Systems | Development | Analytics | API | Testing

Healthcare interoperability enables clinical, administrative, and financial systems to exchange usable health information securely. It connects EHRs, payer platforms, laboratories, pharmacies, medical devices, and patient applications. In 2026, interoperability requires more than transferring data between disconnected systems. Healthcare organizations must standardize data models, resolve patient identities, govern access, maintain semantic consistency, and support real-time API-based workflows.

Your data engineer just accidentally deleted the production customer table. Again. This scenario plays out in organizations worldwide, not because of malicious intent, but because of poorly configured access controls. With data breaches and compliance violations, managing who can access what in your Snowflake data warehouse isn't optional: it's essential.

Auto-generated APIs changed how fast teams ship. Point DreamFactory at a database and you get a complete REST API in seconds: every table, full CRUD, live documentation, role-based security. For thousands of teams, that is the whole job. But auto-generated APIs mirror your schema. Your applications, and increasingly your AI agents, want something more deliberate: clean paths, shaped responses, and endpoints that match how the consumer thinks rather than how the database is laid out.

In the last few months, Confluent has released over 70 new features to our data streaming platform, ranging from new connectors to better networking connectivity to entirely new ways to build streaming applications. We are not slowing down. Confluent’s DSP can do more, across every pillar, in ways that are more powerful, more complete, and more connected than ever before.

A customer writes in asking where their order is. The AI support agent checks the account, sees the order marked shipped, and sends a reply. The order was cancelled forty minutes ago. The agent wasn't wrong about anything it could see. It was reasoning over stale data that refreshes every six hours. This is why AI projects stall. Not because the models aren't capable, but because they lack AI-ready data and a reliable view of the current state of the business.

Organizations today are under immense pressure to deliver on two critical fronts: building mission-critical, real-time operational systems and powering the next generation data science and artificial intelligence (AI) workflows with analytics-ready data. Historically, achieving both meant navigating a divided, complex architecture.