Brooklin - a stateless and 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?...
infrastructure Articles
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- 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 such as LinkedIn's Who viewed my profile, Talent insights, and more. Pinot uses Apache Helix for managing cluster resources and Apache Zookeeper to store metadata. Pinot has wide adoption at LinkedIn, ranging from internal...
- 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 diverse data. Protecting, maintaining, and serving data has always been of paramount importance for enriching the member experience and ensuring service reliability. In this blog post, we’ll address a critical part of data...
- Topics:
- scale,
- infrastructure,
- Developer Productivity
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In this blog post, we’ll share how we migrated Espresso, LinkedIn’s distributed data store, to a new Netty4-based framework and...
- Topics:
- infrastructure,
- Distributed Systems,
- ESPRESSO
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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
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LinkedIn’s infrastructure has grown remarkably over the years, and that has kept the Production Infrastructure Engineering teams on...
- Topics:
- infrastructure,
- scale