Data Lineage: Understanding Data Flow

Data lineage is one of the most powerful features in Ilum, providing comprehensive visibility into the flow of data across your organization. This guide explores how data lineage works, its benefits, and practical use cases for implementing data lineage in modern data platforms.

Understanding Data Lineage​

What is Data Lineage?​

Data lineage is the process of tracking and documenting the flow of data from its origin to its destination. It captures metadata about data sources, transformation rules, and dependencies to provide a comprehensive view of the data lifecycle. Data lineage helps organizations understand data by documenting how data moves through various data systems and data pipelines.

The lineage process tracks:

• Data origins and data sources
• Data transformation operations applied
• Intermediate data processing steps
• Final data destinations
• Dependencies between data elements

How Data Lineage Works​

Data lineage works by capturing metadata at each stage of the data pipeline. The data lineage tool identifies data sources and their schemas, records data transformation and data mapping rules, documents data changes, and builds relationships between datasets and jobs.

In Apache Spark environments, data lineage uses an External Spark Listener Class that observes key events:

• Job creation and execution
• Dataset reads and writes
• Data transformation operations

When these events occur, the listener sends corresponding metadata to a lineage service (such as Marquez) using standardized formats like OpenLineage. This automated data lineage approach enables data engineers to capture lineage without manual intervention.

Why Data Lineage is Important​

Data lineage is important for modern enterprise data management. The benefits of data lineage include:

• Regulatory Compliance: Maintains audit trails for GDPR, CCPA, and other regulatory frameworks, especially for sensitive data
• Transparency: Documents data origins, transformations, and destinations
• Troubleshooting: Enables rapid tracing of data quality issues to their source
• Data Governance: Aligns data practices with governance policies and ensures data integrity
• Impact Analysis: Assesses downstream effects of schema or data pipeline changes

Data lineage provides visibility into data movement and enables organizations to manage data effectively across complex data systems.

Ilum Data Lineage Implementation​

Ilum integrates Marquez as its lineage service. When enabled, Ilum automatically configures jobs to use the OpenLineage listener and provides a UI to visualize data flows.

Ilum's Marquez Fork: Upstream-First Approach​

As early Marquez adopters, Ilum runs Marquez in production for comprehensive lineage and metadata tracking. To maintain continuous development while ensuring stability, Ilum maintains a fork of Marquez with the following principles:

• Drop-in compatibility: 100% compatible with upstream Marquez API and data model
• Non-breaking improvements: Stability fixes and additive features (including a Search API)
• Upstream-first strategy: All battle-tested enhancements are contributed back to the Marquez project
• Zero breakage policy: No breaking changes to existing APIs or storage schemas

The fork exists to enable rapid iteration while maintaining full compatibility with the OpenLineage specification and upstream Marquez. Starting with Marquez 0.52.x, Ilum is upstreaming features incrementally, focusing on safety and backwards compatibility. Ilum does not use marquez-web; instead, it provides a custom UI optimized for large-scale lineage exploration, searchability, and operational tooling.

This approach ensures that data lineage solutions remain stable and reliable while contributing improvements back to the open-source community.

Access the lineage page via the Lineage tab in the sidebar.

Spark OpenLineage Integration​

Ilum configures every job with the following parameters to capture lineage automatically:

spark.extraListeners=io.openlineage.spark.agent.OpenLineageSparkListener
spark.openlineage.transport.type=http
spark.openlineage.transport.url=<MARQUEZ_URL>
spark.openlineage.transport.endpoint=/api/v1/lineage

This configuration applies to all job types, including SQL execution engines, ensuring comprehensive lineage across all data systems.

Visualizing Data Flow with Lineage Diagrams​

Ilum provides data lineage tools in two main areas:

• Dedicated Lineage Page: Per-job lineage views
• Table Explorer Integration: Table-specific lineage within the data explorer

Graph Components​

Data lineage diagrams consist of:

• Job nodes: Represent jobs that produce or consume data assets
• Dataset nodes: Represent data assets produced or consumed by jobs
• Edges: Illustrate relationships and data flow between jobs and datasets

These data lineage diagrams visually represent the flow and transformation of data through your pipelines.

Column-Level Lineage​

The data explorer supports column-level lineage visualization, showing dependencies between specific data elements within datasets. This granular view helps data engineers understand data at the column level.

Next-Generation Lineage Features​

Ilum's lineage solution includes advanced capabilities for understanding data flow across bronze → silver → gold layers.

Smart Job Clustering​

Ilum automatically groups similar jobs into compact clusters to maintain graph readability. This clustering:

• Prevents overwhelming visualizations in complex data pipelines
• Preserves drill-down capability to individual runs
• Maintains comprehensibility at scale for big data environments

Layer-Aware Data Flow​

Track data evolution across refinement layers with:

• Key fields and data types displayed on table cards
• Clear upstream and downstream edges
• Visual layer badges (Bronze → Silver → Gold)

This layer awareness enables:

• Understanding data transformation stages
• Tracking data quality improvements across layers
• Identifying where data enters and exits each refinement stage
• Validating medallion architecture implementation

Operational Overlays​

Job nodes display critical operational metrics:

• Last run timestamp: When the job last executed
• Average duration: Typical data processing times
• Success rate: Job reliability metrics

These overlays enable quick identification of bottlenecks and problematic jobs, helping data engineers improve data pipeline performance.

Version-Aware Datasets​

The Versions tab for each data asset shows:

• Schema evolution over time
• Lifecycle events (e.g., OVERWRITE operations)
• Historical changes affecting downstream consumers

Understanding data across versions helps prevent surprises and enables proactive communication about breaking changes.

ERD ↔ Lineage Toggle​

Switch between two complementary perspectives:

• Entity-relationship view: Table relationships and schema design
• Lineage view: Runtime data flow and job execution

Use ERD mode for schema design, then verify runtime behavior in Lineage mode to ensure data integrity.

Navigation Features​

Navigate large graphs with:

• Mini-map: Bird's-eye view of complex lineage
• Zoom and pan: Smooth exploration of relationships
• Multi-select: Highlight multiple nodes simultaneously
• Status badges: Operational status indicators (OPERATIONAL, FAILED)

Data Lineage Search and Data Catalog Integration​

Ilum's global search enables instant location of data assets, columns, and jobs within the data catalog.

Search Capabilities​

The unified search bar supports:

• Table/dataset names: Direct navigation to any table in your data catalog
• Column names: Find all occurrences of data elements across datasets
• Job names or IDs: Locate specific jobs by name or identifier
• Storage paths: Search by physical location (e.g., s3://bucket/path, gs://bucket/path)

Matching nodes are highlighted on the lineage graph for focused exploration, making it easy to track data across your organization.

Namespace-Aware Search​

Control search scope:

• All namespaces: Query across the entire data platform
• Current namespace only: Limit to active namespace

Quick Actions​

From search results:

• View Details: Open data asset or job details
• Open SQL: View the query that created or transformed the data
• Jump to Job: Navigate to job execution details

Data Catalog Indexing​

Search results remain current through automatic reindexing of:

• Hive Metastore metadata
• Column-level metadata
• Lineage edges and relationships

Indexing occurs after each successful job run or schema change, ensuring the data catalog reflects the latest state.

Access Points​

Access lineage search from:

• Any dataset's Lineage tab (search bar)
• The unified Jobs view
• The Datasets browser in the data catalog

ERD View for Data Model Visualization​

ERD (Entity-Relationship Diagram) View provides interactive data modeling with live operational context, enhancing the data catalog experience.

Layer-Aware Data Modeling​

Tables display layer badges (Bronze → Silver → Gold), showing:

• Data quality levels at each stage
• Data transformation pipeline stages
• Medallion architecture documentation

Keys, Relations, and Cardinality​

ERD View visualizes database relationships:

• Primary keys: Clearly marked on each table
• Foreign keys: Visual indicators for linked fields
• Cardinality indicators: Relationship types (1:N, 1:1, N:M)

Field-Level Schema​

Each table card displays:

• Column names: All data elements in the table
• Data types: Type information for each column (e.g., id: long → integer)
• Type evolution: Type changes across layers

Operational Context​

Table nodes include real-time information:

• Freshness indicators: Recent data updates (e.g., "1 hour ago")
• Operational status: Current state (OPERATIONAL, STALE, FAILED)
• Data quality signals: Table health indicators

Join Visualization​

Relationship connectors snap to exact key fields, showing:

• Columns participating in relationships
• Foreign key reference directions
• Table connection patterns in the data model

Navigation​

Large data models remain manageable with:

• Smooth pan and zoom: Effortless navigation
• Tidy connectors: Automatically routed relationship lines
• Collapsible sections: Focus on relevant model portions

Use Cases for Data Lineage​

Use Case: Data Quality Improvement​

Trace data flow to identify where data quality issues arise. Data lineage enables root cause analysis by pinpointing the exact data transformation or data processing step that introduced errors. This use case helps ensure data quality across the entire data lifecycle.

Data lineage can help organizations:

• Identify data quality issues quickly
• Trace errors back to their source
• Validate data transformation logic
• Improve data reliability

Use Case: Regulatory Compliance​

Regulations like GDPR and CCPA require transparent tracking of sensitive data. Data lineage provides clear audit trails documenting:

• Data movement across data systems
• Access and modification history
• Sensitive data handling and data provenance

Lineage can help demonstrate compliance and reduce regulatory risk.

Use Case: Impact Analysis​

Before making data changes:

• Identify all downstream dependencies
• Assess schema modification impacts
• Plan data migration projects
• Minimize disruption to data consumers

Understanding data dependencies through lineage prevents unexpected failures.

Use Case: Data Cataloging and Discovery​

Data lineage enhances the data catalog by providing:

• Data origin and data transformation details
• Dependency information between data assets
• Quality and suitability assessment capabilities

Lineage information integrated into the data catalog improves understanding for all users, enabling data discovery and data analytics initiatives.

Choosing the Right Data Lineage Tool​

When selecting a data lineage tool, consider:

• Integration capabilities: Works with your existing data systems, data warehouses, and data pipelines
• Automated data lineage: Captures lineage without manual intervention
• Data lineage diagrams: Visualizes data flow effectively
• Data catalog integration: Enriches your data catalog with lineage information
• Scalability: Handles enterprise data volumes and complexity
• Data governance features: Supports compliance and data management requirements

A robust data lineage solution should provide end-to-end visibility and help manage data across complex environments. Ilum's lineage implementation, built on a battle-tested Marquez fork with upstream-first principles, provides production-ready data lineage capabilities with advanced features for enterprise data platforms. The lineage solution includes automated data lineage capture, comprehensive search capabilities, and operational overlays that help data engineers track data effectively across complex data pipelines.

How to Implement Data Lineage​

Steps to Implement Data Lineage​

Implementing data lineage involves several key steps:

1. Define scope: Identify critical data assets and data sources to track
2. Capture lineage: Profile data sources, document data mapping rules, and define data transformation logic
3. Configure tools: Set up your data lineage tool to capture lineage from data systems
4. Visualize data: Create data lineage diagrams to represent the flow of data
5. Share and maintain: Distribute lineage information to stakeholders and update regularly

Data lineage enables organizations to track data systematically when following these steps.

Leverage OpenLineage Features​

The OpenLineage listener is pre-configured with essential settings but supports advanced features:

• Custom job namespaces
• Manual dataset tagging
• Custom metadata capture

For details, see the OpenLineage documentation.

Keep OpenLineage Updated​

The OpenLineage listener receives frequent updates with new features and metadata collection capabilities. Ensure you use recent versions to benefit from improvements like column-level lineage tracking.

Custom Spark Listeners​

For additional or custom metadata capture, data engineers can implement custom Spark Listeners:

spark.extraListeners=com.example.CustomListener

Example custom listener in Scala:

import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart, SparkListenerJobEnd}

class CustomListener extends SparkListener {
  override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
    // Custom logic to capture metadata when a job starts
    println(s"Job started: ${jobStart.jobId}")
    // Send metadata to your lineage service here
  }

  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
    // Custom logic to capture metadata when a job ends
    println(s"Job ended: ${jobEnd.jobId}, result: ${jobEnd.jobResult}")
    // Send additional metadata here
  }
}

Ensure the custom listener is available on the Spark classpath. The spark.extraListeners property accepts comma-separated lists for multiple listeners.

Types of Data Lineage​

Understanding the different types of data lineage helps organizations choose the right approach for their needs.

Forward Data Lineage​

Forward data lineage traces data flow from data sources to all downstream consumers. It answers: "Where does this data go?"

Use forward data lineage to:

• Identify downstream dependencies before making data changes
• Assess impact of schema modifications
• Plan data migration projects
• Ensure consistency across dependent data systems

Backward Data Lineage​

Backward data lineage traces data from its current location back to original data sources. It answers: "Where did this data come from?"

Use backward data lineage to:

• Perform root cause analysis on data quality issues
• Validate data transformation logic
• Debug data pipeline errors
• Audit data provenance for compliance

End-to-End Lineage​

End-to-end lineage combines forward and backward perspectives, providing complete end-to-end data visibility from origin to destination. This comprehensive view enables:

• Complete data flow understanding
• Impact analysis in both directions
• Schema evolution tracking
• Full audit trail maintenance

Data lineage can help organizations track data across the entire data lifecycle when using end-to-end lineage.

Benefits of Data Lineage​

Get More Value from Data​

One of the key benefits of data lineage is the ability to get more value from data assets. When data lineage helps organizations understand data origins and data transformation, they can ensure data quality and accuracy. This increased confidence enables businesses to use data lineage for analysis and decision-making.

By having end-to-end lineage, data engineers can quickly identify and rectify issues in the data pipeline, ensuring that insights derived are reliable and actionable.

Enhancing Data Governance​

Data governance is significantly enhanced through data lineage implementation. Data lineage provides a clear and auditable flow of data, which is critical for regulatory compliance and internal control. Organizations can track data movement and data changes from data sources to final destinations.

The lineage information enables better data management, accountability, and transparency—essential components of robust data governance frameworks.

Critical Data Management​

Data lineage is critical for data management in complex enterprise data landscapes. It allows organizations to effectively manage data by providing visibility into how data moves across different data pipelines and data warehouses.

Implement data lineage to quickly identify the impact of data changes and trace data quality issues back to their root cause. This is invaluable for maintaining data quality and preventing data-related incidents.

Common Challenges in Data Lineage Implementation​

Complex Enterprise Data Landscapes​

Modern enterprise data environments have numerous data sources and intricate data pipelines. Ensuring comprehensive coverage and accurate data mapping across all data systems requires:

• Systematic approach to metadata capture
• Regular validation of lineage information
• Collaboration between data engineers and stakeholders

Without data lineage, managing complex data systems becomes significantly more difficult.

Maintaining Data Lineage Quality​

As data systems evolve and data transformation processes change, lineage information must remain accurate. Address this through:

• Regular audits of lineage data
• Automated validation processes
• Consistent documentation practices

Data lineage helps organizations maintain accurate metadata about their data assets over time.

Troubleshooting​

No Lineage Data Captured​

If no lineage data appears:

• Verify the lineage service (Marquez) is running and accessible
• Ensure OpenLineage listener dependencies are included in Spark jobs
• Check Spark job logs for OpenLineage listener errors
• Confirm URL and endpoint configuration for the lineage service
• Verify custom configurations are reflected in job properties

Ilum will set everything up for you.

Learn More About Data Lineage​

Understanding Data Lineage Concepts​

Data lineage tracks the detailed journey of data through your organization. Understanding how data lineage works, the types of data lineage available, and the benefits of implementing data lineage helps organizations use data more effectively.

Key concepts include:

• Data provenance: The origin and history of data
• Data flow: Movement of data through pipelines
• Data transformation: Operations that modify data
• Data mapping: Relationships between source and target data elements

Best Practices​

• Start small: Begin with critical data assets and expand coverage
• Automate: Use automated data lineage tools to reduce manual effort
• Integrate: Connect lineage with your data catalog for comprehensive data management
• Validate: Regularly verify lineage accuracy and completeness
• Educate: Train data engineers and users on how to use data lineage effectively

Data lineage is essential for modern data governance and helps organizations ensure data quality, maintain compliance, and get more value from their data assets.