Co-authors: Walaa Eldin Moustafa, Wenye Zhang, Sushant Raikar, Raymond Lam, Ron Hu, Shardul Mahadik, Laura Chen, Khai Tran, Chris Chen, and Nagarathnam Muthusamy Introduction At LinkedIn, our big data compute infrastructure continually grows over time, not only to keep pace with the growth in the number of data applications, or their domains spanning data...
scale Articles
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- Topics:
- scale,
- Apache Pig,
- Data,
- Dali,
- Open Source
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Editor’s note: This blog post is the first in a series providing an overview and history of LinkedIn’s experimentation platform. At any given time, LinkedIn’s experimentation platform is serving up to 41,000 A/B tests simultaneously on a user population of over 700 million members. Operation at such a scale is enabled with the LinkedIn Targeting, Ramping, and...
- Topics:
- scale,
- A/B Testing,
- infrastructure,
- T-REX,
- experimentation
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Co-authors: Scott Meyer, Andrew Carter, and Andrew Rodriguez Editor’s note: This is the second part of a two-part blog series. Part 1 described how graph data relates to the in-memory graph of structures commonly used as models (as in “model-view-controller”) by applications, how graph data can be stored in a conventional relational database, and why...
- Topics:
- scale,
- infrastructure,
- knowledge graph,
- Data
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Co-authors: Scott Meyer, Andrew Carter, and Andrew Rodriguez Editor’s note: In this two-part blog series, we introduce LIquid, a new...
- Topics:
- scale,
- infrastructure,
- knowledge graph,
- Data
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Co-authors: Viranch Mehta, Jon Sorenson, Samir Jafferali As LinkedIn has grown to more than 690 million members, we’ve expanded our...
- Topics:
- scale,
- infrastructure,
- SRE
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Co-authors: Jefferson Lai, Vinyas Maddi, and Vijay Ramamurthy One of our core engineering principles here at LinkedIn is to create...
- Topics:
- scale,
- infrastructure,
- Product Design