Spline: Illuminating Data Lineage in the Apache Spark Ecosystem
In today’s data-driven landscape, organizations face an increasing challenge: as data pipelines grow more complex, understanding how data flows, transforms, and evolves becomes critical yet increasingly difficult. This challenge is particularly acute in Apache Spark environments, where massive-scale data processing often involves intricate transformations that remain opaque to both technical and business users. The inability to trace data’s journey creates significant challenges for governance, compliance, and troubleshooting efforts.
Enter Spline (SPark LINEage), an open-source data lineage tracking solution designed specifically for Apache Spark. Unlike general-purpose lineage tools that require extensive integration work, Spline seamlessly captures detailed lineage information directly from Spark applications with minimal configuration. By automatically recording transformation logic, data structures, and dependencies, Spline provides unprecedented visibility into Spark-powered data pipelines.
This article explores how Spline is transforming data lineage practices in Spark environments, its key capabilities, implementation strategies, and real-world applications that can help organizations build more transparent, trustworthy data ecosystems.
Before diving into Spline’s capabilities, it’s important to understand the unique lineage challenges in Spark environments:
Spark’s power and flexibility come with inherent visibility challenges:
- Complex Transformations: Sophisticated operations that alter data in ways difficult to track manually
- Lazy Execution: Deferred processing that obscures the actual execution plan
- Optimized Execution: Runtime optimizations that may differ from the logical plan
- Distributed Processing: Operations spread across nodes with no centralized record
- Dynamic Execution: Varying execution paths based on data characteristics
These factors create a “black box” effect, where data goes in, results come out, but the journey between remains largely invisible.
This lack of visibility creates significant business challenges:
- Regulatory Requirements: GDPR, CCPA, and industry regulations increasingly demand data lineage
- Audit Difficulties: Unable to demonstrate how specific outputs were derived
- Trust Issues: Data consumers questioning the reliability of results
- Troubleshooting Complexity: Difficult to trace problems to their source
- Knowledge Silos: Critical transformation logic understood only by original developers
Spline (SPark LINEage) is an open-source project that automatically captures, stores, and visualizes detailed lineage information from Apache Spark applications. Rather than a general-purpose lineage tool requiring extensive integration work, Spline is purpose-built for Spark, enabling near-automatic lineage capture.
Spline is built around several key concepts:
- Execution Plans: Detailed records of Spark’s data processing logic
- Execution Events: Runtime information about actual processing
- Data Structures: Descriptions of data schemas and transformations
- Lineage Harvesting: Automated collection of lineage information
- Lineage Visualization: Interactive exploration of data flows
The design philosophy emphasizes:
- Minimal Intrusion: Limited changes to existing Spark applications
- Automatic Capture: Harvesting lineage without manual documentation
- Comprehensive Detail: Tracking both high-level flows and detailed transformations
- Developer Accessibility: Easy implementation for Spark developers
- Scalable Architecture: Handling enterprise-scale lineage needs
Spline implements a modern, scalable architecture designed for enterprise deployment:
At a high level, Spline consists of:
- Agent Library: The component integrated with Spark applications
- Lineage Harvester: Captures and processes lineage information
- Persistence Layer: Stores lineage data (typically MongoDB)
- REST API: Service layer for lineage retrieval
- Web UI: Visualization interface for exploring lineage
+---------------------+ +---------------------+
| | | |
| Spark Application | | Spline System |
| | | |
+---------------------+ +---------------------+
| • Data Processing | | • Lineage Harvester |
| • Transformations | | • REST API Service |
| • Spline Agent |---------→ • Persistence Layer |
| | Lineage | • Web UI |
| | Data | • Lineage Explorer |
+---------------------+ +---------------------+
| |
| Lineage Storage |
| |
+---------------------+
| • Execution Plans |
| • Data Structures |
| • Execution Events |
| • Dependencies |
| • Transformations |
+---------------------+
Spline captures detailed lineage information about:
- Execution Plans: The logical and physical processing steps
- Data Structures: Input and output schemas with field-level details
- Transformations: The specific operations applied to data
- Expressions: Detailed logic of data transformations
- Dependencies: Relationships between datasets and operations
This detailed model enables both high-level flow visualization and drill-down analysis:
// Simplified example of Spline execution plan (partial)
{
"id": "plan-123456789",
"name": "Daily Customer Analytics",
"systemInfo": {
"name": "Apache Spark",
"version": "3.2.1"
},
"agentInfo": {
"name": "spline-agent-spark",
"version": "0.7.0"
},
"extraInfo": {
"appName": "CustomerAnalyticsPipeline"
},
"operations": {
"write": {
"outputSource": "s3://analytics/customer/daily/2023-05-15/",
"append": false,
"id": "op-write-001",
"childIds": ["op-project-001"]
},
"other": [
{
"id": "op-project-001",
"type": "Projection",
"childIds": ["op-filter-001"],
"params": {
"projectList": [
{
"id": "expr-001",
"name": "customer_id",
"dataType": "string",
"exprType": "AttributeReference"
},
{
"id": "expr-002",
"name": "lifetime_value",
"dataType": "decimal",
"exprType": "Arithmetic",
"operation": "sum",
"childIds": ["expr-003", "expr-004"]
}
]
}
},
{
"id": "op-filter-001",
"type": "Filter",
"childIds": ["op-read-001"],
"params": {
"condition": {
"id": "expr-005",
"exprType": "Predicate",
"operation": "greaterThan",
"childIds": ["expr-006", "expr-007"]
}
}
},
{
"id": "op-read-001",
"type": "LogicalRelation",
"params": {
"relation": "s3://raw-data/transactions/2023-05-15/"
}
}
]
},
"inputSources": [
"s3://raw-data/transactions/2023-05-15/"
],
"outputSource": "s3://analytics/customer/daily/2023-05-15/",
"executedAt": "2023-05-15T08:00:00Z"
}
Spline integrates with Spark applications through several mechanisms:
- Listener API: Using Spark’s QueryExecutionListener interface
- DataSourceV2 API: Capturing detailed read/write operations
- Execution Plan Analysis: Extracting logical and physical plans
- Reflection Techniques: Accessing internal Spark structures when needed
- Custom Wrappers: Providing enhanced integration for specific use cases
This multi-layered approach ensures comprehensive lineage capture with minimal configuration.
Spline provides several core capabilities that make it particularly valuable for Spark lineage tracking:
Spline excels at automatically capturing lineage without manual instrumentation:
- Zero-Code Integration: Simple configuration changes for basic lineage capture
- Non-Intrusive Design: Minimal impact on existing Spark applications
- Comprehensive Collection: Capturing all transformations and data flows
- Scalable Processing: Handling large-scale Spark jobs
- Configurable Detail Level: Adjustable verbosity for different use cases
This automated approach eliminates the manual effort and inconsistency of traditional lineage documentation.
Spline captures the specifics of how data is transformed:
- Operation Recording: Documenting each transformation step
- Expression Capture: Detailed logic of calculations and conditions
- Schema Evolution: Tracking changes to data structures
- Field-Level Lineage: Tracing individual column transformations
- User-Defined Functions: Capturing custom transformation logic
This detailed view provides unprecedented insight into how data is actually processed:
// Example Spark transformation with Spline lineage capture
import za.co.absa.spline.harvester.SparkLineageInitializer._
// Enable Spline with a single line
spark.enableLineageTracking()
// Regular Spark code - lineage is captured automatically
val rawData = spark.read
.option("header", "true")
.csv("s3://raw-data/transactions/")
val processedData = rawData
.filter($"transaction_date" > "2023-01-01")
.groupBy($"customer_id")
.agg(
sum($"amount").as("total_spend"),
count("*").as("transaction_count"),
max($"transaction_date").as("last_transaction_date")
)
.withColumn("avg_transaction", $"total_spend" / $"transaction_count")
.withColumn("customer_segment",
when($"total_spend" > 1000, "Premium")
.when($"total_spend" > 500, "Regular")
.otherwise("Basic")
)
// Write data - lineage includes full transformation journey
processedData.write
.mode("overwrite")
.parquet("s3://analytics/customer-segments/")
Spline provides powerful visualization capabilities:
- Flow Diagrams: Visual representation of data lineage
- Transformation Details: Drill-down into specific operations
- Schema Views: Examination of data structures
- Impact Analysis: Understanding downstream effects of changes
- Historical Perspective: Reviewing past execution plans
These visualization tools transform complex lineage data into accessible, actionable insights:
+----------------+ +----------------+ +----------------+
| | | | | |
| Raw | | Filtered | | Aggregated |
| Transactions |-------->| Transactions |-------->| Customer Data |
| | | | | |
+----------------+ +----------------+ +----------------+
|
|
v
+----------------+ +----------------+ +----------------+
| | | | | |
| Customer |<--------| Enriched |<--------| Calculated |
| Segments | | Customer Data | | Metrics |
| | | | | |
+----------------+ +----------------+ +----------------+
Spline enables effective exploration of lineage information:
- Text Search: Finding relevant execution plans and datasets
- Filtering Capabilities: Narrowing results by time, application, or other attributes
- Impact Analysis: Identifying downstream dependencies
- Provenance Tracking: Finding the origins of specific datasets
- Time-based Navigation: Exploring lineage evolution over time
These discovery features help users quickly find relevant lineage information:
Query: customer_segment
Results:
- Execution Plan: "Daily Customer Segmentation" (2023-05-15)
Expression: CASE WHEN total_spend > 1000 THEN "Premium" ...
Output: s3://analytics/customer-segments/
- Execution Plan: "Marketing Campaign Analysis" (2023-05-10)
References field: customer_segment
Input: s3://analytics/customer-segments/
Successfully implementing Spline requires thoughtful planning and execution:
Spline offers flexible deployment approaches:
For quick setup and testing, Docker provides a streamlined approach:
# Docker Compose deployment
git clone https://github.com/AbsaOSS/spline.git
cd spline/examples/docker-compose
docker-compose up
For production environments, Kubernetes offers scalability and resilience:
# Example Kubernetes deployment (simplified)
apiVersion: apps/v1
kind: Deployment
metadata:
name: spline-api-server
labels:
app: spline
component: api-server
spec:
replicas: 3
selector:
matchLabels:
app: spline
component: api-server
template:
metadata:
labels:
app: spline
component: api-server
spec:
containers:
- name: spline-api-server
image: absaoss/spline-api-server:latest
ports:
- containerPort: 8080
env:
- name: MONGO_HOST
value: "mongodb-service"
- name: MONGO_PORT
value: "27017"
- name: MONGO_DB_NAME
value: "spline"
For environments with specific requirements, traditional installation works well:
# Example manual deployment steps (simplified)
# 1. Deploy MongoDB
# 2. Deploy Spline REST API server
java -jar spline-api-server.jar \
--spline.mongodb.url=mongodb://localhost:27017/spline
# 3. Deploy Spline Web UI
java -jar spline-web-ui.jar
Spline can be integrated with Spark applications through several approaches:
For most Scala and Java Spark applications:
// Add Spline agent dependency
// libraryDependencies += "za.co.absa.spline.agent.spark" % "spark-3.2-spline-agent-bundle" % "0.7.0"
import za.co.absa.spline.harvester.SparkLineageInitializer._
// Enable Spline with a single line
spark.enableLineageTracking()
// Continue with normal Spark code
// Lineage is captured automatically
For PySpark applications:
# Add this to your PySpark application
from pyspark.sql import SparkSession
# Create Spark session with Spline configuration
spark = SparkSession.builder \
.appName("PySpark with Spline") \
.config("spark.driver.extraJavaOptions",
"-javaagent:/path/to/spline-agent-spark.jar") \
.config("spark.spline.producer.url",
"http://spline-server:8080/producer") \
.getOrCreate()
# Continue with normal PySpark code
# Lineage is captured automatically
For applications launched with spark-submit:
spark-submit \
--conf "spark.driver.extraJavaOptions=-javaagent:/path/to/spline-agent-spark.jar" \
--conf "spark.executor.extraJavaOptions=-javaagent:/path/to/spline-agent-spark.jar" \
--conf "spark.spline.producer.url=http://spline-server:8080/producer" \
--class com.example.SparkApp \
/path/to/application.jar
Most successful Spline deployments follow a phased approach:
- Assessment Phase
- Inventory Spark applications and their importance
- Identify lineage requirements and use cases
- Evaluate technical prerequisites and constraints
- Define success metrics and expected benefits
- Plan initial scope and expansion strategy
- Pilot Implementation
- Deploy Spline in a controlled environment
- Integrate with 1-3 representative Spark applications
- Validate lineage accuracy and completeness
- Develop initial lineage use cases
- Train key users on lineage tools
- Scaled Deployment
- Expand to additional Spark applications
- Implement integration within CI/CD pipelines
- Develop documentation and training
- Create operational procedures
- Integrate with broader governance initiatives
- Operational Maturity
- Embed lineage in data governance workflows
- Implement lineage-based alerts and monitoring
- Develop advanced use cases and integrations
- Measure and communicate business value
- Establish continuous improvement processes
This incremental approach balances quick wins with comprehensive coverage.
Spline has been successfully applied across industries to solve diverse lineage challenges:
A global bank implemented Spline for financial reporting lineage:
- Challenge: Documenting data transformations for regulatory compliance
- Implementation:
- Integrated Spline with critical financial data processing pipelines
- Captured detailed transformations of accounting and risk data
- Created lineage documentation for regulatory reports
- Implemented automatic lineage verification for production jobs
- Provided auditor access to lineage visualizations
- Results:
- 70% reduction in audit preparation time
- Comprehensive evidence for regulatory inquiries
- Improved confidence in regulatory reporting
- Enhanced ability to explain calculation methodologies
A retail organization used Spline to improve data quality processes:
- Challenge: Understanding sources and impacts of data quality issues
- Implementation:
- Deployed Spline across inventory and sales data pipelines
- Integrated lineage with data quality monitoring
- Created lineage-aware alerting for data issues
- Developed impact analysis for quality problems
- Implemented lineage-based data quality dashboards
- Results:
- 50% faster identification of data quality issue sources
- Improved ability to assess impact of quality problems
- Enhanced collaboration between data engineering and business teams
- More effective prioritization of quality improvement efforts
A healthcare research organization implemented Spline for clinical data tracking:
- Challenge: Ensuring appropriate transformations of sensitive patient data
- Implementation:
- Integrated Spline with clinical data processing pipelines
- Captured detailed transformations including anonymization steps
- Created governance checkpoints using lineage information
- Implemented lineage verification for compliance
- Provided research teams with transformation transparency
- Results:
- Enhanced compliance with healthcare regulations
- Improved confidence in data anonymization processes
- Simplified audit processes for ethical review committees
- Better collaboration between data engineers and clinical researchers
Beyond basic lineage tracking, Spline enables several advanced capabilities:
Spline can be connected with broader governance solutions:
- Metadata Repository Integration: Feeding lineage to enterprise metadata systems
- Policy Compliance Verification: Using lineage to validate governance rules
- Classification Propagation: Tracking sensitive data through transformations
- Approval Workflows: Incorporating lineage in change management
- Audit Evidence Generation: Creating compliance documentation from lineage
# Example: Integrating Spline lineage with a governance platform
import requests
import json
def sync_lineage_to_governance(governance_api_url, execution_plan_id):
# Fetch execution plan details from Spline
spline_response = requests.get(
f"http://spline-server:8080/api/execution-plans/{execution_plan_id}",
headers={"Accept": "application/json"}
)
execution_plan = spline_response.json()
# Transform to governance platform format
governance_lineage = {
"id": execution_plan["id"],
"name": execution_plan.get("name", "Unnamed Job"),
"timestamp": execution_plan["executedAt"],
"inputs": [
{
"id": source,
"name": source.split("/")[-1],
"type": "dataset"
}
for source in execution_plan["inputSources"]
],
"output": {
"id": execution_plan["outputSource"],
"name": execution_plan["outputSource"].split("/")[-1],
"type": "dataset"
},
"transformations": extract_transformations(execution_plan),
"user": execution_plan.get("extraInfo", {}).get("user"),
"application": execution_plan.get("extraInfo", {}).get("appName")
}
# Send to governance platform
governance_response = requests.post(
f"{governance_api_url}/lineage/import",
headers={"Content-Type": "application/json"},
data=json.dumps(governance_lineage)
)
return governance_response.status_code == 200
Spline can be extended with domain-specific lineage information:
// Example: Extended lineage with custom attributes
import za.co.absa.spline.harvester.SparkLineageInitializer._
import za.co.absa.spline.harvester.extra.UserExtraMetadataProvider
// Enable Spline with custom metadata provider
spark.enableLineageTracking(Some(new UserExtraMetadataProvider {
override def getMetadata(): Map[String, Any] = {
Map(
"pipeline_id" -> "CUST-DAILY-001",
"data_owner" -> "marketing_team",
"sensitivity_level" -> "RESTRICTED",
"retention_period" -> "7 years",
"quality_score" -> 0.98,
"business_domain" -> "customer_analytics"
)
}
}))
// Continue with normal Spark code
// Lineage is captured with additional metadata
This extensibility enables:
- Business Context: Adding domain-specific metadata
- Custom Attributes: Supporting specialized governance requirements
- Enhanced Visualization: Displaying additional context in the UI
- Advanced Search: Finding lineage based on custom attributes
- Integration Support: Connecting with domain-specific systems
For large-scale Spark environments, Spline offers optimization options:
- Selective Capture: Configuring lineage detail level for different applications
- Asynchronous Processing: Non-blocking lineage collection
- Batched Submission: Grouping lineage events for efficient processing
- Targeted Persistence: Storing only relevant lineage information
- Scalable Backend: Distributed storage and processing options
These optimizations ensure lineage collection doesn’t impact critical data processing performance.
Organizations achieving the greatest success with Spline follow these best practices:
Focus initial implementation on high-value lineage needs:
- Identify Spark applications processing sensitive or regulated data
- Prioritize jobs supporting critical business processes and decisions
- Focus on complex transformations that are difficult to document manually
- Target areas with known governance or troubleshooting challenges
- Consider both immediate pain points and strategic initiatives
This targeted approach delivers visible value while building momentum.
Make lineage a natural part of development practices:
- Include Spline integration in project templates and standards
- Implement lineage validation in CI/CD pipelines
- Add lineage review to code review processes
- Create developer documentation and training
- Provide easy access to lineage visualization during development
This integration ensures consistent lineage collection across applications.
Align Spline with broader governance initiatives:
- Map lineage capabilities to governance requirements
- Integrate with metadata repositories and catalogs
- Incorporate lineage in data quality management
- Use lineage to support regulatory compliance
- Include lineage in data documentation processes
This alignment maximizes the business value of lineage information.
Implement lineage collection that scales with your environment:
- Test lineage impact on critical Spark jobs
- Adjust detail level based on performance requirements
- Implement appropriate backend scaling for large deployments
- Monitor lineage collection and storage metrics
- Establish performance optimization guidelines
This attention to performance ensures sustainable lineage practices.
As Spline continues to evolve, several key trends are shaping its development:
Extending lineage to AI and ML workflows:
- Feature Engineering Lineage: Tracking feature derivation and transformation
- Model Training Lineage: Capturing training data and parameter selection
- Experiment Tracking: Connecting experiments to their data sources
- Model Deployment Lineage: Following models from training to production
- Prediction Traceability: Linking predictions back to training data
This extension addresses the growing need for transparency in AI systems.
Enhancing support for cloud data platforms:
- Managed Spark Services: Improved integration with Databricks, EMR, and Dataproc
- Cloud Storage Lineage: Better handling of cloud storage references
- Serverless Support: Lineage for serverless Spark applications
- Cloud-Native Deployment: Optimized deployment for cloud environments
- Multi-Cloud Tracking: Following data across cloud boundaries
These capabilities will extend lineage benefits to increasingly cloud-centric data ecosystems.
Adapting lineage for streaming applications:
- Structured Streaming Lineage: Enhanced support for Spark streaming
- Temporal Context: Time-based view of streaming data lineage
- Incremental Updates: Efficient handling of continuous processing lineage
- State Tracking: Understanding stateful streaming operations
- Event-Time Lineage: Aligning lineage with event timestamps
This evolution will address the growing importance of real-time data processing.
In today’s complex Spark environments, understanding how data flows and transforms has become essential for governance, compliance, and operational excellence. Spline addresses this challenge by providing an open-source lineage tracking solution specifically designed for Apache Spark, enabling automated capture of detailed transformation information with minimal configuration.
By combining non-intrusive integration with comprehensive lineage detail, Spline transforms the “black box” of Spark processing into a transparent, documented flow of data. From financial services to retail to healthcare, diverse industries are using Spline to enhance governance, troubleshoot data issues, and build more transparent data pipelines.
The most successful implementations of Spline balance technical integration with clear business objectives, creating practical lineage solutions that deliver tangible value. As lineage capabilities continue to evolve—incorporating ML workflows, cloud-native integration, and streaming support—Spline provides a foundation that can grow with your organization’s Spark ecosystem.
Whether you’re addressing regulatory compliance, improving data quality, or enhancing operational visibility, Spline offers a Spark-specific approach that can transform how you understand and manage your critical data transformations.
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