Pinot is an open source, scalable distributed OLAP data store that entered the Apache Incubation recently. Developed at LinkedIn, it works across a wide variety of production use cases to deliver real-time, low latency analytics. One of the biggest challenges in Pinot is achieving and maintaining tight SLA on latency and throughput on large data sets. Existing...
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- Topics:
- Pinot,
- Distributed Systems,
- Open Source
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“Make LinkedIn feel instantaneous” is a mantra our performance team lives by to deliver the best possible experience to our members. We are constantly on the lookout for ways to improve our site speed and user experience, be it through optimizing our infrastructure and edge, accelerating our web and mobile applications’ performance, or tuning our systems and...
- Topics:
- Performance,
- experimentation,
- machine learning
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At LinkedIn, most decisions are made using experiments. When we want to decide between two features, we test them against each other in the real world: we give feature A to a random set of members, feature B to another set, and we compare the results. Are users of feature A more engaged? Do they have a better experience with our products? If so, feature A wins...
- Topics:
- Testing,
- A/B Testing
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Earlier this year, we published a blog post sharing details on ThirdEye, LinkedIn’s comprehensive platform for real-time monitoring...
- Topics:
- monitoring,
- metrics,
- Pinot
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At LinkedIn, we are entrusted with protecting the privacy and data of our members. Storing members’ choices on how they intend their...
- Topics:
- infrastructure,
- scale
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LinkedIn wouldn't be the company it is today without the engineers who built it and the talented individuals in technical roles across...
- Topics:
- data science,
- culture