Create a flexible, scalable NoSQL database with DigitalOcean 

Easily manage complex datasets with DigitalOcean’s NoSQL database offerings

NoSQL databases on DigitalOcean

DigitalOcean offers two managed NoSQL database offerings that enable developers to build fast, scalable, high-performance applications without worrying about database management. Choose from Managed MongoDB or Managed Redis to bring your application to market more quickly while benefiting from a flexible, scalable NoSQL database solution. Benefits of DigitalOcean’s NoSQL database options include

  • Fully managed database solution so you can focus on building your application
  • End-to-end security so that access to nodes is restricted to whitelisted sources
  • Automatic updates so you’re always running the latest database version
  • Minimal downtime and automated failovers with standby nodes
  • Fast, reliable performance on DigitalOcean Droplets, our virtual machines

Additionally, when you use a DigitalOcean NoSQL database you’ll get all of DigitalOcean’s other benefits built-in, including predictable pricing, a vast library of documentation and tutorials, and a simple user interface. You can also seamlessly integrate DigitalOcean NoSQL databases with other products including App Platform and DigitalOcean Kubernetes.

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Sign up for our Managed Redis or Managed MongoDB NoSQL databases today.

What is a NoSQL Database?

NoSQL databases, also known as non-relational databases, are databases that allow for unstructured data, as opposed to traditional relational SQL databases which require data to be structured into tables. NoSQL databases have risen in popularity as modern applications have become more complex, requiring the storage and retrieval of data that is unstructured, semi-structured, and polymorphic.

NoSQL databases have flexible data storage schemas that can easily manage and scale complex datasets. This makes them popular for use cases including Software as a Service and other web applications, gaming applications, and mobile applications that require a large amount of data. NoSQL databases are also more cloud-friendly than traditional databases, as data can be stored across multiple servers, regions, and even cloud providers. By distributing data across multiple locations, developers can scale their applications horizontally and create backups in the event of a failure.

While there are multiple types of NoSQL databases, as explained in the section below, the main benefits of NoSQL databases include:

  • Flexibility: Users can leverage multiple data storage schemas in a NoSQL database, and can be adapted to new data formats. Formats supported including text data, JSON files, lists, binary values, and complex networks of related information stored in graph formats.
  • Speed: NoSQL databases can manage large volumes of data without sacrificing performance, which means applications relying on large datasets can quickly query them.
  • Scalability: NoSQL databases can scale horizontally, by adding additional compute power in the form of additional servers and creating clusters that work together. This enables users to host databases across multiple servers and locations inexpensively, rather than scaling vertically through the use of more powerful and expensive servers.
  • Developer-friendly features: NoSQL databases support agile development workflows and are intended to make developers more efficient, by enabling them to query data through simple code. By using a NoSQL database, developers can easily store, manage and retrieve their data.

Types of NoSQL Databases

NoSQL databases typically come in four types–document databases, key value store database, wide column store databases, and graph databases. While all four are types of NoSQL database, they vary in how they store data, and some are better for certain types of application than others.

  • Document databases: These databases store data in documents which are similar to JSON files, and contain pairs of fields and values. Document databases support a range of values including number, text, objects, strings, and more, and are flexible and semi-structured. Document databases allow developers to store and retrieve data using the same document model as is in their application code, and documents can evolve as the application itself changes, which is beneficial for many application types. Because documents can include both unstructured and structured data in the same file, they support more complex data needs than some other database types.

  • Key-value databases: Key-value databases store paired keys and values, where the key is the unique identifier used to find an object. Keys and values can range in type from numbers and binary objects to more complex objects. Key-value databases are a simpler way of storing data than document databases, using a very efficient system. This allows key-value databases to perform at very high speeds and scale quickly, which makes them ideal for applications that need to quickly access large amounts of data.

  • Graph databases: Graph databases use a graph structure to store data and relationships between data. They include nodes, which are specific data points, and edges, which are relationships between the nodes, linked together in the form of a graph so that developers can easily query the relationships because they are stored together. Because they link data, graph databases are best for applications that use multiple datasets that are connected, such as social networking applications or fraud detection.

  • Wide column stores: Wide-column stores are similar to the tables found in relational databases, but store data in dynamic columns rather than rows, and each column is stored in its own file. Users of wide column stores can group columns into families to group related data together. They are similar to key-value databases in that they store data in a simple format, but enable an additional layer of complexity. Wide value stores are often used for large datasets that are distributed across multiple nodes, such as time-series data or real-time analytics.

  • In-memory databases: In-memory databases store data in the main memory (RAM) rather than on a disk, which makes them extremely fast. This type of database is used in applications such as gaming and advertising which require responses in fractions of a second, but has some risks associated with relying on RAM, which can be unstable or shut down in the event of a power outage.

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