Why Database Multi-Model Designs Are Gaining Traction

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Why Database Multi-Model Designs Are Gaining Traction

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Lucas and Luna explore why a growing number of organizations are adopting multi-model databases—systems that natively support relational, document, graph, and key-value workloads in a single engine. They examine the driving forces: the rise of polyglot persistence fatigue, the cost savings from eliminating multiple database stacks, and the practical trade-offs in query performance and operational complexity. The episode centers on a case study of a mid-sized e-commerce company that consolidated its data layer from four separate databases into one multi-model system running ArangoDB, cutting infrastructure costs by 40% while maintaining sub-50 millisecond query latencies for 90% of their workloads. Lucas explains how the technology has matured since the early days of MongoDB and Couchbase, and Luna presses on where single-model specialists still outperform. They close by discussing when a multi-model approach makes sense—and when it's a trap. #MultiModelDatabase #ArangoDB #PolyglotPersistence #DatabaseConsolidation #NoSQL #GraphDatabase #DocumentStore #KeyValueStore #InfrastructureCost #QueryPerformance #Technology #DataEngineering #CloudMigration #DatabaseArchitecture #FexingoBusiness #BusinessPodcast #TechTrends2026 #DataStorage Keep every episode free: buymeacoffee.com/fexingo

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