Co-authors: Jefferson Lai, Vinyas Maddi, and Vijay Ramamurthy One of our core engineering principles here at LinkedIn is to create leverage. In practice, what this means is to build software that is easy to reuse by other teams. An effective way we are able to do this is to favor platforms that generalize long-term functionality rather than build inflexible,...
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- Topics:
- scale,
- infrastructure,
- Product Design
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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
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Co-authors: Walaa Eldin Moustafa, Wenye Zhang, Adwait Tumbde, Ratandeep Ratti Introduction Over the years, the popularity of Apache Spark at LinkedIn has grown, and users today continue to leverage its unique features for business-critical tasks. Apache Spark allows users to consume datasets using powerful, yet easy-to-use APIs such as the Dataset interface. The...
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Co-authors: Suruchi Shah and Hari Shankar The rise of instant messaging has changed how we communicate. Compared to the back-and-forth...
- Topics:
- Performance,
- infrastructure,
- Architecture,
- Search
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As a step to help reduce the spread of COVID-19, many companies are now encouraging employees to work remotely. Supporting a workforce...
- Topics:
- infrastructure
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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