Open-Source Tools That Offer Column-Level Lineage

Column-level lineage provides field-by-field visibility into data flows, helping teams debug pipelines faster, meet regulatory requirements, and build trust in analytics.

What Is Column-Level Lineage?

Data lineage is the record of how data moves and changes from its origin to its final destination. Column-level lineage takes this one step deeper by mapping transformation logic for each individual column rather than at the coarse table or dataset level. Instead of saying “table orders_daily comes from orders_raw,” column-level lineage can tell you that:

• orders_daily.order_id is a direct copy of orders_raw.id
• orders_daily.revenue_usd is derived from orders_raw.price_cents / 100
• orders_daily.source_country comes from a lookup inside dim_countries

This granular map is invaluable for impact analysis, debugging, governance, and cost-efficient pipeline design.

Why Does Column-Level Lineage Matter?

• Faster incident response: When a dashboard breaks, engineers can pinpoint the exact upstream field rather than sifting through dozens of tables.
• Regulatory compliance: Frameworks like GDPR and HIPAA require knowing where sensitive columns such as email or ssn flow.
• Optimized transformations: Seeing unused columns lets you trim expensive queries.
• Trust & data literacy: Analysts can self-serve impact analysis without pinging engineers.

Core Capabilities Required

1. Parser-level understanding of SQL to extract column dependencies (CTEs, functions, conditionals).
2. Runtime metadata capture (e.g., via Spark, Airflow, dbt, Flink) so lineage matches what actually ran.
3. Storage & graph layer to persist and query lineage relationships.
4. Visualization & API for impact analysis, governance workflows, and embedding into developer tools.

Top Open-Source Tools That Provide Column-Level Lineage

1. DataHub

Originally open-sourced by LinkedIn, DataHub stores metadata in a graph database and offers automated column-level lineage if you ingest:

• dbt manifests (dbt parses SQL for you)
• Snowflake or BigQuery query logs
• Spark plans via spline integration

DataHubs React UI renders a lineage graph where each column can be expanded to show parents and children.

2. OpenMetadata

Backed by the CNCF, OpenMetadata includes a built-in SQL parser and supports column-level lineage for more than 20 databases and orchestrators. It captures lineage through:

• Query log ingestion (Snowflake, Postgres, BigQuery, MSSQL, etc.)
• Runtime interceptors for Spark and Airflow
• dbt artifact imports

The UI highlights column-to-column edges and allows tag propagation for PII or data quality scores.

3. OpenLineage + Marquez

OpenLineage is an open standard for lineage events. Marquez is the reference implementation that stores and visualizes them. Recent versions include columnLineage in the JSON spec, allowing integrations to emit field-level edges. Spark, dbt, Airflow, and Flink emitters already support this.

4. Apache Atlas

Atlas provides governance and classification for the Hadoop ecosystem. The Hive hook and Spark Atlas Connector can emit field-level lineage into Atlas
9s graph backed by JanusGraph or Solr. UI support is less modern than DataHub/OpenMetadata but still functional.

5. Spline

Spline (Spark Lineage) captures execution plans from Apache Spark and stores column transformations in a MongoDB / ArangoDB graph. While Spark-specific, it excels at deep lineage for complex DataFrame operations.

6. Tokern Lineage Engine

Tokern, built on sqlparse, ingests query history from Snowflake, Redshift, Postgres, and BigQuery to compute column-level lineage. A minimal UI and Airflow operator make it attractive for teams wanting a lightweight solution.

7. SQLLineage

sqllineage (Python) can parse SQL strings and output column dependencies. It19s more of a library than a platform but great for embedding lineage directly in CI checks or notebooks.

Honorable Mentions

• Amundsen: Table-level by default; plugins can push column edges.
• WhereHows: An older LinkedIn project superseded by DataHub.
• Google
  9s Dataplex Lineage: Not open source but integrates with OpenLineage standard.

Choosing the Right Tool

Consider:

• Ecosystem fit: Spark-heavy = Spline; dbt-first = DataHub or OpenMetadata.
• Operational burden: Marquez and Tokern are lightweight; Atlas can be complex.
• Community & releases: DataHub and OpenMetadata have active Slack channels and weekly improvements.
• Visualization needs: Field-level graph UIs vary widely.

Best Practices for Implementing Column-Level Lineage

1. Start with critical pipelines such as finance or growth metrics. Avoid boiling the ocean.
2. Emit lineage at runtime rather than re-parsing historical SQL where possible. You19ll capture dynamic SQL and UDFs.
3. Integrate with CI/CD so every new DAG, dbt model, or SQL migration pushes lineage automatically.
4. Tag sensitive fields at the source to propagate compliance labels downstream.
5. Store lineage in a queryable graph (e.g., Neo4j, JanusGraph) so you can answer impact questions like “If I drop orders_raw.vat_rate who screams?”

Common Pitfalls and How to Avoid Them

• Relying only on static parsing: Fails on dynamic SQL, macros, runtime conditionals. Fix by capturing execution plans or query logs.
• Ignoring Versioning: Lineage without dataset version context causes flaky audits. Store schema versions alongside edges.
• Over-retention of history: Keeping every edge forever balloons storage and slows queries. Implement TTL or partitioned storage.

End-to-End Example Using OpenMetadata + dbt

1. Enable the openmetadata_dbt ingestion pipeline to read target/manifest.json and target/run_results.json.
2. Run your dbt project:dbt run && dbt test
3. In OpenMetadata UI, navigate to Lineage > Models. Expand customers_lifetime_value.ltv to see its upstream columns (orders.amount_usd, payments.fee, etc.).

Galaxy Integration

Galaxy19s SQL editor stores rich query history and metadata. While Galaxy does not yet generate lineage graphs, its APIs can export executed SQL statements and parameter values. You can feed those logs into OpenLineage or Tokern to achieve column-level lineage without changing your workflow. Future roadmap items like built-in data catalog features may surface lineage directly inside Galaxy19s UI.

Key Takeaways

• Column-level lineage delivers fine-grained visibility essential for governance and debugging.
• Open-source options like DataHub, OpenMetadata, and OpenLineage make adoption feasible without vendor lock-in.
• Successful rollouts focus on runtime capture, incremental adoption, and tight CI/CD integration.
• Galaxy users can integrate query logs with lineage platforms today and look forward to native support in the future.