"When LinkedIn's engineers published their announcement about Northguard & Xinfra earlier this year, it sent ripples through the event streaming community. Here was the company that created Apache Kafka - the backbone of modern data infrastructure - essentially saying they'd outgrown their own creation. But this isn't just another " we built a better Kafka" story. This is about what happens when you scale from 90 million to 1.2 billion users, when your system processes 32 trillion records daily across 17 petabytes of data, & when your operational complexity grows beyond what even the most sophisticated tooling can manage."
"In 2010, when Kafka was first developed, LinkedIn had 90 million members. Today, they serve over 1.2 billion users. That's not just a linear scaling problem - it's an exponential complexity challenge that touches every aspect of distributed systems design."
LinkedIn scaled from about 90 million members in 2010 to over 1.2 billion users, driving event volumes to roughly 32 trillion records per day and 17 petabytes of data. That scale exposed Kafka's limitations in throughput, storage, and operational complexity at extreme scale. Operational complexity grew beyond what existing tooling and practices could manage, creating reliability and manageability challenges. LinkedIn built Northguard and Xinfra to address those limits by redesigning components for automation, observability, and system-level scalability. The response required rethinking distributed systems design to handle exponential complexity rather than linear growth.
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