"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. The Scale That Broke Kafka Let's start with the numbers that matter! 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 90 million members in 2010 to over 1.2 billion users, creating exponential complexity for streaming infrastructure. Apache Kafka, created at LinkedIn, reached operational limits as the platform processed 32 trillion records daily across 17 petabytes of data. Operational complexity exceeded the capabilities of existing tooling and Kafka's design assumptions. LinkedIn engineered replacements named Northguard and Xinfra to address scale, performance, and manageability challenges. The shift reflects problems of non-linear scaling in distributed systems and the need for architectures that handle extreme throughput, massive data volumes, and evolving operational demands.
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