Column-Level Lineage (Open-Source Tools Explained)

Column-level lineage answers the question “Where did this column come from, how was it calculated, and who will be affected if I change it?” Unlike table-level lineage, which shows only dataset-to-dataset relationships, column-level lineage drills down to every field, mapping dependencies from raw sources through SQL, ELT tools, notebooks, and dashboards. This granularity is rapidly becoming a must-have for modern data teams that embrace self-service analytics, complex transformations, and stringent compliance requirements.

Why Column-Level Lineage Matters

Tracking data at the column level delivers tangible value in several areas:

• Impact analysis: Instantly identify downstream reports, ML features, or micro-services that rely on a column before altering or deprecating it.
• Root-cause analysis: When a metric looks off, trace the anomaly back through intermediate columns to the faulty transformation.
• Regulatory compliance: Prove how Personally Identifiable Information (PII) is transformed, masked, or redacted at each processing stage.
• Cost optimization: De-duplicate redundant computations and avoid unnecessary materializations by seeing where logic is repeated.
• Developer onboarding: New engineers can explore lineage graphs instead of reading stale wiki pages, reducing ramp-up time.

How Column-Level Lineage Works

Metadata Extraction

Lineage engines parse code (SQL, Python, Scala), observe runtimes (Spark execution plans, dbt manifests), or listen to logs (Snowflake QUERY_HISTORY) to capture transformation metadata. Each parsed statement produces “read” and “write” events at the column level.

Lineage Graph Construction

The events are stitched into a Directed Acyclic Graph (DAG) where nodes represent <dataset, column> pairs and edges describe transformation functions (e.g., SUM, JOIN, CAST). Open-source lineage frameworks typically persist this graph in a graph database (Neo4j), relational store (PostgreSQL), or even as JSON files.

Visualization & Querying

APIs or UI components expose the DAG so users can:

• Render interactive graphs
• Perform impact analysis ("Show me everything downstream of orders.total_amount")
• Search for orphan columns
• Detect circular dependencies

Key Open-Source Tools

1. OpenMetadata

Combines a metadata store, ingestion framework, and React UI. Column-level lineage is extracted via SQL parsers and database query logs. Supports Snowflake, BigQuery, Redshift, Postgres, dbt, and Spark.

2. Marquez

A spec-compliant implementation of the OpenLineage standard. Marquez receives OpenLineage events from jobs (Airflow, Spark, dbt) and builds a column-level graph stored in PostgreSQL.

3. DataHub

Originally created at LinkedIn, DataHub uses sqlglot for static SQL parsing and supports dbt’s manifest.json. Column-level edges are materialized in an Apache Kafka-backed metadata store.

4. Tokern Lineage Engine

Lightweight Python library that parses SQL with sqlparse + custom grammars. Good for embedding lineage into existing ETL codebases.

5. Spline (Spark Lineage)

Captures column-level lineage directly from Spark execution plans without parsing source code. Ideal for JVM-based big data pipelines.

Practical Example: Parsing dbt manifest to Build Lineage

from openmetadata.client import OpenMetadata
from openmetadata.ingestion.source.dbt import DbtSourceConfig, DbtSource

config = DbtSourceConfig(
manifest_path="./target/manifest.json",
catalog_path="./target/catalog.json",
project_dir="./",
target_dataset="analytics"
)

# Initialize OpenMetadata client
ometa = OpenMetadata("http://localhost:8585/api", auth_method="no-auth")

# Run ingestion
source = DbtSource(config, ometa)
source.prepare()
source.launch()


After ingestion, OpenMetadata’s UI lets you click any column and view upstream/downstream columns, SQL logic, owners, and tests derived from dbt.

Best Practices

• Adopt a standard schema such as OpenLineage so multiple tools (e.g., Airflow, Spark, Kubernetes) can emit compatible events.
• Centralize parsing logic: Prefer extracting lineage once—during CI build or orchestrator runtime—rather than duplicating parsers in every service.
• Version your metadata: Store lineage snapshots alongside code changes so you can diff graphs between commits.
• Govern PII at the column level: Tag columns as PII, SENSITIVE, etc., and propagate tags downstream automatically.
• Tie lineage to observability: Alert owners when a column with failing data quality tests feeds critical dashboards.

Common Misconceptions

• “Table-level lineage is enough.” In modern ELT where wide tables feed many derived models, table-level graphs hide critical dependencies.
• “Lineage requires heavyweight agents.” Static SQL parsing (e.g., dbt manifests) delivers rich lineage without production overhead.
• “Column lineage is only for compliance teams.” Engineers, analysts, and product managers all benefit from faster debugging and clearer ownership.

Where Galaxy Fits In

While Galaxy is primarily a modern SQL editor, its collaborative environment and AI copilot produce and modify SQL that defines your data lineage. By exporting executed queries or integrating with OpenLineage emitters, Galaxy can become a first-class producer of column-level metadata, ensuring that lineage graphs stay accurate—even when ad-hoc analysts iterate quickly in the editor.

Actionable Takeaways

1. Pick an open-source standard (OpenLineage or Egeria) before adopting a tool.
2. Start with dbt + OpenMetadata to capture 80% of SQL lineage with minimal setup.
3. Instrument your orchestrator (Airflow, Dagster) to emit runtime lineage for dynamic SQL.
4. Surface lineage in daily workflows—e.g., surfacing downstream impact warnings directly inside Galaxy when editing a query.
5. Continuously reconcile static (parsed) and runtime (observed) lineage for highest fidelity.