Editor's note: This blog has been updated. Background Like many internet companies, LinkedIn has faced data growth challenges. Naturally, distributed storage systems became the solution to handle larger volumes of data and queries per second (QPS). But, aside from scaling issues, the variability in access patterns also grew quickly. For example, some scenarios...
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Two years ago we hit a wall. The scale of LinkedIn’s data was growing beyond what we could analyze. At the same time, our members needed their analytics and insights in real-time. We needed a solid solution that would grow with LinkedIn and serve as the platform to power all of our analytics needs across the company at web-scale. In this post, we will share how...