Modern Data Architecture for AI in Healthcare
What You Need to Know About Iceberg, Delta Lake, and Kafka in Azure
By Paul Swider, CEO & Chief AI Officer, RealActivity
As healthcare organizations race to harness AI for operational and clinical transformation, many are running into the same foundational challenge, their data architecture isn't ready. To build trustworthy AI and large language models (LLMs), you need scalable, modern data pipelines that support real-time ingestion, compliance-grade lineage, and flexibility for ever-changing data sources. This is where technologies like Apache Iceberg, Delta Lake, and Kafka enter the conversation—and where Microsoft Azure and Fabric offer enterprise-ready equivalents.
Let’s demystify how these foundational tools translate in the Microsoft ecosystem and why they matter for healthcare executives navigating digital transformation.
Apache Iceberg - The Lakehouse Table Format – and Its Azure Equivalent
Apache Iceberg is a modern open table format designed to handle petabyte-scale datasets with ACID guarantees. It enables clean versioning, schema evolution, and time travel—key requirements when building models or conducting retrospective audits.
While Iceberg has gained momentum across open-source platforms (Trino, Flink, Presto), Azure has embraced Delta Lake as its enterprise-native alternative. Built initially by Databricks, Delta Lake is now tightly integrated into Azure Synapse, Azure Databricks, and Microsoft Fabric—especially in the OneLake architecture.
In Microsoft Fabric, Delta Lake is the default for building lakehouse architectures—a unifying layer that allows clinicians, analysts, and executives to collaborate on the same source of truth. With Delta, we get:
Real-time updates
High-performance queries
Enterprise compliance support (e.g., audit trails and rollback)
Schema enforcement for structured and semi-structured data
For healthcare organizations, this means you can build AI pipelines that are governed, auditable, and future-ready without standing up your own Iceberg clusters.
Delta Lake in AI and LLM Workflows
When developing large language models or implementing AI copilots across hospital operations, Delta Lake plays a vital role in the ETL (Extract, Transform, Load) process. It's the layer where we clean, normalize, and version training data.
In our work with academic medical centers and NIH-funded institutions, we use Delta Lake to:
Ingest large volumes of clinical and operational data
Enforce schema consistency across evolving data formats
Enable data scientists to time-travel and compare datasets across training runs
Maintain traceability for compliance with CMS and NIH reporting
This is essential when you're building AI systems that need to explain their outputs, meet regulatory expectations, and adapt quickly.
Apache Kafka and Real-Time Streaming in Azure
Apache Kafka is the go-to technology for high-throughput, real-time data streaming. In healthcare, that could mean everything from medical device telemetry to patient registration events to EHR audit logs.
Azure offers a fully managed, Kafka-compatible service called Azure Event Hubs. It allows you to plug in Kafka clients with minimal code changes and stream data directly into your AI or analytics pipeline—without the burden of managing infrastructure.
Alternatively, if your teams are already working with native Kafka features like schema registry or stream processing (ksqlDB), Confluent Cloud on Azure provides a fully managed Kafka experience with full fidelity.
Why does this matter? Real-time streaming supports:
Patient flow optimization (ED tracking, inpatient capacity)
Operational intelligence (alerting on delays, rescheduling bottlenecks)
AI model retraining based on real-world events
For healthcare execs, this is about turning raw activity into operational foresight—at scale and in near-real time.
The Takeaway: Build AI on a Modern Data Backbone
Too often, health systems try to implement AI with legacy architecture that simply wasn’t built for it. Whether you're using copilots for administrative approvals, or building LLMs to interpret documentation, your outcomes are only as good as your data infrastructure.
Delta Lake gives you trusted, scalable, and governed data pipelines for AI training.
Event Hubs enables real-time data flow to power live decision-making.
Microsoft Fabric provides the unified platform to bring it all together—with the governance and collaboration tools healthcare demands.
In short. Let’s stop patching old pipes and start building the kind of intelligent, future-ready infrastructure our providers—and patients—deserve. Cheers!