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Apache Kafka® and Stream Processing at Pinterest

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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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The software will allow unlimited-time usage of commercial features on a single Kafka broker. Upon adding a second broker, a 30-day timer will automatically start on commercial features, which cannot be reset by moving back to one broker.

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