Bridging Data Lakes and Warehouses in Azure
Introduction Organizations today face an explosion of data coming from APIs, IoT devices, clinical systems, and legacy infrastructure. Healthcare providers, in particular, are burdened with integrating diverse formats such as HL7 messages, real-time streams, and structured claims data. This data variety makes it difficult to create a unified analytics platform that can support both operational reporting and advanced analytics. The traditional divide between data lakes and data warehouses adds complexity. Data lakes offer flexible, low-cost storage for all types of data, but lack transactional consistency and query performance. Data warehouses provide optimized performance for structured data, but are less adaptable to change and expensive at scale. This blog explores how Azure's powerful data services can help you bridge this divide using a Lakehouse architecture. We'll walk through practical implementation details using Azure Synapse, Event Hubs, DuckDB, Azure Blob Storage...