Built at LinkedIn, Pinot is an open source, distributed, and scalable OLAP data store that we use as our de-facto near-real-time analytics service. We’ve previously discussed how and why we built Pinot to power a wide spectrum of use cases, including internal business intelligence dashboards to analyze highly-dimensional data and “Who Viewed My Profile” to...
Data Articles
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- Topics:
- Pinot,
- metrics,
- Data,
- Open Source
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Serving the most relevant information for LinkedIn members in the homepage feed requires a massive effort—hundreds of features are used to personalize content for hundreds of millions of members. For each homepage visit, our machine learning models have to find and surface the best activity across a member's whole network, and they have to source that content...
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Co-authors: Guillaume Saint-Jacques, Amir Sepehri, Nicole Li, and Igor Perisic Introduction Previously on this blog, we’ve shared information on best practices in data science, particularly in areas such as A/B testing. We’ve also discussed the importance of ethics in fields such as data science, early implementations of “fairness by design” principles in our...
- Topics:
- A/B Testing,
- data science,
- experimentation,
- research,
- Product Design,
- Data,
- machine learning,
- T-REX
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Co-authors: Walaa Eldin Moustafa, Wenye Zhang, Adwait Tumbde, Ratandeep Ratti Introduction Over the years, the popularity of Apache...
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Co-authors: Arun Swami, Sriram Vasudevan, Sailesh Mittal, Jiefu Zheng, Joojay Huyn, Audrey Alpizar, Changling Huang, Maneesh Varshney,...
- Topics:
- scale,
- Developer Productivity,
- tools,
- Data
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Co-authors: Xiang Zhang and Chuck Jerian Espresso is LinkedIn’s document-oriented, highly available, and timeline-consistent...
- Topics:
- Performance,
- Data,
- ESPRESSO