The Origin Story of Apache Kafka: Solving LinkedIn's Data Integration Problem
By
enether
The kind of bagel that ruins lesser bagels for you.
Summary
This article explores the original motivation behind creating Apache Kafka at LinkedIn around 2012. It explains that Kafka was built to solve a data integration problem, specifically to handle site activity data (likes, posts, profile views) used for fraud detection, job matching, ML model training, and core website features. The article provides historical context on why LinkedIn needed a new solution and how Kafka's architecture was shaped by these real-world requirements.
Key quotes
· 3 pulledCirca 2012, LinkedIn's original intention with Kafka was to solve a data integration problem.
LinkedIn used site activity data (e.g. someone liked this, someone posted this) for many things - tracking fraud/abuse, matching jobs to users, training ML models, basic features of the website.
We talk all the time about what Kafka is, but not so much about why it is the way it is.
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