A streaming migration playbook for Data Engineering Leads moving healthcare workloads to real-time - bounded scope, managed Kafka and Flink, and a one-workload-per-week migration cadence with the batch path running in parallel.
Identify the three to five workloads where streaming matters most. Healthcare typically picks: ED throughput, clinical alerts, eligibility verification, care gap notification, and sepsis prediction. Everything else stays batch until the streaming layer has earned the right to expand.
Deploy a managed Kafka and Flink stack. Confluent Cloud, AWS MSK with Managed Flink, or equivalent. Self-building the streaming infrastructure consumes the entire migration window before any workload ships.
Migrate one workload per week to the streaming stack. Each migration ships behind a feature flag with the batch path still running in parallel, so rollback is one toggle and the team learns the streaming stack in production before it is the only path.
Identify the three to five workloads where streaming matters most. Healthcare typically picks: ED throughput, clinical alerts, eligibility verification, care gap notification, sepsis prediction.
Deploy a managed Kafka and Flink stack. Confluent Cloud, AWS MSK with Managed Flink, or equivalent.
Migrate one workload per week to the streaming stack. Each migration ships behind a feature flag with the batch path still running in parallel.
Time. Self-building Kafka + Flink alone takes longer than the entire 13-week window. Managed gets you to production faster and the cost premium is justified by the time saved.
Same posture as the existing analytics platform — encrypted in transit and at rest, BAA with the managed provider, role-based access. The streaming layer inherits the trust boundary.
Late-arrival window per workload, with side outputs for very late records. We have run this with up to 30-minute late-arrival tolerance on AMI-style data and shorter on EHR data.
Not all of it. Streaming replaces the batch outputs that benefit from real-time. Reporting workloads on long aggregations stay batch.
Parallel operation. The batch alert and the streaming alert run side by side until reconciliation shows the streaming output matches. Only then does the batch path retire.
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