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Record unification at scale

Healthcare & Life Sciences

Unifying records across sources that were never designed to agree with each other.

More than 400 million athlete records, stitched together across sources that had no reason to agree on identity. Volume was never the hard part. A run that fails halfway through cannot start again from the beginning, so the pipeline sits on durable execution rather than a job scheduler, with retries and real-time visibility when a step dies mid-flight. Our published work in this sector is health and wellness data unification and the governance around it, on Databricks and Temporal.

Clients in this sector

  • Athlinks
  • ChronoTrack
  • Life Time
  • SimonMed

FAQ

Healthcare & Life Sciences: questions we get asked

Have you worked with HIPAA or clinical data?

Our published work in this sector is health and wellness data at scale rather than regulated clinical systems. What transfers is the governance: designing Unity Catalog so grants hold, keeping lineage intact through a refactor, and making applications and agents inherit permissions rather than route around them. That is at /services/data-ai-governance, and it is the same substrate a regulated estate is built on.

What made 400 million records hard?

Identity across sources that never agreed, and failure partway through a long run. Athlinks at /case-studies/unlocking-big-data-projects-with-temporal-orchestration uses Temporal so a step that dies mid-flight resumes from where it stopped, with retries and real-time visibility, instead of replaying the whole pipeline.

Talk to someone who has built this.

A technical conversation with a senior engineer who has shipped in this sector, about what you are trying to build.