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May 13, 2025

Introducing Bigeye’s Matillion Connector

2 min read

Adrianna Vidal
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One of the world’s largest industrial tech companies came to us with a challenge: they were migrating dozens of pipelines to a modern architecture (Snowflake, Matillion, Qlik) but still had critical dashboards powered by legacy systems like Oracle and SQL Server.

Each week, 60+ people joined a global standup just to confirm that dashboards were working.

There was little room for error. But there was also no way to trace what their Matillion jobs were doing.

Then they hit a silent data quality issue. It took weeks to discover. Weeks more to fix. And that’s when they knew: they needed end-to-end visibility. Now.

That’s why we built the Matillion connector.

What it does

The new connector gives data teams complete lineage across Matillion orchestration and transformation jobs by synthesizing the SQL logic within each job.

That means:

  • Full visibility into how data moves and changes through Matillion
  • A way to monitor both old and new pipelines in one place
  • Confidence that migrations are progressing as planned

Their team can now proactively monitor critical ETL flows, without having to manually trace SQL or guess what Matillion is doing behind the scenes.

This connector started with one customer. Now it’s available to everyone.

Ready to try it for yourself? Explore all of Bigeye’s integrations 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
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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