Building modern applications often requires the ability to perform full-text searches with fuzzy matching capabilities on data that’s primarily stored in NoSQL databases like DynamoDB. While DynamoDB excels at fast key-based lookups and can handle massive scale, it lacks the sophisticated search capabilities that applications need for features like autocomplete, typo-tolerant search, and complex text analysis. OpenSearch (the open-source fork of Elasticsearch) provides these advanced search capabilities, but keeping it synchronized with your primary data store presents unique challenges.

Real-time processing architectures address the fundamental challenge of extracting actionable insights from continuously flowing data streams while maintaining low latency and high throughput requirements. Unlike batch processing systems that operate on static datasets with relaxed timing constraints, real-time systems must process events as they arrive, often within milliseconds or seconds of generation. This temporal sensitivity introduces unique design considerations around event ordering, backpressure handling, and state management that distinguish real-time architectures from their batch-oriented counterparts.

Command Query Responsibility Segregation represents a fundamental shift in how we think about data persistence and retrieval in distributed systems. Rather than treating reads and writes as symmetric operations against a single data model, CQRS acknowledges the inherent differences between these operations and optimizes each path independently. In the context of AWS services, this pattern becomes particularly powerful when we leverage the managed services ecosystem to handle the complexity of maintaining separate command and query models.