Kyle Kirwan
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January 11, 2023

Bigeye is now on the Google Cloud Marketplace

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Kyle Kirwan
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We are thrilled to announce that Bigeye is now available on the Google Cloud Platform (GCP) Marketplace!

With Bigeye, your team gets better data quality, at any scale. Companies ranging from small startups to enterprises trust Bigeye to securely raise the bar on data quality, whether they have hundreds or billions of data points.

Now, Google Cloud users can get started immediately through our Marketplace offering. Purchase a Bigeye SaaS subscription directly on the marketplace today. Or contact Bigeye for custom environment sizing.

The GCP Marketplace houses software for users to research and deploy. If you join the GCP Marketplace, you’ll be able to enjoy many benefits like: consolidated Google Cloud billing, faster procurement (skip the approvals!), and quicker time to ROI.

Want to learn more? Here are some FAQs for Bigeye users:

FAQs

Q: Which plans are available on the GCP Marketplace?

A: You can purchase a subscription to Bigeye directly on the GCP marketplace. No contact with Bigeye required.

Q: Do I need a GCP marketplace account for this?

A: Yes. To proceed, your project must be enabled for purchase by your billing administrator.

Q: Where will I see how much I’m being charged for Bigeye?

A: All billing through marketplaces will be charged through your cloud provider. Check the billing console from your cloud provider.

Q: If I’m already a Bigeye customer, how do I switch to billing on the GCP marketplace?

A: Subscribing to GCP marketplace will automatically create a new organization for you. If you want to use GCP billing for an existing project, please reach out to support@bigeye.com and we’ll help you with details and migration.

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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.

Get the Best of Data Leadership

Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.

Want the practical playbook?

Join us on April 16 for The AI Trust Summit, a one-day virtual summit focused on the production blockers that keep enterprise AI from scaling: reliability, permissions, auditability, data readiness, and governance.

Get Data Insights Delivered

Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.

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