Apache Kafka® is widely used at Pinterest to power the recommendation systems for both organic and promoted content to its 200+ million monthly active users.
With the recent adoption of the Confluent Go client and Kafka Streams, Pinterest has experienced significantly improved system stability and performance of its Kafka clients and real-time processing framework, as well as improved scalability and lower maintenance costs.
In this talk, members of the Pinterest team:
- Share details of their results
- Offer lessons learned from their Confluent Go client migration
- Discuss their use cases for adopting Kafka Streams
Speakers
Liquan Pei, Software Engineer, Ads Infrastructure Team, Pinterest
Liquan Pei is currently a software engineer on the Ads Infra team at Pinterest working on building stream infrastructure for real-time budgeting, pacing and feature engineering.
Boyang Chen, Software Engineer, Ads Infrastructure Team, Pinterest
Boyang Chen is currently a software engineer on the Ads Infra Team at Pinterest also working on real-time budgeting, pacing and feature engineering.
Shawn Nguyen, Platform Engineer, Pinterest
Shawn Nguyen is currently a platform engineer at Pinterest working on building scalable, reliable data infrastructure for logging and big data systems.
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