In today’s data-driven world, organizations tend to have more data than they know what to do with. A recent study by Seagate found that 75 per cent of organizational data is unleveraged in a world where data is crucial to business success.
The problem is that the residential databases that many organizations rely on can’t handle the large amount of different data types stored within them, preventing organizations from gaining actionable insights and making the most out of their data assets.
To solve this issue, many organizations turn to NoSQL databases so they can easily keep an eye on the waves of big data in their possession and extract the value it contains within it.
What is a NoSQL database?
A NoSQL database – sometimes referred to as "not only SQL" or "non-relational" databases – is a type of database that stores and manages data differently from traditional relational databases.
NoSQL databases are built to scale horizontally by adding more servers. This makes it easier to handle increasing data volumes compared to relational databases that scale vertically by adding more powerful hardware to a single server, which can become expensive in the long run.
NoSQL databases therefore excels at handling large, complex datasets that traditional relational tables struggle with. This is especially useful for companies working with big data like social media feeds, sensor readings, or customer logs. Since NoSQL databases have a flexible schema, they can store this unstructured data "as is" without needing to force it into a rigid structure.
The flexible schema of NoSQL databases also allows developers to design and deploy applications faster. Since data structure isn't as fixed as with relational databases, there's less need to pre-define everything upfront. This can be a big advantage for companies that need to adapt quickly to changing requirements.
NoSQL databases vs SQL databases
Unlike relational databases with predefined table structures, NoSQL databases offer flexible schemas. This means data can be stored in various formats like documents, key-value pairs, wide-column stores, or graphs, depending on the specific NoSQL, allowing you to store data that doesn't fit neatly into rigid relational tables and easily adapt to evolving data structures.
NoSQL databases are also built to scale horizontally. By adding more commodity servers to the cluster, you can easily handle increasing data volumes.
This contrasts with relational databases that typically scale vertically by adding more powerful hardware to a single server, which can become expensive and complex to manage in the long run.
NoSQL vs SQL: Key Benefits
NoSQL databases can outperform relational databases for certain types of queries, particularly when dealing with large datasets. This is because NoSQL is built are built for horizontal scaling and can be easily optimized for specific data access patterns.
The flexible schema of NoSQL databases also allows for faster development cycles, allowing you to store data in various formats, including unstructured, semi-structured, and structured data. This makes them ideal for complex data that doesn't fit neatly into rigid relational tables. Since data structures aren't rigidly defined upfront, developers can adapt to changing requirements more easily.
With NoSQL databases, you can easily add more commodity servers to handle growing data volume, making them a good choice for applications that expect significant data increases. NoSQL databases are often designed with high availability in mind too, meaning they can stay operational even if some hardware components fail.
These features make NoSQL databases a good choice for applications that:
- Handle large and unstructured datasets, like social media posts or sensor data.
- Have data models that are constantly growing and evolving.
- Require high performance for specific queries on massive datasets.
- Need a flexible and agile development environment.
Best NoSQL databases
There are a variety of NoSQL databases available today, each of which can help your organization make sense of your large amounts of data faster and more efficiently.
In this list, we’re counting down ten of the best NoSQL databases on the market today, ranking each pick on its range of features, ease of use and scalability.
Cassandra
Apache Cassandra is a highly scalable and fault-tolerant NoSQL database designed to handle massive amounts of data spread across many commodity servers. Instead of rows and columns in a fixed table structure, Cassandra uses a wide-column store. This means each row can have a different set of columns, allowing for flexible data structures and easy accommodation of new data types. It’s also a distributed database, meaning it runs on multiple servers (nodes) working together as a cluster, giving you horizontal scalability by adding more nodes to handle increasing data volumes.
With no single point of failure, Cassandra can continue operating even if some nodes fail. Data is replicated across multiple nodes, ensuring redundancy and accessibility. It also guarantees "eventual consistency," meaning all nodes will eventually have the same data after a short period. Cassandra can deliver fast read and write performance, particularly for large datasets, making it suitable for applications requiring high throughput and low latency.
ScyllaDB
ScyllaDB is an open-source NoSQL database specifically designed for high performance and scalability. Like Cassandra, ScyllaDB uses a wide-column store for flexible data models. This means rows can have different sets of columns, allowing you to store various data types and easily adapt to changing data structures. SkyllaDB is also distributed across multiple servers (nodes) working together as a cluster. This enables horizontal scalability by adding more nodes to handle increasing data volumes, offering high availability while remaining operational even during node failures.
ScyllaDB is known for its exceptional read and write speeds, often exceeding many competitors’ performance for specific workloads. This is achieved through optimizations in areas like data access and memory management. ScyllaDB offers lower latency compared to many other of the top NoSQL databases available today, making it ideal for applications requiring real-time data access and responsiveness.
MarkLogic Server
MarkLogic Server is a unique NoSQL document database specifically designed for handling complex data structures and managing large volumes of unstructured information. It offers a powerful combination of features that cater to information-intensive applications and content management needs. MarkLogic Server is specifically designed for storing and managing XML data. It provides native XML querying capabilities and optimizes storage and retrieval for XML documents. You can also define schemas for your data but still accommodate variations within the document structure.
MarkLogic Server excels at full-text search, allowing you to search across entire documents or specific elements within them. This makes it powerful for applications requiring in-depth content search functionalities. Unlike some schema-less NoSQL options, MarkLogic Server also allows you to define schemas for your data. This helps enforce data integrity and enables powerful search and querying capabilities. However, it still offers more flexibility than rigid relational database schemas.
Couchbase Server
Couchbase Server is an incredibly popular NoSQL database known for its versatility, scalability, and performance. Unlike some NoSQL options that specialize in a single data model, Couchbase Server offers a multi-model approach, allowing you to store and manage data in various formats. Couchbase Server can be also deployed in a distributed cluster, allowing horizontal scaling by adding more nodes to handle increasing data volumes and user traffic. Data is replicated across multiple nodes for redundancy and fault tolerance too, meaning that even if some nodes fail, the database remains operational and data remains durable.
With Couchbase, you can store data in JSON documents with flexible schemas, making it ideal for complex data structures. This document-oriented model and flexible schemas allow you to store data without rigid table structures, making it easier to accommodate evolving data models and unforeseen data types. Couchbase Server also integrates with Memcached, a popular in-memory caching system. This enables caching of frequently accessed data for faster retrieval, improving application performance.
IBM Cloudant
IBM Cloudant is a popular NoSQL database offered by IBM as a cloud-based service (DBaaS) on the IBM Cloud platform. It's a full-featured JSON document store designed for web, mobile, and serverless applications that require high performance, scalability, and flexibility. Cloudant stores data in JSON documents, a flexible and human-readable format. The database is also specially designed for horizontal scaling. You can add more servers (instances) on-demand to handle growing data volumes and user traffic. This elasticity makes it suitable for applications with unpredictable data growth.
Cloudant replicates data across multiple geographically dispersed data centres, providing high availability and data durability. This means your data remains accessible even during hardware failures and outages. IBM also provides data centres across multiple regions, allowing you to choose a deployment location that best suits your application's needs and regulatory requirements. Cloudant integrates seamlessly with other IBM Cloud services like IBM Cloud Functions and IBM Watson services, enabling the creation of powerful and scalable applications.
Raven DB
RavenDB is a document-oriented NoSQL database developed by Hibernating Rhinos. It offers a unique combination of features, including flexibility, high performance, and ACID (Atomicity, Consistency, Isolation, Durability) guarantees, which are typically associated with relational databases. Unlike many NoSQL options, RavenDB guarantees ACID properties, ensuring data consistency and integrity for critical operations. At the same time, it offers a flexible schema, and you can evolve your data structures over time without needing to modify the database schema itself. The combination of flexible schema design and ACID guarantees makes the NoSQL a compelling option for applications requiring both adaptability and data consistency.
Along with its impressive set of features, RavenDB provides a user-friendly web-based studio for managing your database, and simplifying tasks like data exploration, indexing, and user management. It also supports LINQ (Language Integrated Query), a familiar query language for developers comfortable with .NET. This simplifies querying and manipulating data within your documents. RavenDB offers a free open-source Community Edition and paid commercial editions with additional features and support.
Azure Cosmos DB
Azure Cosmos DB is a globally distributed, multi-model NoSQL database within Microsoft’s Azure Cloud that’s specially designed for high availability, scalability, and low latency access to data for modern applications. Unlike many NoSQL options focused on a single data model, Azure Cosmos DB provides flexibility by supporting multiple data models, including document, key-value, wide-column and graph. It also supports various open APIs like SQL API, MongoDB API, Cassandra API, and JavaScript object notation (JSON) for interacting with your data, providing developers with familiarity with different data access patterns to work comfortably with the database.
Coming from an industry giant Azure Cosmos DB provides predictable performance for your applications through guaranteed low latency and consistent reads/writes that other NoSQL providers simply can’t compete with. It’s globally distributed too, allowing it to replicate your data across geographically distributed Microsoft data centres so that your data remains accessible even during regional outages. You can configure the level of consistency (strong, eventual) for reads and writes based on your application's needs. This allows you to balance data consistency with performance requirements.
Astra DB
Astra DB, developed by DataStax, is a cloud-native Database-as-a-Service (DBaaS) built on top of the popular open-source NoSQL database Apache Cassandra. It offers a managed service experience for Cassandra, allowing you to leverage its scalability and fault tolerance without the burden of managing the underlying infrastructure. Unlike traditional Cassandra deployments, Astra DB is built specifically for the cloud environment. This means it takes advantage of cloud infrastructure features for automatic scaling, high availability, and simplified management.
You don't need to worry about server provisioning, software installation, or ongoing maintenance either. DataStax, the company behind AstraDB, handles all the infrastructure management, allowing you to focus on developing and deploying your applications. Unlike many other of the top NoSQL databases on the market, Astra DB is cloud-native. This makes it ideal for cloud environments, enabling it to leverage cloud infrastructure and services for better scalability, performance, and easier management compared to self-hosted Cassandra deployments. Astra DB also integrates with vector search capabilities, enabling complex and context-sensitive searches across diverse data formats, potentially useful for applications like recommendation engines or natural language processing. At the same time Astra DB is also incredibly scalable – you can easily scale your database up or down based on your data volume and application needs.
Amazon DynamoDB
Amazon DynamoDB is a serverless, key-value and document database offered by Amazon Web Services (AWS) that’s designed for high performance and scalability at any size. It's a popular choice for modern applications requiring ultra-fast data access and the ability to handle massive datasets and user traffic. As a serverless NoSQL database, DynamoDB primarily functions as a key-value store, allowing you to access data using unique keys. However, it also supports storing data in JSON documents within items, offering some flexibility for complex data structures. The database takes care of server provisioning and management, you simply define your tables and use the database, eliminating infrastructure management overhead for developers. Scaling is automatic too – the database scales capacity on-demand to handle growing data volumes and user traffic, so the need to manually provision servers for scaling.
While primarily a key-value store, DynamoDB offers limited support for ACID (Atomicity, Consistency, Isolation, Durability) transactions for specific use cases requiring strong data consistency. It also offers optional Global Tables, a feature that replicates your data across multiple AWS regions for high availability, disaster recovery, and geographically distributed applications. And for those already using AWS, Dynamo DB integrates seamlessly with other AWS services like AWS Lambda, Amazon Kinesis, and Amazon Cognito, simplifying application development within the AWS ecosystem.
MongoDB
MongoDB is arguably one of the most popular NoSQL databases on the market today, known for its ease of use, scalability, and flexible document model. Like many other of the most popular NoSQL databases, it stores data in flexible JSON-like documents, allowing you to store complex data hierarchies and various data types within a single document. This flexible schema approach makes it easier to adapt to evolving data models without rigid table structures, helping organizations manage the different data types in a database. MongoDB can also be scaled horizontally by adding more servers (shards) to the cluster. This allows you to handle increasing data volumes and user traffic efficiently.
MongoDB offers a powerful query language (MQL) similar to JavaScript for querying and manipulating data within your documents. This makes it easier for developers familiar with JavaScript to work with the database. It provides an impressive range of developer tools, drivers, and libraries for various programming languages too to simplify development and integration with your application. The document-oriented model and JSON-like syntax also make MongoDB easy to learn and use for developers familiar with JSON and web development concepts. MongoDB has a large and active community, offering extensive documentation, tutorials, and various third-party tools and libraries to support development.
