Schema-Agnostic Indexing with Azure Document DB

Abstract: This research explores Azure Document DB, Microsoft’s distributed NoSQL database designed to handle the rapid growth of semi-structured and unstructured data with a flexible, schema-agnostic model. We analyse its core automatic indexing engine, which enables efficient query execution without predefined schemas, making it well-suited for dynamic and evolving data structures. The study evaluates Document DB’s performance in multi-tenant environments, where diverse workloads share resources, and assesses its effectiveness in real-time data ingestion and querying. Experimental results and early production deployments across various domains demonstrate the database’s strengths in scalability and flexibility, as well as some limitations in complex query optimization. Finally, the research proposes future enhancements, including integrating AI-driven query optimization techniques, to further improve performance and adaptability in large-scale cloud-native applications.