Elasticsearch can function as a NoSQL database, offering flexibility, scalability, and performance as a search and analytics engine, although it does not fully align with the traditional characteristics of NoSQL databases. It operates without typical transactions, instead relying on a write-ahead-log for durability, and is schema-flexible, allowing it to infer data types from JSON documents while offering tools for schema customization. As a document-oriented database, Elasticsearch excels in write-once-read-many workloads, though it requires denormalization, which can complicate updates. The system is designed for distributed environments, making it easy to scale, but it lacks built-in security features and robustness against certain errors, such as OutOfMemory. Elasticsearch is often used alongside other databases that handle constraints and transactional data, serving as a powerful tool for search capabilities with features such as caching, relevancy models, and spell-checking.