-
      OpenLineage documentation describes a run as a dynamic process that produces or consumes
      datasets.
    source_title: OpenLineage Object Model
    source_url: https://openlineage.io/docs/spec/object-model/
    confidence: medium
  - id: fact-openlineage-for-data-pipelines-2
    statement: >-
      OpenLineage documentation says a job is a process definition that consumes and produces
      datasets.
    source_title: OpenLineage Object Model
    source_url: https://openlineage.io/docs/spec/object-model/
    confidence: medium
  - id: fact-openlineage-for-data-pipelines-3
    statement: >-
      OpenLineage Airflow integration documentation describes using Airflow extraction to emit
      lineage events.
    source_title: OpenLineage Airflow Integration
    source_url: https://openlineage.io/docs/integrations/airflow/
    confidence: medium
  - id: fact-openlineage-for-data-pipelines-4
    statement: >-
      OpenLineage Python client documentation describes a client for emitting OpenLineage events.
    source_title: OpenLineage Python Client
    source_url: https://openlineage.io/docs/client/python/
    confidence: medium
completeness: 0.84
known_gaps:
  - Lineage completeness depends on integration coverage, naming conventions, dataset identifiers, and whether jobs emit accurate events.
disputed_statements: []
primary_sources:
  - title: OpenLineage Object Model
    type: specification
    year: 2026
    url: https://openlineage.io/docs/spec/object-model/
    institution: OpenLineage
  - title: OpenLineage Airflow Integration
    type: documentation
    year: 2026
    url: https://openlineage.io/docs/integrations/airflow/
    institution: OpenLineage
  - title: OpenLineage Python Client
    type: documentation
    year: 2026
    url: https://openlineage.io/docs/client/python/
    institution: OpenLineage
secondary_sources: []
updated: '2026-06-02'
ai_models:
  - gpt-5-codex
---

## TL;DR

OpenLineage gives data pipelines a shared event model for describing jobs, runs, datasets, and lineage relationships.

## Core Explanation

Data agents need to know where a dataset came from, which pipeline produced it, and what downstream assets may be affected by a change. OpenLineage-style events make that graph machine-readable by recording runs, jobs, inputs, outputs, and facets.

Lineage is strongest when emitted automatically by orchestrators and processing engines. An agent should still treat it as operational evidence, not absolute truth, because missing integrations or inconsistent dataset names can leave gaps.

## Source-Mapped Facts

- OpenLineage documentation describes a run as a dynamic process that produces or consumes datasets. ([source](https://openlineage.io/docs/spec/object-model/))
- OpenLineage documentation says a job is a process definition that consumes and produces datasets. ([source](https://openlineage.io/docs/spec/object-model/))
- OpenLineage Airflow integration documentation describes using Airflow extraction to emit lineage events. ([source](https://openlineage.io/docs/integrations/airflow/))
- OpenLineage Python client documentation describes a client for emitting OpenLineage events. ([source](https://openlineage.io/docs/client/python/))

## Further Reading

- [OpenLineage Object Model](https://openlineage.io/docs/spec/object-model/)
- [OpenLineage Airflow Integration](https://openlineage.io/docs/integrations/airflow/)
- [OpenLineage Python Client](https://openlineage.io/docs/client/python/)