How to build unified JSON search solutions in AWS

This article explains how to build a unified JSON search architecture by distributing workloads across specialized AWS services, using a movie streaming platform as a reference example.

• No single database efficiently handles JSON's conflicting demands: ACID transactions, global low latency, petabyte-scale analytics, and complex search simultaneously
• Amazon DynamoDB stores real-time user state, playback positions, and bookmarks with high-throughput writes
• Amazon DocumentDB manages flexible movie catalogs with nested, variable attributes without schema migrations
• Amazon Aurora PostgreSQL guarantees transactional integrity for billing and subscriptions using JSONB columns
• Amazon S3 serves as the foundational data lake for petabyte-scale JSON logs and telemetry
• Amazon Redshift analyzes billions of user activity events using the SUPER data type for JSON queries
• Amazon OpenSearch Service powers content discovery with full-text search, fuzzy matching, and relevance ranking
• OpenSearch Ingestion (OSI) synchronizes data across services via initial snapshots and continuous Change Data Capture
• Optimize by indexing only search-relevant fields, batching operations, deduplicating sources, and using incremental updates
• Design for eventual consistency with bounded, predictable synchronization windows based on workload requirements

This distributed architecture enables each AWS service to excel at its specific workload pattern while maintaining synchronized JSON data across operational, analytical, and discovery layers.