Description
In this episode of Rust in Production, we reflect on the inaugural season, sharing insights from companies pioneering with Rust to shape the future of infrastructure. Starting with Rust's early days before the stable compiler, we chart the language's evolution and its growing adoption in production by leading companies. The episode explores why companies choose Rust, highlighting its unparalleled safety features, concurrency management, and performance that not only drives down costs but significantly enhances user experience. The narrative then shifts to the practicalities of adopting Rust, including learning resources, the importance of community support, and integrating Rust into existing codebases. We discuss the surprisingly positive experience of hiring Rust developers, thanks to the language's appealing prospects. Moreover, the episode delves into Rust's explicit approach to handling complexity, offering a steeper yet rewarding learning curve. We also address the misconception of Rust's limited adoption, revealing its widespread use across various industries and the consensus around certain Rust patterns and libraries. Despite the challenges in showcasing Rust's adoption due to the reticence of some companies to share their stories, the positive feedback from the podcast's audience underscores a keen interest in Rust's real-world applications. Concluding the season, we extend a heartfelt thanks to all guests and listeners, emphasizing the value of sharing Rust experiences and the potential for further growth in the Rust ecosystem. We share a wishlist for the Rust community, including expanded learning resources, improved tooling for production environments, and more public discussions of Rust's benefits. As the show gears up (see what we did there?) for its second season, listeners are encouraged to engage and suggest topics or guests, which could broaden our common understanding of Rust in production.
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Published 11/14/24
Picture this: Your organization's data infrastructure resembles a busy kitchen with too many cooks. You're juggling Kafka for messaging, Flink for processing, Spark for analytics, Airflow for orchestration, and various Lambda functions scattered about. Each tool excellent at its job, but together...
Published 10/31/24