Interview
summary
Describes backend ML and data pipelines at Workday and concrete steps taken to keep data correct, secure and elastic as volumes grow. Explains moving a slow search index to a streaming pipeline for horizontal scaling, using Scala and functional programming to cut low-level debugging, and adopting Kafka, Akka Streams and HTTP-based architectures. Mentions legacy-code constraints, measurable speed and scalability improvements, and ongoing planning for larger future data loads.
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