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CASE STUDY:

The Cornell Lab of Ornithology

Institutional Profile: Non-profit organization under the auspices of Cornell University, dedicated to conservation, education, and citizen science.

The open data collected by the Cornell Lab regularly guides academic research and conservation initiatives. As the creators and managers of citizen science applications like eBird and Merlin, they support hundreds of thousands of users in tracking over 100 million bird observations per year, making them stewards of some of the largest biodiversity datasets in the world.

“Moving our web applications to Mirantis has completely transformed the way we deliver software.”

David Childs, DevOps Engineer for Cornell Lab of Ornithology

Engaged in a global, data-driven mission that touches the lives of millions of living creatures, the Cornell Lab requires IT infrastructure that provides high availability and scalability, while freeing developers to move quickly on new projects. As a non-profit organization—and a part of a venerable academic institution—they also face a technical challenge: meeting sky-high performance expectations while leveraging existing campus infrastructure as economically as possible.

Highly Available and Scalable Infrastructure

David Childs is DevOps Engineer for the Cornell Lab of Ornithology. His decisions touch on every facet of the Cornell Lab’s IT infrastructure, from application platform administration to the design of deployment processes.

“We strive for highly available and scalable infrastructure that is deployed as code,” Childs says, “so that our developers can deploy frequently with low risk.” But the University’s on-site server-farm was an essential resource, available at much lower cost than cloud resources. He needed a solution that provided the benefits of cloud-native workflows while still using on-premises infrastructure—and all without consuming developers’ time with a cloud migration.

Mirantis provided a solution. With a private cloud on the University’s bare-metal, the Cornell Lab team was able to leverage Infrastructure-as-a-Service to achieve a flexible, performant environment while maintaining control of their data. The results were transformative.

Turning Data into Knowledge

“Moving our web applications to Mirantis has completely transformed the way we deliver software,” Childs says. “We empowered teams to manage their products without an operations bottleneck, eliminated configuration drift conflicts, and provided full parity between test and production. We gained high availability, scalability, and a standardized deployment process.” With this streamlined development workflow, the Cornell Lab’s lead time for new projects is measured in hours or even minutes, instead of the days or weeks that might have been required before.

The newfound flexibility also means that the Lab is well-positioned to take advantage of container orchestration. For many teams, a major challenge to running Kubernetes lies in the maintenance and management of the tool itself. According to Childs, “Using Mirantis means we have a single source of truth for upgrade documentation, as well as a single source of support for each platform component.” This support helps the Lab to provide the high reliability that their users around the world expect.

“We’re pioneering new techniques at the interface of citizen science, machine learning, and data visualization,” says Childs. Whether they’re obtaining wildlife recordings from active sensor networks around the world, educating the next generation of conservation scientists, or delivering petabytes of ornithological data to both researchers and the public, the Cornell Lab is prepared to turn data into knowledge, at scale and around the world.

Challenge

Deliver high-availability, scalable citizen science applications serving a global user-base, while maintaining open data crucial to research and conservation, and facilitating developer agility.

Solutions

Mirantis Kubernetes Engine, Mirantis Container Runtime, and the Mirantis Secure Registry deployed on premises, allowing the organization to improve reliability, scalability, and developer workflows while leveraging institutional resources.

Results

Accelerated development, reduced risk, economical infrastructure deployed as code, and highly availability and scalable applications.

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