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May 6, 2025
Introducing Bigeye’s Azure Data Factory Connector
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Azure Data Factory (ADF) is a widely used ETL tool, especially in enterprise environments. And while ADF is powerful, understanding what’s happening inside a pipeline (or what broke and why) isn’t always easy.
With Bigeye’s new ADF connector, we’re making it easier to get the full picture. Teams can now visualize ADF pipelines directly in the Bigeye lineage graph, alongside their broader data ecosystem.
Here’s what you’ll see:
- Stored procedures revealed in context
- ADF Copy steps that show how and where data moves from one system to another
- Lookup operations that act as filters before a copy
This visibility helps teams spot misconfigurations, trace the root cause of data issues faster, and understand the impact of pipeline changes before they go live.
If you’re working in a hybrid data environment or already using ADF as part of your stack, this update adds another layer of clarity and control.
Explore the full list of supported connectors here.
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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights
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