# Tag: Linkedin

Posts tagged Linkedin.

- [How LinkedIn Built a Faster, Safer, and Smarter HDFS Ecosystem](https://scaleengineer.com/blog/how-linkedin-built-a-faster-safer-and-smarter-hdfs-ecosystem)
  - LinkedIn scaled HDFS with HA, Observer nodes, encryption & Wormhole, boosting speed, reliability & secure data access for massive growth.
- [How LinkedIn Cut Build Times from 30 Minutes to 10 Seconds](https://scaleengineer.com/blog/how-linkedin-cut-build-times-from-30-minutes-to-10-seconds)
  - LinkedIn’s RDev lets engineers code in the cloud with pre-built containers, cutting setup from 30 mins to 10 secs while keeping CI consistent.
- [How LinkedIn Made the “My Network” Tab Faster, Smoother, and More Flexible](https://scaleengineer.com/blog/how-linkedin-made-the-my-network-tab-faster-smoother-and-more-flexible)
  - LinkedIn sped up My Network by unifying APIs, adding pagination, and using a backend-driven render model, cutting latency and improving the overall UX.
- [How LinkedIn Reduced Latency and Cost by Merging Two Critical Systems](https://scaleengineer.com/blog/how-linkedin-reduced-latency-and-cost-by-merging-two-critical-systems)
  - LinkedIn merged identity midtier and data services, cutting network hops to reduce latency, memory use, and cost while keeping APIs unchanged.
- [How LinkedIn Rebuilt its Profile Highlights System](https://scaleengineer.com/blog/how-linkedin-rebuilt-its-profile-highlights-system)
  - LinkedIn rebuilt Profile Highlights into a plug-in platform, enabling faster experiments, independent teams, better performance, and ~50% lower costs.
- [EP 56: How LinkedIn Scaled to 1 billion Users?](https://scaleengineer.com/blog/how-linkedin-scaled-to-1-billion-users)
  - By shifting to microservices from monoliths, using tools like Hadoop, Kafka, Rest.li, LinkedIn scaled to a billion of users globally.
- [How LinkedIn Uses Machine Learning to Moderate Content at Scale](https://scaleengineer.com/blog/how-linkedin-uses-machine-learning-to-moderate-content-at-scale)
  - LinkedIn is using ML to prioritize content smarter, not replace humans but helping reviewers act faster, scale better, and keep the platform safe without losing judgment or nuance.
- [How LinkedIn Rebuilt Service Discovery to Scale to Millions of Services](https://scaleengineer.com/blog/how-linkedin-rebuilt-service-discovery-to-scale-to-millions-of-services)
  - LinkedIn rebuilt service discovery using Kafka and Observer, enabling scalable, push-based updates with lower latency and higher availability.
- [How LinkedIn Built Northguard and Xinfra to Move Beyond Kafka](https://scaleengineer.com/blog/how-linkedin-built-northguard-and-xinfra-to-move-beyond-kafka)
  - LinkedIn built Northguard and Xinfra to overcome Kafka's scaling limits with self-balancing storage, distributed metadata, and seamless migration.