What is NoSQL?

NoSQL databases generally provide flexible schemas that enable faster and more iterative development. The flexible data model makes NoSQL databases ideal for semi-structured and unstructured data.

NoSQL databases are typically designed to scale out by using distributed clusters of hardware, as opposed to scaling up by adding expensive and robust servers. Some cloud providers handle these operations behind the scenes as a fully managed service.

NoSQL databases are optimized for specific data models and access patterns. These enable higher performance than if you were trying to accomplish similar functionality with relational databases.

NoSQL databases provide highly functional APIs and data types that are purpose-built for each of their respective data models.

You can deliver real-time recommendations, personalization, and improved user experiences with NoSQL databases. For example, Disney+ delivers its extensive digital content library to over 150 million+ subscribers using NoSQL database technology. It can scale and deliver popular features such as Continue Watching, Watchlist, and Personalized Recommendations with Amazon DynamoDB.

You can use graph databases to quickly discover complex relationships within your data. For instance, Wiz re-imagined cloud security as a graph using Amazon Neptune. Wiz helps their customers improve their security posture by quickly identifying and fixing the most critical risks. They use graph model stored in Amazon Neptune to uncover the toxic combination of risk factors that represent critical risks. The Wiz risk engines traverse the graph and within seconds, weave together a series of interconnected risks factors in a security graph.

Distributed NoSQL databases are excellent for building high-availability, low-latency applications for messaging, social media, file sharing, and more. For example, Snapchat has more than 290 million users sending billions of pictures and video messages daily. It uses NoSQL database systems to reduce the median latency of sending messages by 20%.

Consider this example of modeling the schema for a simple book database:

• In a relational database, a book record is often disassembled (or “normalized”) and stored in separate tables, and relationships are defined by primary and foreign key constraints. In this example, the Books table has columns for ISBN, Book Title, and Edition Number; the Authors table has columns for AuthorID and Author Name; and finally, the Author-ISBN table has columns for AuthorID and ISBN. The relational model is designed to enable the database to enforce referential integrity between tables in the database, normalized to reduce the redundancy, and generally optimized for storage.

• In a NoSQL database, a book record is usually stored as a document. For each book, the item, ISBN, Book Title, Edition Number, Author Name, and AuthorID are stored as attributes in a single document. In this model, data is optimized for intuitive development and horizontal scalability.

Key-value databases are highly partitionable and allow horizontal scaling at a level that other types of NoSQL databases may not achieve. A key-value database stores data as a collection of key-value pairs in which a key serves as a unique identifier. Keys and values can be anything, ranging from simple objects to complex compound objects. Use cases such as gaming, ad tech, and IoT lend themselves particularly well to the key-value store data design. Amazon DynamoDB is designed to provide consistent performance with single-digit millisecond latency for any scale of workloads. 

Document databases have the same document model format that developers use in their application code. They store data as JSON objects that are flexible, semi-structured, and hierarchical in nature.. The flexible, semistructured, and hierarchical nature of documents and document databases allows them to evolve with applications’ needs. The document database model works well with catalogs, user profiles, and content management systems, where each document is unique and evolves over time. Amazon DocumentDB (with MongoDB compatibility) and MongoDB are popular document databases that provide powerful and intuitive APIs for flexible and iterative development.

Graph databases are purpose-built to make it easy to build and run applications that work with highly connected datasets. They use nodes to store data entities and edges to store relationships between entities. An edge always has a start node, end node, type, and direction. It can describe parent-child relationships, actions, ownership, and the like. There is no limit to the number and kind of relationships a node can have. You can use a graph database to build and run applications that work with highly connected datasets. Typical use cases for a graph database include social networking, recommendation engines, fraud detection, and knowledge graphs. Amazon Neptune is a fully-managed graph database service supporting both the Property Graph model and the Resource Description Framework (RDF) with the choice of two graph APIs (TinkerPop and RDF/SPARQL).

While other non-relational databases store data on disk or SSDs, in-memory data stores are designed to eliminate the need to access disks. They are ideal for applications that require microsecond response times or have large spikes in traffic. You can use them in gaming and ad-tech applications for features like leaderboards, session stores, and real-time analytics. Amazon MemoryDB for Redis is a Redis-compatible, durable, in-memory database service that delivers microsecond read latency, single-digit millisecond write latency, and Multi-AZ durability. Amazon ElastiCache is a fully managed, in-memory caching service compatible with both Redis and Memcached, to serve low-latency, high-throughput workloads. Amazon DynamoDB Accelerator (DAX) is another example of a purpose-built data store that makes DynamoDB reads an order of magnitude faster.

A search-engine database is a type of non-relational database that is dedicated to the search of data content, such as application output logs used by developers to troubleshoot issues. They use indexes to categorize similar characteristics among data and facilitate search capability. Search-engine databases are optimized for sorting unstructured data like images and videos. Amazon OpenSearch Service is purpose-built for providing near-real-time visualizations and analytics of machine-generated data by indexing, aggregating, and searching semistructured logs and metrics.