Modernizing document storage for search, scale, and agility

This article explains how to recognize when document data has outgrown relational databases and guides teams on migrating to Amazon DocumentDB or DynamoDB based on their access patterns.

• Relational databases struggle with querying nested JSON fields, requiring workarounds like ETL pipelines, caching layers, and search sidecars
• Amazon DocumentDB suits workloads that filter and search by fields inside documents with flexible, evolving query patterns
• Amazon DynamoDB fits key-based access patterns requiring high throughput and consistent single-digit millisecond latency
• Migration follows a three-phase pattern: bulk load, change data capture for sync, and incremental traffic shift with canary releases
• The end-state architecture is hybrid, with relational databases retaining transactional data while document stores handle their respective access patterns

Progressive migration strategies enable teams to move document data without risky full cutovers, replacing workaround infrastructure with purpose-built storage models.