Visualizing Data Lineage with Marquez

Marquez is an open-source metadata service that captures, stores, and visualizes data lineage. By emitting OpenLineage events from your pipelines and sending them to a Marquez backend, you gain a continuously updated graph of datasets, jobs, and their relationships. This article walks through the underlying concepts, shows how to get lineage into Marquez, and demonstrates multiple ways to visualize that lineage for debugging, governance, and collaboration.

What Is Data Lineage?

Data lineage is the record of how data moves and transforms through an organization—from its origin, through each processing step, to its final destination. It answers questions such as:

• Where did this field come from?
• Which jobs depend on this table?
• If I change a column name, who will be affected?

Visualizing lineage turns this metadata into an interactive graph, making complex dependencies easy to understand.

Why Use Marquez for Lineage Visualization?

Marquez focuses on three pillars:

1. Open Standard: It implements the OpenLineage specification, a vendor-neutral JSON schema for lineage events.
2. Real-Time Capture: Instrumented jobs emit lineage during execution, so the graph is always up to date.
3. First-Class UI & APIs: A web UI renders an interactive DAG, while REST and GraphQL endpoints let you query lineage programmatically.

Together, these capabilities let you observe dataflows, trace incidents, and meet compliance requirements.

End-to-End Walk-Through

1. Spin Up Marquez Locally

# docker-compose.yml
version: '3.9'
services:
postgres:
image: postgres:14
environment:
POSTGRES_USER: marquez
POSTGRES_PASSWORD: marquez
marquez:
image: marquezproject/marquez:latest
ports: ["5000:5000", "3000:3000"]
environment:
MARQUEZ_DB_USER: marquez
MARQUEZ_DB_PASSWORD: marquez
MARQUEZ_DB_HOST: postgres
MARQUEZ_PORT: 5000
depends_on: [postgres]


Run docker compose up. The API is now on localhost:5000 and the UI on localhost:3000.

2. Emit OpenLineage Events

Add the openlineage-python client to a pipeline. For example, inside an Airflow DAG:

from openlineage.airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def extract():
# fetch from source
pass

def transform():
# heavy lifting
pass

def load():
# write to warehouse
pass

dag = DAG(
dag_id="etl_quickstart",
start_date=datetime(2024, 1, 1),
catchup=False,
)

for task_id, func in {"extract": extract, "transform": transform, "load": load}.items():
PythonOperator(task_id=task_id, python_callable=func, dag=dag)


Set the following environment variables so Airflow will send lineage to Marquez:

OPENLINEAGE_URL=http://localhost:5000
OPENLINEAGE_NAMESPACE=demo
OPENLINEAGE_API_KEY= # leave blank for local


Each task run now posts START, COMPLETE, or FAIL events to Marquez containing inputs, outputs, schema, and run metadata.

3. Explore the Web UI

Open http://localhost:3000 and select the demo namespace. You will see:

• A list of jobs with run status badges
• Dataset detail pages showing schema and historical runs
• An interactive lineage graph where you can pan, zoom, and click nodes to expand dependencies

4. Programmatic Lineage Queries

Need lineage inside automated checks or docs? Use the REST API:

curl -X GET \
"http://localhost:5000/api/v1/namespaces/demo/lineage?node=demo:dataset:public.orders&depth=3" | jq


This returns JSON containing nodes and edges suitable for rendering with D3, GraphViz, or internal tooling.

Best Practices for Clear Lineage Diagrams

Instrument at the Right Level

Emit lineage at the job boundary that has business meaning (e.g., ETL step, dbt model) rather than every helper script. This keeps the graph readable.

Annotate Schemas

Include column-level facets such as schema or columnLineage. Marquez aggregates these so you can inspect field-level impact.

Use Consistent Naming

Choose a namespace convention (team.project) and stick to it. Lineage graphs are only intuitive when dataset IDs follow a pattern.

Automate Deployment

Run Marquez in Kubernetes with Helm or deploy the marquez-chart so that lineage is always available in staging and prod.

Common Misconceptions

"Marquez replaces my catalog"

Marquez focuses on lineage capture. While it exposes dataset metadata, it’s not a full catalog like DataHub. Many orgs integrate Marquez with a catalog, feeding lineage via Kafka.

"Lineage slows pipelines"

The OpenLineage emitters are non-blocking HTTP calls executed after job state transitions. Overhead is typically under 50 ms per event.

"Only Airflow is supported"

There are official emitters for Spark, dbt, Flink, Dagster, Kubernetes Jobs, and custom apps via the API. Any tool can send events if it can POST JSON.

Galaxy Integration

Galaxy is a SQL editor and therefore is tangential to Marquez. If your team uses Galaxy to develop SQL transformations that later run in dbt or Airflow, emitting OpenLineage events from those jobs will let you trace queries authored in Galaxy through to downstream dashboards—bridging the gap between authoring and observability.

Next Steps

1. Instrument one production pipeline with OpenLineage.
2. Deploy Marquez behind your SSO and SSL terminate.
3. Embed Marquez UI iframe or lineage JSON into internal runbooks.

Visualizing lineage with Marquez gives teams x-ray vision into their dataflows, accelerating debugging and safeguarding trust in analytics.