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gRPC Migration Automation at LinkedIn
Karthik Ramgopal and Min Chen discuss how LinkedIn changed the remote procedure calls (RPC) protocol for 50,000 production endpoints from Rest.li to Google's gRPC.
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Lessons Learned from Building LinkedIn’s AI Data Platform
Felix GV provides an overview of LinkedIn’s AI ecosystem, then discusses the data platform underneath it: an open source database called Venice.
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LIquid: a Large-Scale Relational Graph Database
Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.
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Streaming a Million Likes/Second: Real-Time Interactions on Live Video
Akhilesh Gupta talks about how Linkedin uses the Play/Akka Framework and a scalable distributed system to enable live interactions at massive scale at extremely low costs across multiple data centers.
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A Dive into Streams @LinkedIn with Brooklin
Celia Kung talks about Brooklin, LinkedIn’s managed data streaming service, and dives deeper into its architecture and use cases, as well as their future plans.
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People You May Know: Fast Recommendations over Massive Data
Sumit Rangwala and Felix GV present the evolution of PYMK’s architecture, focusing on Gaia, a real-time graph computing capability, and Venice, an online feature store with scoring capability.
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Fairness, Transparency, and Privacy in AI @LinkedIn
Krishnaram Kenthapadi focuses on the application of privacy-preserving data mining and fairness-aware ML techniques in practice, by presenting case studies spanning different LinkedIn applications.
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Nearline Recommendations for Active Communities @LinkedIn
Hema Raghavan focusses on technologies they have built to power LinkedIn’s “People You May Know” product and describes their nearline platform for notification recommendation.
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CI/CD: Lessons from LinkedIn and Mockito
Szczepan Faber talks about two different use cases of implementing continuous delivery at scale: LinkedIn and Mockito. Yet the challenges, benefits & impact on the engineering culture are very similar
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API Design Lessons Learned: Enterprise to Startup
Mohamed El-Geish explores lessons learned at big companies like Microsoft and LinkedIn, and adapts the insights drawn from them to fit a fast-growing startup.
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Front-End APIs: Powering Fast-Paced Iterations
Aditya Modi and Karthik Ramgopal explore LinkedIn’s ideas behind API modeling, the challenges they’ve faced, and how they are evolving their modeling strategy over time based on their learnings.
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Big Data Infrastructure @ LinkedIn
Shirshanka Das describes LinkedIn’s Big Data Infrastructure and its evolution through the years, including details on the motivation and architecture of Gobblin, Pinot and WhereHows.
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