Elasticsearch and MongoDB are two leading distributed datastores designed for handling NoSQL data, each with distinct strengths and ideal use cases. Elasticsearch, an open-source search engine built on Apache Lucene, excels in full-text search, log analytics, and anomaly detection, offering features like distributed search, RESTful interfaces, and integration with tools like Kibana. It is particularly suited for scenarios where complex search queries and geo-search capabilities are crucial. Conversely, MongoDB is a document-oriented database known for its scalability and high throughput in handling large datasets, making it optimal for applications requiring CRUD operations without the need for rapid full-text search. Written in C++, MongoDB provides features such as built-in security, schema-less design, and support for various data models, making it prominent in document store databases. Both technologies offer robust backup and recovery options, but they differ in handling relational data and data storage architecture, with Elasticsearch focusing on immutable Lucene segments and MongoDB utilizing a memory map file for efficient data management. While Elasticsearch is written in Java and offers a multitude of client support languages, MongoDB also provides extensive language support and operates under a Server Side Public License (SSPL), with advanced features available in its enterprise edition.