Beyond Kafka: How LinkedIn Built Northguard & Xinfra to Scale Event Streaming for the Next Decade!

"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."

"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, increasing event volume to about 32 trillion records daily and storing roughly 17 petabytes. Apache Kafka, originally created at LinkedIn, encountered operational and architectural limits under exponential growth. The scaling pressure produced complexity beyond existing tooling, stressing partition management, throughput, storage, global replication, and operational overhead. Failure modes and manageability became harder at extreme scale. LinkedIn developed Northguard and Xinfra to replace Kafka with systems designed for extreme scale, reduced operational complexity, and improved manageability for global event streaming.

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