Kyle Kirwan
kyle-kirwan
Company
-
November 16, 2021

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

min read

Kyle Kirwan
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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 Bigeye.com 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 bigeye.com. 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 bigeye.com.

Alright, back to it!

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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
about the author

Kyle Kirwan

Chief Product Officer, Bigeye

Kyle Kirwan is Co-Founder and Chief Strategy Officer of Bigeye, where he leads strategic partnerships, prototype development, and other zero-to-one projects.

Kyle’s journey to founding Bigeye began at Uber, where he helped scale the company’s experimentation and data platforms during a period of hypergrowth. As a product leader and former founding data scientist on Uber’s experimentation platform, he worked on standardizing metrics across thousands of A/B tests that shaped rider, driver, and pricing experiences for millions of users.

It was at Uber that Kyle met Egor Gryaznov. Shortly after Egor joined, he launched Uber’s first SQL bootcamp. Kyle signed up partly out of curiosity, and partly to make sure the new guy actually knew his stuff. They quickly bonded over giving each other increasingly complex SQL challenges to solve.

As Uber’s data ecosystem grew to hundreds of petabytes and thousands of weekly users, Kyle saw a pattern emerge: testing the data pipelines was valuable but didn’t scale. His team experimented with using machine learning models on the daily data profiles of tables in the data lake to see if anomalies could be identified without manually writing data quality checks. This technique would later be termed data observability.

In 2019, Kyle and Egor co-founded Bigeye to use the lessons learned at Uber to transform data management in the enterprise. Today Bigeye serves some of the world’s largest organizations and ensures their data is trustworthy, and that their enterprise AI initiatives are grounded in that trusted data.

about the author

about the author

Kyle Kirwan is Co-Founder and Chief Strategy Officer of Bigeye, where he leads strategic partnerships, prototype development, and other zero-to-one projects.

Kyle’s journey to founding Bigeye began at Uber, where he helped scale the company’s experimentation and data platforms during a period of hypergrowth. As a product leader and former founding data scientist on Uber’s experimentation platform, he worked on standardizing metrics across thousands of A/B tests that shaped rider, driver, and pricing experiences for millions of users.

It was at Uber that Kyle met Egor Gryaznov. Shortly after Egor joined, he launched Uber’s first SQL bootcamp. Kyle signed up partly out of curiosity, and partly to make sure the new guy actually knew his stuff. They quickly bonded over giving each other increasingly complex SQL challenges to solve.

As Uber’s data ecosystem grew to hundreds of petabytes and thousands of weekly users, Kyle saw a pattern emerge: testing the data pipelines was valuable but didn’t scale. His team experimented with using machine learning models on the daily data profiles of tables in the data lake to see if anomalies could be identified without manually writing data quality checks. This technique would later be termed data observability.

In 2019, Kyle and Egor co-founded Bigeye to use the lessons learned at Uber to transform data management in the enterprise. Today Bigeye serves some of the world’s largest organizations and ensures their data is trustworthy, and that their enterprise AI initiatives are grounded in that trusted data.

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