The 10 best NoSQL cloud databases
Explore the top 10 NoSQL cloud databases in this detailed guide. Discover key features, benefits, and why they stand out in a competitive market.
You may have seen our two blog posts in this three-post series, NoSQL cloud databases: Benefits and features explained and NoSQL cloud database use case guide. This is the last post, which helps you determine how to pick one. While a number of products identify themselves as NoSQL cloud databases, they’re all slightly different and have their respective specialties. Here are ten of the leading NoSQL cloud databases.
Aerospike Cloud
Aerospike Cloud is a fully managed DBaaS that helps you go from start to scale quickly. It scales alongside your applications, facilitating growth from gigabytes up to petabytes. Engineered for high throughput and low latency applications, with a reported 99.99% uptime, it exhibits resilience against complete zone failures, providing uninterrupted service.
Why it stands out:
Speed: Delivers a low-latency experience even with large datasets
Predictable performance: Aerospike Cloud’s underlying Hybrid Memory Architecture, where the index is in-memory, and data is read from the disk, provides consistent performance from gigabytes on up, regardless of which shard or replica is accessed
Resilience: 99.99% uptime even with zone failure
Top features:
High throughput real-time data processing engine
Database templates for rapid deployment
Multi-model support for key-value and document/JSON APIs
Benefits:
It speeds up apps for improved customer experiences.
Managed services reduce operational tasks.
Cloud storage provides security.
It supports high scalability for continued growth.
Cons:
Needs more sample code for developers
Needs a more granular pay-as-you-go model
Best for: Businesses that aim for high-performance applications, such as AdTech or finance, that require real-time data processing at a global scale.
Organizations that prioritize system reliability will appreciate Aerospike's system designed for 99.99% uptime and low latency. Aerospike Cloud works well for critical applications such as fraud detection and customer engagement platforms and serves as a real-time user profile database.
Amazon DynamoDB
If your organization uses Amazon Web Services (AWS) as its cloud provider, it makes sense to consider Amazon DynamoDB, Amazon’s NoSQL cloud-managed services product.
Why it stands out:
Multi-model: It supports both document and key-value data models, making it more versatile than competitors that support only a single data model.
Massive scale: According to the company, it handles more than 10 trillion requests per day.
Top features:
Multi-region, fully replicated databases provide high uptime
On-demand scaling adapts to your application’s needs in real time
Enterprise-grade security with fine-grained access control
Streams for real-time data processing capabilities
Built-in support for atomicity, consistency, isolation, and durability (ACID) transactions ensures reliable operations
Benefits:
It provides performance stability, even during unpredicted spikes.
It scales starting from smaller companies and supports them as they grow
It saves time with automated back-ups and a management-free operational model.
Multi-region replication means data is always available and safe.
It integrates well with other AWS services.
Cons:
May have a steeper learning curve for AWS newcomers
Pay-per-use pricing makes cost predictability challenging
Best for:
Companies of all sizes that require a database with good performance and scalability, particularly those already integrated within the AWS ecosystem.
DynamoDB is more than just a database. It’s the NoSQL service coupled with AWS and part of the Amazon universe. So, if you’re already using AWS and want to simplify your life, DynamoDB could tilt the scales in its favor.
Couchbase Capella
Couchbase Capella offers more flexibility to organizations that run on more than one cloud provider or that want to keep their cloud provider options open for the future.
Why it stands out:
Deploy anywhere: Couchbase Capella runs on both public clouds and hybrid environments.
Powerful indexing: Compared with competitors that provide limited search, Couchbase Capella provides an internal full-text search engine.
Couchbase Lite: The company also provides an embeddable database for mobile and Internet of Things (IoT) devices with built-in bidirectional data synchronization.
Top features:
Supports SQL-based querying for NoSQL databases with SQL ++ (formerly N1QL)
Internal full-text search engine for data discovery
Includes fully managed integrated cache layer
Supports on-premises operation
Provides memory-first replication and sync
Benefits:
Couchbase Capella supports familiar SQL-like queries, making it easier for developers who need to transition from relational databases.
A cloud platform-agnostic setup gives your organization more flexibility and lower costs, avoiding vendor lock-in. You can also run it on-premises if you don’t want to use the cloud.
Read/write performance is improved due to the integrated cache layer.
Performance is improved due to memory-first replication.
Cons:
Memory-first replication can require costly hardware infrastructure
Performance falls off steeply when data volumes grow beyond available memory
Adding or removing cluster nodes requires manual data rebalancing
Best for:
Organizations that like Couchbase’s SQL++ query language and that wish to take advantage of its mobile synch capabilities
DataStax Astra
As with Couchbase Capella, Datastax Astra offers the advantage of not being tied to a single cloud provider. This makes it more suitable for organizations with hybrid cloud services or who want to change cloud providers more easily.
Why it stands out:
Open-source support: The organization is strongly tied in with the Cassandra community, both by purchasing a Cassandra consulting company and paying it forward by making the majority of the commits to the Cassandra project.
Top features:
Supports multiple data models, including columnar, graph, key-value, and time series
Cloud-agnostic design supports all the standard public cloud services
Benefits:
Support for multiple data models makes Datastax Astra versatile and suitable for a wide variety of use cases.
Cloud-agnostic support works well for hybrid organizations that want to keep their cloud options open.
Cons:
Does not offer strong consistency
Does not support synchronous active-active data replication
Best for:
Distributed data environments only requiring eventual consistency
Google Cloud Bigtable
It's no surprise that a database crafted by Google to underpin its products, such as Google Cloud Bigtable, excels in scalability.
Why it stands out:
Massive scalability: It supports petabytes of data across commodity servers and handles more than six billion requests per second.
Wide-column data format: This also means BigTable tracks changes to your data through time.
Top features:
Real-time data syncing across app instances due to built-in live synchronization
Offline support that syncs data when connectivity is restored
Easy administration for tasks such as replication and cluster resizing
Automatic load balancing helps regularize response
Multi-region replication for availability and disaster recovery
Benefits:
It includes software development kits for the web, iOS, Android, Flutter, C++, and Unity to facilitate development.
It offers reliable operations with 99.99% availability.
With built-in load balancing, easy replication, and cluster resizing, administration isn’t a lot of work.
It protects data with built-in security and data validation rules.
Because it’s Google, it integrates well with other Google Cloud services, creating a cohesive cloud ecosystem.
Cons:
Ease of scalability comes with a price, which can add up for high-volume transactions
Doesn’t offer a query language, which some NoSQL databases do
Best for:
Developers building applications that need high throughput and scalability for key/value data, such as customer 360, financial services, IoT, and graph applications
If you’re concerned about scalability, you’ll have to admit that a NoSQL database that supports Google’s operations can probably handle yours.
Microsoft Azure Cosmos DB
If your organization is already using Microsoft Azure, it makes sense to look at Microsoft’s NoSQL offering, Azure Cosmos DB.
Why it stands out:
Global distribution: Azure Cosmos DB spans multiple regions, making your data widely available.
Integration with Microsoft Azure: Obviously, something put out by Microsoft is likely to have the best support for Microsoft products.
Top features:
Global distribution across any number of Azure regions
Offers single-digit millisecond read and write latencies at the 99th percentile
Multi-model support with APIs for SQL, MongoDB, Cassandra, Gremlin, and Table
Five consistency models to balance latency, throughput, and consistency
Service level agreements covering throughput, latency, availability, and consistency
Benefits:
It provides global distribution to provide a multi-homed setup for your data.
It offers 99.999% availability for reliable access.
Multiple data and consistency models provide more flexibility.
It includes managed scalability based on your usage.
Access to query languages makes development easier for organizations in transition from SQL databases and for use cases that require it.
Cons:
Not as well suited for non-Microsoft Azure shops
Costs escalate if not managed appropriately, especially with multi-region distribution
Best for:
Organizations that need a wide range of development options for applications such as chatbots, fintech, IoT, and e-commerce
In the same way that Google Bigstore is good for organizations using Google and Amazon DynamoDB is good for organizations using AWS, Microsoft Azure Cosmos DB is good for organizations using Microsoft Azure. That said, its wide variety of data and consistency models, as well as its multiple query languages, gives developers more familiarity and flexibility.
MongoDB Atlas
MongoDB Atlas is a widely used NoSQL document database intended for web applications up to a certain scale.
Why it stands out:
Multi-cloud versatility: MongoDB Atlas works with any cloud provider, making it useful for organizations with hybrid systems or organizations that want to keep their options open.
Widely used: Because it’s a market leader, it’s easier to find resources for it.
Top features:
Document data model
Includes some SQL-like features, such as consistency
Robust open-source community
Includes a query language
Integrated security features such as creating and configuring user accounts
Benefits:
Document data model architecture is intended for flexibility and ease of development, especially for small- to medium-sized companies.
Broad acceptance and open-source support mean it’s easier to find tools and help.
Managed security protects data and reduces administrative overhead.
Replication and fault tolerance make data more available.
Support for features such as query language and consistency makes the transition to NoSQL easier.
Cons:
Configuring all the advanced features of the software for MongoDB newcomers can be quite complex
Doesn’t scale as well as some alternatives, especially for write-intensive applications
Best for:
Teams looking for a basic NoSQL database with data structure flexibility, especially those operating in a multi-cloud environment or needing a rich feature set to support diverse application requirements
There are advantages to using something popular. It’s safe. (Remember, “Nobody ever got fired buying IBM?”) First of all, one has to assume there’s a reason it’s popular. It can also be easier to find resources, whether it’s developers or support. But it’s also important to look at the features of popular products to make sure they’re the best suited for your organization’s needs.
Oracle NoSQL Database Cloud Service
Oracle is pretty much synonymous with SQL, but it offers a fully managed NoSQL service as well.
Why it stands out:
Name recognition: Oracle NoSQL Database Cloud Service brings the reliability of years in the industry, so if you have trouble convincing higher-ups of the value of NoSQL, a name brand might help.
Comprehensive capabilities: From on-premise to cloud integration, it's designed to support a wide array of enterprise scenarios.
Top features:
Built-in administrative and scalability features
Multi-model database support for key-value, document, and graph data types
Multiple consistency policies
“Availability zones” to improve reliability
Query language access—including SQL
Benefits:
“Availability zones” make it resilient.
Because it is multi-model, it supports multiple use cases.
It requires reduced overhead for managing database infrastructures
Obviously, it’s going to work well in Oracle environments.
Its multiple consistency policies give developers options.
Cons:
Isn’t widely used, meaning it may be harder to find resources
Because Oracle focuses on its SQL product, likely to be seen as a secondary product, meaning less staffing, support, and development
Best for:
Enterprises that already rely on a range of Oracle products.
Organizations that already use Oracle relational databases and other products might find Oracle NoSQL Database Cloud Service an easier sell if they are looking to dip their toe into the NoSQL space. Its many data models, consistency policies, and even an SQL-like query language act as useful training wheels.
Redis Enterprise Cloud
Initially created as an in-memory cache store to boost performance, Redis has evolved over the years. Today, some organizations use Redis as a high-speed, low-latency NoSQL database.
Why it stands out:
Speed: Its in-memory architecture makes it fast, particularly for data that’s frequently accessed.
Supports microservices: Its design means it can act as a centralized store for microservices
Top features:
In-memory data store for sub-millisecond data serving
Active-active geo-distribution for uptime and consistency
In addition to an in-memory data store, also has the option to store data on disk
Support for a wide array of data structures, including streams, hashes, and lists
Integrated modules for search, JSON, AI, and time-series data
Benefits:
Its speed means work gets done more quickly, especially with frequently accessed data.
Runs on-premises as well as partnering with major cloud providers, making it suitable for hybrid organizations.
Data structure support makes it viable for a number of use cases and provides developer flexibility.
The company and its products have a large community of supporters to provide help and other resources.
The disk storage option makes it more reliable.
Cons:
Designed as a single instance data store, not a distributed NoSQL, so performance and scalability are hampered
In-memory storage means costs may scale with usage, requiring careful management to optimize efficiency
Best for:
Small-to-medium-sized amounts of frequently accessed data where speed is required.
Organizations that rely on real-time analytics or need to manage live sessions with users find Redis Enterprise Cloud performs well.
ScyllaDB Cloud
ScyllaDB is a next-generation NoSQL database designed for data-intensive applications that require high throughput, low latency, and predictable scalability. ScyllaDB supports wide-column and key value modeling for hundreds of gigabytes to petabytes of data and with high availability.
Why it stands out:
High performance: ScyllaDB is optimized to handle high throughput and low-latency operations.
Non-hierarchical structure: All nodes are equivalent, so replication and repartitioning are automatic.
Top features:
Compatible with Apache Cassandra, including supporting Cassandra Query Language, offering a transition for existing applications
Advanced caching mechanisms improve read and write speeds
Auto-tuning capabilities for resources, ensuring optimal performance without manual intervention
Support for key-value, wide column, and time series data formats
Built-in fault tolerance that helps to promote continuous operation
Benefits:
It provides good and reliable performance for predictable response in fast-paced industries.
Non-hierarchical architecture makes scaling simple.
Built-in redundancy and replication strategies keep applications robust.
It integrates well with existing Cassandra workloads for ease of migration.
Multiple data formats support more use cases and provide more developer flexibility.
Cons:
Sacrifices some data structure flexibility for high and reliable speed
High-performance features may come with a cost premium over other NoSQL solutions
Best for:
Companies with large data streams needing real-time processing, such as in financial services, AdTech, or IoT, which require low latency.
Organizations that anticipate rapid growth will find ScyllaDB Cloud's auto-tuning, replication, and repartitioning optimizations to be critical assets that don’t require as much operator time.
FAQs
As with most things in life, there are both advantages and disadvantages of cloud databases.
Advantages:
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Regular operational expense rather than a large initial capital outlay for hardware
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Reduced staffing needs
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Generally, confidence that backups, updates, and security protocols are being applied, though of course this always depends on the cloud provider
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Ease in scaling up and down to accommodate load without having to invest in equipment
Disadvantages:
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Lack of control, which can particularly be an issue in highly secure or regulated industries
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Cloud providers can be an enticing target for security breaches
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Vulnerable to problems with the cloud provider, such as going out of business
Well, first of all, whether it’s the “era of NoSQL” is a matter of debate, because traditional relational databases still maintain a large market share. That’s because the jobs they’re good at—running transactions against structured data—are still needed by businesses. It’s the same reason gas-powered cars continue to be sold and operated in the “era” of electric cars: because electric cars don’t fulfill everyone’s needs.
