"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 to over 1.2 billion users, increasing workload to 32 trillion records daily and 17 petabytes of data. Apache Kafka, originally developed at LinkedIn, encountered limits under exponential growth and growing operational complexity. Operational tooling and existing Kafka deployments struggled to manage the scale and reliability requirements. LinkedIn developed Northguard and Xinfra as replacements to address scalability, data volume, and operational overhead. The new systems focus on handling extreme throughput, simplifying operations, and accommodating complex distributed systems demands. The scale considerations expose architectural limits and motivate purpose-built infrastructure for hyper-scale event streaming.
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