NoSQL databases are flexible data management systems that efficiently handle large volumes of unstructured and semi-structured data, offering a good option for modern applications and development workflows that require scalability and flexibility in their data models. Unlike traditional relational databases, NoSQL databases do not impose rigid data structures, allowing for more arbitrary collections of data to be stored. This flexibility often comes at the cost of consistency and transactional guarantees. Modern NoSQL databases have improved over time, providing better performance, achieving the required scale and reliability, while also tackling previously under-served unstructured data models. With a diverse range of data models, including key-value pairs, row/column stores, and JSON documents, NoSQL databases are suitable for virtually any need, enabling quicker and easier deployment of schema changes to production. They offer excellent scalability and uptime, as well as performance, by distributing their workload over multiple instances, servers, nodes, etc., and can be incredibly robust using built-in protocols to continue operating when connections or nodes fail. The query interface of NoSQL databases tends to be easier to leverage and integrate into applications, making them accelerate software development compared to older technologies. Despite the differences between NoSQL and SQL databases, modern NoSQL databases like Fauna tackle the lack of relational data modeling as well as the widespread desire for ACID-compliant transactions, providing a more balanced approach. The types of NoSQL databases include document, key-value, wide-column, and graph databases, each offering unique benefits and use cases. Examples of popular NoSQL databases include MongoDB, DynamoDB, and Fauna, which provide flexibility, scalability, and state-of-the-art functionality in their respective packages.