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,...
scale Articles
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- Topics:
- scale,
- infrastructure,
- Product Design
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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: Arun Swami, Sriram Vasudevan, Sailesh Mittal, Jiefu Zheng, Joojay Huyn, Audrey Alpizar, Changling Huang, Maneesh Varshney, Adrian Fernandez Data’s value is best realized when prepared and treated correctly. However, when you’re working with data at an extensive scale, it’s not as easy to make sure that every data set has been cleaned and validated....
- Topics:
- scale,
- Developer Productivity,
- tools,
- Data
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As an engineer, your goal is for every commit to seamlessly land in production and provide a delightful experience for your customers....
- Topics:
- Continuous Integration,
- scale,
- deployment,
- 3x3
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Co-authors: Jon Lee and Wesley Wu Apache Kafka is a core part of our infrastructure at LinkedIn. It was originally developed in-house...
- Topics:
- scale,
- Kafka,
- Data,
- Open Source
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Introduction LinkedIn is committed to providing economic opportunities for every member of the global workforce, and we’re growing at...
- Topics:
- scale,
- infrastructure,
- Data