f19cc81772
Closes: https://github.com/fleetdm/fleet/issues/41799 Changes: - Added support for a new required meta tag for anonymous case study articles: `cardTitleForCustomersPage`. The value of this meta tag is used as the title of the automatically generated card link for the article on the /customers page. - Added support for a new meta tag for anonymous case study articles: `cardBodyForCustomersPage`. If provided, the card link for the article will use this value for the body text, if not provided, the card link will display the `articleTitle` meta tag value. - Updated the /customers page to automatically create card links for case study articles that have `useBasicArticleTemplate` and `cardTitleForCustomersPage` meta tags.
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Top gaming company enhances server observability with Fleet
Fleet's extremely wide and diverse set of data allows us to answer questions that we didn't even know we had. On top of that, the experience is near instantaneous. Seconds to sort through billions of data points and return the exact handful that we need, with complete auditing and transparency. We're able to address reliability and compliance concerns without sacrificing a single point-of-a-percent of performance for our servers. All of this done consistently and continuously.
— Principal Infrastructure Engineer at top gaming company
Challenge
The leading gaming company was looking for better visibility into an expansive server infrastructure without impacting the performance for millions of users. Existing tools would either leave gaps in visibility or require incredible amounts of manual intervention to make sure configurations were set to specification.
Solution
Fleet is designed to scale seamlessly from tens of servers to hundreds of thousands of servers with negligible performance impact. This dramatically simplifies gathering data for compliance audits and makes it possible to build more advanced security paradigms.
Results
Fleet scaled out of the box, from managing tens to hundreds of thousands of servers.
They were able to get real-time observability across every enrolled server, even within previous blindspots
They can now quickly answer complex questions, providing near-instantaneous access to precise data points with complete auditing and transparency across multiple teams.
They reduced the need for manual interventions and were able to integrate Fleet easily with their existing tools.
By switching to Fleet, they were able to save time utilizing Fleet's native automations, instead of writing logic manually and incorporating previous blind spots into their security program. With real-time data insights across hundreds of thousands of servers, they were able to answer questions before they had them, all without sacrificing performance or reliability.
Their story
A leading online platform for user-generated games faced significant challenges in managing and observing its extensive server infrastructure. They were looking to:
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Manage a rapidly growing fleet of servers, with deployments scaling up to 100,000 servers, each with substantial memory and processing capabilities.
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Ensure server observability within edge data centers.
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Avoid even more fragmented processes and reduce the overhead of managing their servers.
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Facilitate easier data exports and integration with other systems, such as Splunk
To address these challenges, they adopted Fleet for server observability, leveraging its report engine and open API to enhance its infrastructure management alongside:
Scalable deployment
Fleet’s architecture ensures minimal performance impact even as the server count grows exponentially.
Comprehensive security and compliance
The gaming company now utilizes Fleet’s customizable compliance checks and vulnerability assessments to maintain high-security standards across multiple teams.
Robust API and integration
Fleet API and webhook support enables automation and integration with their existing systems, eliminating the need for additional middleware and reducing reliance on manual configurations.
Advanced data handling
Fleet’s ability to handle large data sets efficiently allows them to perform complex queries and generate accurate inventories of software components, everything from different Python versions to identified vulnerable software across varying server environments.
User-friendly management
Fleet facilitates the deployment and maintenance of agents without the need for ongoing manual intervention, aligning with the goal of reducing operational overhead and enhancing reliability.
Conclusion
By adopting Fleet for server observability, they've successfully addressed scalability, security, and operational challenges within their infrastructure. Fleet’s comprehensive and automated management capabilities have enabled them to maintain high-performance standards, ensure compliance, and support their expansive and dynamic server environment. As Fleet continues to integrate with their existing systems, it remains a critical component in the company’s strategy to securely enable millions to create and exist in virtual worlds without any measurable performance hits.