November 16, 2021

Updating our name: Announcing Bigeye, the product formerly known as Toro.

Toro, the monitoring platform for analytics and data engineering teams, is now called Bigeye.

Kyle Kirwan

TL;DR — Toro, the monitoring platform for analytics and data engineering teams, is now called Bigeye. While the brand is new, the team, mission, and product remain the same.

Today I’m proud to announce a new identity for Toro Data Labs. With a year behind us of prototyping, meeting our early customers, raising funding, and bringing together a team, we’re coming up (briefly) for air to announce our new identity.

The new name of Toro is Bigeye.

Everything else… is the same! Our mission of enabling simpler and more seamless operational excellence for analytics, data engineering, and data science teams is unchanged. The product (while constantly evolving) is still laser focused on ensuring everyone whose work depends on accurate data has continuous monitoring and alerting to protect them from silent outages.

All existing Toro accounts have been migrated to where you can sign in as usual. We don’t anticipate any interruption to service, but if you need help you can always contact us at: support at If you don’t have an account and would like to see how Bigeye automates monitoring and the detection of data outages, give us a shout: hello at

Alright, back to it!

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Monthly cost ($)
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Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
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
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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