What Is Data Lineage?

Learning Objectives

• Define data lineage and explain why it matters to data reliability and compliance.
• Identify the building blocks: sources, transformations, targets, and metadata.
• Compare forward & backward, business & technical, design-time & run-time lineage.
• Implement a step-by-step approach for capturing lineage in SQL-centric stacks.
• Apply best practices and avoid common mistakes.
• Use Galaxy to explore, document, and share lineage with your team.

1. Introduction to Data Lineage

1.1 Definition

Data lineage is the complete, end-to-end record of how a data asset moves through your stack—where it originates, the transformations it undergoes, and the systems or people that ultimately consume it. Think of it as a version-controlled map for data, comparable to Git history for code.

1.2 Why It Matters

• Trust & Accuracy – Trace anomalies back to the specific transformation or source field.
• Regulatory Compliance – GDPR, HIPAA, and SOC 2 audits often require provable data flows.
• Impact Analysis – Know which dashboards break when you alter a column.
• Debugging & Cost Control – Spot redundant steps and expensive queries.

2. Key Components of Data Lineage

2.1 Sources

Raw databases, CSV uploads, event streams, APIs—anywhere data originates.

2.2 Transformations

• SQL Scripts – JOINs, aggregations, CTEs.
• ELT/ETL Jobs – dbt models, Airflow pipelines.
• Code – Python, Scala, or Java dataframes.

2.3 Destinations

Analytics tables, dashboards, machine-learning features, or operational APIs.

2.4 Metadata & Context

Run timestamps, owners, schema versions, and performance stats that enrich the graph.

3. Types of Data Lineage

3.1 Directional

• Forward Lineage – Given a source change, which downstream assets are affected?
• Backward Lineage – Given a dashboard metric, trace back to raw sources.

3.2 Abstraction Level

• Business Lineage – Describes concepts like “Monthly Recurring Revenue.”
• Technical Lineage – Breaks down field-level transformations and SQL logic.

3.3 Lifecycle Stage

• Design-Time – Static DAGs from dbt or Airflow code.
• Run-Time – Actual executed SQL with operational metrics.

4. Practical Example: Tracking Lineage in SQL

Assume you ingest customer orders from raw.orders, clean them into stg_orders, and build a revenue dashboard table mart.revenue_daily. Below is a simplified dbt-style transformation using Common Table Expressions (CTEs):

WITH cleaned_orders AS (
SELECT order_id,
customer_id,
CAST(order_total AS NUMERIC) AS order_total,
order_date::DATE AS order_date
FROM raw.orders
WHERE order_status = 'complete'
),
revenue_by_day AS (
SELECT order_date,
SUM(order_total) AS daily_revenue
FROM cleaned_orders
GROUP BY order_date
)
SELECT *
FROM revenue_by_day;

Lineage Graph:

1. raw.orders → filter & type cast → cleaned_orders
2. cleaned_orders → aggregation → revenue_by_day (materialized as mart.revenue_daily)

Capturing Lineage Automatically

Tools like dbt or Galaxy parse the SQL AST (Abstract Syntax Tree) to extract dependencies. Whenever you run this query in Galaxy, the platform can:

• Link the result to raw.orders and mart.revenue_daily
• Version the query text and attribute ownership
• Show downstream dashboards that rely on mart.revenue_daily

5. Implementing Data Lineage: A Step-by-Step Guide

Step 1 – Inventory Your Assets

Create a catalog of tables, views, files, and dashboards. Tools: dbt docs, Google Sheets, or Galaxy’s auto-sync.

Step 2 – Pick a Lineage Granularity

Start at table-level; graduate to field-level once the organization matures.

Step 3 – Instrument Transformations

• For SQL, parse queries or use wrappers like dbt or Galaxy’s query parser.
• For code, add decorators or logging hooks.

Step 4 – Store Metadata

Use a centralized metadata store (e.g., Postgres, OpenMetadata, or Galaxy’s upcoming catalog).

Step 5 – Visualize & Validate

Render DAGs, run data quality tests, and perform impact analysis before releases.

Step 6 – Automate Enforcement

Block schema changes if downstream tests fail—Galaxy integrates with GitHub checks to enforce this.

6. Common Challenges & How to Overcome Them

• Hidden Logic – SQL embedded in BI tools. Solution: centralize queries in Galaxy and enforce usage.
• Pipeline Drift – Over time, manual scripts appear. Solution: enforce pull-request reviews and automated lineage checks.
• Performance Overhead – Excessive logging can slow jobs. Solution: sample lineage in dev, full capture in prod.
• Ownership Confusion – No one knows who owns stg_users. Solution: add owner tags and approval workflows in Galaxy.

7. Data Lineage Tools & Ecosystem

7.1 Open-Source / Vendor Tools

• dbt – Auto-generates model DAGs.
• OpenLineage – Standardized metadata spec.
• Marquez – Reference implementation of OpenLineage.
• Airflow + Lineage Plugins

7.2 How Galaxy Fits In

Galaxy is more than a SQL editor—it’s a collaborative workspace that automatically:

1. Parses every executed query to extract source ↔ target dependencies.
2. Versions queries and links them to Git for audit trails.
3. Lets teams endorse lineage-aware queries, turning them into reusable building blocks.
4. Surfacing lineage context inside the AI copilot, so generated SQL respects existing models.

8. Best Practices

• Adopt “SQL-first” transformations—avoid opaque UI builders where lineage can’t be parsed.
• Enforce code reviews with lineage diff checks.
• Document business definitions alongside technical lineage.
• Automate data quality tests at each hop.
• Limit privileges; only endorsed queries feed dashboards.

9. Exercises & Hands-On Practice

1. Backward Trace – In Galaxy, open a critical metric query and list all upstream tables. Confirm the path against your dbt DAG.
2. Forward Impact – Change a column name in a staging model within a dev schema. Use Galaxy search to find affected queries.
3. Lineage Diagram – Export the lineage JSON from Galaxy (or dbt) and draw a simple graph with Mermaid or yEd.
4. Quality Gate – Write a test that blocks merges when lineage shows an unapproved raw table feeding a prod dashboard.

10. Key Takeaways & Next Steps

• Data lineage is foundational for trust, compliance, and agility.
• Start simple: table-level, automated parsing, clear ownership.
• Integrate lineage capture into everyday workflows—your SQL editor, CI/CD, and AI assistants.
• Galaxy can jump-start the journey by centralizing queries, parsing lineage, and making it searchable and shareable.

Next Steps: Enable lineage parsing in your Galaxy workspace, audit your top 20 queries, and schedule a team demo to socialize the new observability layer.