OpenLineage defines a vendor-neutral API contract and data model for emitting lineage events as data moves through pipelines. It captures the inputs, outputs, and run facets of job executions, standardizing how tools like Apache Spark, Airflow, and dbt report their processing activities to any compatible backend.
Glossary
OpenLineage
An open standard and framework for collecting and propagating metadata about data lineage across job schedulers, query engines, and analytical tools.
METADATA COLLECTION STANDARD
What is OpenLineage?
OpenLineage is an open standard and framework for collecting and propagating metadata about data lineage across job schedulers, query engines, and analytical tools.
The standard uses a push-based model where instruments emit JSON events to a configurable transport layer, typically Apache Kafka or an HTTP endpoint. This decouples metadata collection from analysis, allowing a single integration to feed multiple observability platforms, catalogs, and governance tools without proprietary lock-in.
OPEN STANDARD FOR DATA LINEAGE
Key Features of OpenLineage
OpenLineage defines a vendor-neutral API and integration framework for capturing, collecting, and propagating metadata about data pipeline execution, enabling a complete and auditable view of data movement across diverse tools.
02
Extensible Facet Architecture
The core event model is augmented by a pluggable facet system, allowing producers to attach arbitrary, domain-specific metadata without breaking the standard.
- Schema Facet: Captures the full column-level schema of an output dataset
- Column-Level Lineage Facet: Maps input columns to output columns through transformation logic
- Data Quality Facet: Attaches quality metrics (e.g., row count, null percentage) to a dataset
- Assertion Facet: Records the outcome of data quality tests (pass/fail) against a dataset This extensibility makes OpenLineage a carrier protocol for the full Data Observability posture.
03
Integration via Native Instrumentation
OpenLineage is integrated directly into the runtime of popular data tools, emitting lineage events automatically without requiring external parsing of logs or SQL.
- Apache Spark: A listener plugin captures logical query plans and maps input-to-output datasets
- Apache Airflow: A dedicated extractor parses operator arguments to identify source and target tables
- dbt: An adapter emits model-level lineage as part of the dbt run process
- Flink & Kafka: Connectors capture streaming lineage for real-time pipelines This native approach ensures completeness and accuracy by hooking into the engine's internal catalog.
04
Backend-Agnostic Transport
The OpenLineage client library decouples event production from consumption. Events are emitted over a configurable transport layer to any compliant backend.
- HTTP: Synchronous POST of events to a lineage API (e.g., Marquez, Egeria)
- Kafka: Asynchronous streaming of events into a durable log for high-throughput pipelines
- Console: Debug output for local development and testing This separation allows organizations to swap lineage consumers without modifying pipeline code, adhering to a pluggable architecture.
05
Column-Level Lineage Support
Beyond table-level lineage, OpenLineage captures fine-grained column-level lineage through the ColumnLineageDatasetFacet. This tracks how specific output columns are derived from input columns via transformation expressions.
- Enables precise impact analysis:
OPENLINEAGE FAQ
Frequently Asked Questions
Clear answers to the most common questions about the OpenLineage standard, its architecture, and how it integrates with modern data platforms to deliver end-to-end lineage visibility.
OpenLineage is an open standard and framework for collecting and propagating metadata about data lineage across job schedulers, query engines, and analytical tools. It works by defining a standardized event model—emitted as JSON payloads—that describes the inputs, outputs, and runtime context of a data processing job. When a job runs, an integration (typically a library or an Airflow plugin) captures START, RUNNING, COMPLETE, or FAIL events and sends them to a configurable backend, such as Marquez, DataHub, or Apache Atlas. The core specification defines a RunEvent containing a Run (the job execution), a Job (the static definition), and Datasets (the inputs and outputs identified by namespace and name). This decouples metadata emission from consumption, allowing any tool that speaks the standard to participate in a unified lineage graph without custom point-to-point integrations.
LINEAGE STANDARDS COMPARISON
OpenLineage vs. Other Lineage Approaches
A technical comparison of OpenLineage against proprietary vendor solutions and manual lineage reconstruction methods for capturing and propagating data pipeline metadata.
| Feature | OpenLineage | Proprietary Vendor Tool | Manual Reconstruction |
|---|---|---|---|
Standardization | Open standard (Marquez spec) | Vendor-specific API | Ad-hoc documentation |
Integration Model | Event-driven, push-based via HTTP/OpenTelemetry | Agent-based or proprietary SDK | SQL parsing and log scraping |
Granularity | Column-level lineage | Table-level (typical) | Table-level (best effort) |
Extensibility | Custom facets and run events | Limited to vendor plugin ecosystem | None |
Cross-Platform Support | Native integrations: Spark, Airflow, dbt, Flink, Snowflake | Limited to vendor ecosystem | Theoretical, high effort |
Real-Time Propagation | |||
Schema Change Tracking | Facet-based schema metadata capture | Vendor-dependent | |
Operational Overhead | Low: library injection | Medium: agent management | High: manual parsing and stitching |
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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