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...
Developer Productivity Articles
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- 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, choosing a language can arguably rely on personal preference, but at LinkedIn, we have a Foundation team tasked with evaluating the impact of such fundamental technical decisions. Recently, we underwent the process of evaluating...
- 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
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Co-authors: Joel Young, Bee-Chung Chen, Bo Long, Marius Seritan, and Priyanka Gariba The rate at which artificial intelligence (AI)...