Co-authors: Yu Li, Szymon Gizecki, and Chinmaya Dattathri To ensure we have significant flexibility in how our teams collaborate, our trunk-based engineering development workflow manages dependencies on a binary level, instead of source level. This requires very efficient and sophisticated management of the resulting dependency graph, and we discussed our...
scale Articles
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The pursuit of our mission to connect the world’s professionals to make them more productive and successful is deeply dependent on the technology and infrastructure we build and maintain. Ten years ago, we had 50 million members. Fast forward five years and that number jumped to 300 million. Today, we have more than 645 million members, 20 million jobs and...
- Topics:
- scale,
- infrastructure
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Brooklin - a distributed service for streaming data in near real-time and at scale - has been running in production at LinkedIn since 2016, powering thousands of data streams and over 2 trillion messages per day. Today, we are pleased to announce the open-sourcing of Brooklin and that the source code is available in our Github repo! Why Brooklin? At LinkedIn,...
- Topics:
- scale,
- Stream Processing,
- infrastructure,
- Kafka,
- Open Source
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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,
- infrastructure,
- Developer Productivity
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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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At LinkedIn, we are entrusted with protecting the privacy and data of our members. Storing members’ choices on how they intend their...
- Topics:
- infrastructure,
- scale