Cerner’s Population Health platform, HealtheIntent, brings together the world’s healthcare data to derive the complete picture of a person’s health. This means normalizing and standardizing healthcare data from a variety of source systems, including electronic medical records, physician ordering systems, and even payer billing and claims systems.
The HealtheIntent platform grew up as a batch system. Most of the processing occurred hourly. Some data even landed only daily or monthly. While there were some technical and operational challenges, the batch world was (mostly) forgiving and (mostly) predictable. Then everything changed.
Over the last year, Cerner has shifted its Hadoop data processing pipeline to a streaming one using Storm and Kafka. The change was born out of a need to have a near-real-time system, so the decision to shift the architecture was easy. In terms of the impact to the design and the code, on the surface the change seemed straightforward, but in the end, it required that the team rethink everything: support, monitoring, alerting, performance measurement, and even team structure and associate onboarding. Michelle Brush shares the lessons learned executing a large-scale architectural migration and provides advice on how to minimize the impact.
Michelle Brush is a math geek turned computer geek with 15 years of software development experience. Michelle has developed algorithms and data structures for search, compression, and data mining in embedded as well as enterprise systems. In her current role as an engineering director for Cerner Corporation, Michelle leads teams that develop the platform for ingesting stream and batch data specific to Cerner’s Population Health solutions. She is also the chapter leader for the Kansas City chapter of Girl Develop It and one of the conference organizers for Midwest.io.
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