Read about optimizing elastic search at Lyft, rapid development with GraphQL at Netflix, a developer-musician's learnings at Okta, running kafka at scale at Confluent and IOS mobile app size optimizations at Uber.

Welcome to the first ever post/email of Eng Brew.  

Given the scale of Lyft's operations, every small optimization must be helpful. But here the author digs deeper into how they optimized elastic search operations. If you are interested in or use elastic search, it is a great read.

https://eng.lyft.com/elasticsearch-optimizations-at-lyft-b555dc020932‍

Going beyond rest with rapid development with GraphQL microservices. Take a look at a different approach than “ONe Graph To Rule Them All” 

https://netflixtechblog.com/beyond-rest-1b76f7c20ef6

This is a really interesting take by a musician who is also a developer, and how these two crafts are alike in some aspects. There is a video accompanied with the post which is a great touch!

https://developer.okta.com/blog/2021/02/24/what-being-musician-taught-me‍

This is a tagged team effort from team at Pinterest where they talk about scaling their data transportation (Kafka) layer. They share the challenges of the scale, their thought process, and how they came up with the solution. 

https://www.confluent.io/blog/running-kafka-at-scale-at-pinterest/

I love how the team at Uber talked about the business problem of large iOS app size, how user settings impact business, and why this engineering problem was so important to solve. I also really enjoyed their analytics approach to measurement and bringing it into the practice.

https://eng.uber.com/how-uber-deals-with-large-ios-app-size/