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Microservices written in Go was a key component of a new system that would enable The Economist to deliver scalable, high performing services and quickly iterate new products. Go's baked in concurrency and API support along with its design as a static, compiled language enabled a distributed eventing system. Overall, The Economist team's experience with Go has been positive experience.
This eMag will inspire you to dig deeper into your systems, question your mental models, and use chaos engineering to build confidence in your system’s behaviors under turbulent conditions.
In this article we described how Analytics Zoo can help real-world users to build end-to-end deep learning pipelines for big data, including unified pipelines for distributed TensorFlow and Keras on Apache Spark, easy-to-use abstractions such as transfer learning and Spark ML pipeline support, built-in deep learning models and reference use cases, etc.
In this podcast recorded at QCon New York, Shane Hastie, Lead Editor for Culture & Methods, spoke to Sangeeta Narayanan of Netflix about improving the developer experience, and why it matters.
Greg Burrell presents Netflix’s journey from siloed teams to their Full Cycle Developer model for building and operating their services at Netflix. He discusses the various approaches they’ve tried, the motivations that pushed them to keep evolving, and the lessons learned along the way.
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