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.

Data lake architectures represent a fundamental departure from traditional data warehousing approaches, embracing schema-on-read principles and polyglot storage strategies that accommodate the velocity, variety, and volume characteristics of modern data ecosystems. Unlike data warehouses that require upfront schema definition and ETL processes to conform data to predefined structures, data lakes preserve raw data in its native format while providing flexible analysis capabilities that adapt to evolving analytical requirements. AWS provides a comprehensive suite of services that enable sophisticated data lake implementations while managing the operational complexity traditionally associated with big data platforms.