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....
Developer Productivity Articles
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- Topics:
- scale,
- Developer Productivity,
- tools
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Co-authors: Alexander Ivaniuk, Jingbang Liu At LinkedIn, we like to say that experimentation is in our blood because no production release at the company happens without experimentation; by “experimentation,” we typically mean “A/B testing.” The company relies on employees to make decisions by analyzing data. Experimentation is a data-driven foundation of the...
- Topics:
- Developer Productivity,
- A/B Testing,
- experimentation,
- T-REX
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Editor's Note: This is the second in a series of posts describing how we improved productivity at scale—both in terms of lines of code and number of engineers—at LinkedIn. In our first post of the #ProductivityAtScale series, we shared details on how we improved build time by 400%. This post covers how we continue to improve productivity with Gradle build cache....
- Topics:
- Developer Productivity,
- gradle
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When adopting a new language at scale, there are many different factors to consider because things can change dramatically. For many,...
- Topics:
- Developer Productivity,
- Android,
- Mobile
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Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases...
- Topics:
- scale,
- Developer Productivity,
- Pinot,
- infrastructure
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With over 630 million members, the LinkedIn platform delivers thousands of features that individually serve and store large amounts of...
- Topics:
- scale,
- infrastructure,
- Developer Productivity