The 25 Best Key Value Databases Of 2024
10 Best Key Value Database Shortlist
Here's my pick of the 10 best software from the 25 tools reviewed.
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In data storage, key-value databases have emerged as a go-to solution, especially in industries like e-commerce. The unique charm of these non-relational databases is their straightforward way to store data - in key-value pairs. A unique identifier key leads you to its corresponding value, much like how a product SKU in a shopping cart leads you to the product details.
This simplicity of storing and retrieving data has proven to be especially useful in managing shopping carts, user sessions, and data caching on the backend. Exploring key-value databases could be a game-changer for you if you're looking to streamline data management and accelerate response times.
What Is A Key Value Database?
A key value database, also known as a key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays - a structure more commonly known as a dictionary or hash. One of the significant strengths of key-value databases is partitioning, allowing the data to be distributed across multiple nodes, predefined schema structure, and enhancing the database's scalability and performance. In a nutshell, key lookup process, a key-value database offers a convenient way to manage simple data, aiding in faster response times and accessible user experiences, crucial for applications where speed is paramount.
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Developers and businesses widely adopt this type of database due to its simplicity, high performance, and horizontal scalability. It's commonly used for handling large amounts of data where the primary operation is retrieving a value associated with a particular key. Use cases include caching, session management, and serving ads, among other real-time big data applications.
Best Key Value Database Summary
| Tools | Price | |
|---|---|---|
| Couchbase | Pricing upon request | Website |
| DataStax | From $30/user/month | Website |
| ArangoDB | No details | Website |
| ScyllaDB | From $10/user/month | Website |
| OrientDB | Enterprise edition from $5,970 | Website |
| Oracle NoSQL Database Cloud | From $25/user/month | Website |
| Amazon DynamoDB | From $1.25/million requests; read operations from $0.25/million requests. | Website |
| Pure Storage | No details | Website |
| Aerospike | From $10/user/month (billed annually) | Website |
| Redis Enterprise | From $5/user/month | Website |
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Compare SoftwareBest Key Value Database Reviews
Couchbase is a distributed NoSQL document-oriented database that provides low-latency data management for web, mobile, and IoT applications. Its ability to sync data across devices and support for edge computing makes it particularly suited to mobile and edge computing use cases.
Why I Picked Couchbase: The decision to include Couchbase was driven by its strong edge computing capabilities and robust mobile data synchronization. Compared to others, Couchbase's unique edge computing features set it apart. Regarding the 'best for' criteria, I believe Couchbase is best suited for mobile and edge computing use cases due to its synchronization capabilities and offline-first approach.
Standout Features & Integrations:
Couchbase provides features like memory-first architecture for high-speed operations, multi-dimensional scaling, and full-stack security from cloud to edge. Its data synchronization feature, Couchbase Sync Gateway, ensures accessible data syncing across various devices. Important integrations include support for various languages (Java, .NET, Node.js, Go, PHP, Python, C, C++) and platforms like Docker, Kubernetes, and Elasticsearch.
Pros and cons
Pros:
- Support for various programming languages
- Comprehensive data synchronization features
- Excellent mobile and edge computing capabilities
Cons:
- It might be too feature-rich for simple applications, making it overkill in some situations
- The learning curve may be steep for those new to NoSQL databases
- Pricing can be complex and less transparent
DataStax is a highly reliable, scalable, distributed database designed for cloud-native applications. It's built on Apache Cassandra, offering robust support for easily handling complex, large-scale data operations.
Why I Picked DataStax: The decision to include DataStax in this list was driven by its excellent capabilities in handling distributed data operations at scale. Its foundation on Apache Cassandra, known for massive scalability and high availability, sets it apart. I believe these characteristics make DataStax best for those who need to manage large-scale, distributed data operations effectively.
Standout Features & Integrations:
Key features of DataStax include linear scalability, continuous availability, and advanced security. It also supports flexible data storage and multi-cloud replication. As for integrations, DataStax can be integrated with Spark for analytics, Solr for search, and Graph for complex, relationship-driven data. It also offers Kubernetes operators for cloud-native deployments.
Pros and cons
Pros:
- Versatile integrations with Spark, Solr, and Graph
- Support for multi-cloud replication
- Excellent linear scalability
Cons:
- The cost could be prohibitive for smaller teams
- Steep learning curve, particularly without a Cassandra background
- Could be overkill for small-scale operations
ArangoDB is a native multi-model database with flexible data models for document database, graphs, and key-values. This tool is designed for high-performance applications, providing all the benefits of a graph database with the power of a general-purpose database.
Why I Picked ArangoDB: When it comes to multi-model data flexibility, ArangoDB is a standout. I chose it for its native multi-model approach, allowing users to switch between different data models according to their needs nimbly. This distinct versatility, along with its high-performance credentials, makes ArangoDB best for projects requiring flexibility in handling different types of data.
Standout Features & Integrations:
ArangoDB has features like native multi-modeling, graph exploration, full-text search, and horizontal scaling. It also offers a query language (AQL) that allows you to use the same semantics regardless of the data access pattern. For integrations, ArangoDB can easily connect with widely used technologies like Docker, Kubernetes, and Prometheus, making it versatile for diverse tech stacks.
Pros and cons
Pros:
- Wide-ranging integrations with common technologies
- High-performance, even with large datasets
- Robust multi-model capabilities
Cons:
- May require careful configuration to fully utilize performance capabilities
- Query language might require adjustment for those used to SQL
- Steeper learning curve compared to single-model databases
ScyllaDB is an open-source distributed NoSQL data store, implemented from scratch to deliver outstanding performance and low latency. It's designed for applications that have rigorous performance and latency demands, making it particularly suited to high-speed, high-volume data environments.
Why I Picked ScyllaDB: In choosing ScyllaDB for this list, I found its commitment to providing high-speed, low-latency operations exceptional. What sets ScyllaDB apart is its built-from-scratch design improves for efficiency and performance. By choosing this tool, I am confident in recommending it for applications that demand high performance and low latency.
Standout Features & Integrations:
ScyllaDB offers automatic sharding and rebalancing, tunable consistency, and high availability, making it a robust solution for modern applications. It also supports materialized views, secondary indexes, and lightweight transactions. Regarding integrations, ScyllaDB works well with Kubernetes, Apache Kafka, and Apache Spark, among others.
Pros and cons
Pros:
- Wide array of integrations
- Comprehensive feature set
- Exceptional performance and low latency
Cons:
- The cost can increase significantly for large deployments
- May be overpowered for simple use cases
- Can be complex to configure for optimal performance
OrientDB is a multi-model database system designed to handle a variety of data models, including graph, document, key-value, and object models. It's particularly effective for projects requiring a graph-oriented database due to its ability to manage complex, interconnected data efficiently.
Why I Picked OrientDB: I selected OrientDB because it offers versatility with its multi-model approach while excelling in graph-oriented DBMS. Its unique hybrid nature stands out from other databases and provides flexibility in handling diverse data types. It's especially fitting for graph-oriented database needs, thanks to its ability to easily represent complex relationships and deliver high performance.
Standout Features & Integrations:
Some of OrientDB's key features include ACID (Atomicity, Consistency, Isolation, Durability) transactions, SQL support, and a robust security framework. It provides efficient storage and retrieval of complex data structures, which is crucial for graph databases. OrientDB can integrate well with various languages like Java, Python, and Node.js through its native APIs.
Pros and cons
Pros:
- Open-source and free for community use
- Flexible with various data models
- Efficient management of graph-oriented data
Cons:
- Enterprise edition might be costly for small businesses
- Community support might not be as extensive as more popular alternatives
- Learning curve due to its unique multi-model nature
Best for integrating with Oracle's robust cloud ecosystem
Oracle NoSQL Database Cloud is a fully managed NoSQL database service for today's most demanding applications. It excels in performance, availability, and scalability while fitting easily into the Oracle ecosystem.
Why I Picked Oracle NoSQL Database Cloud: In making my selection, the integration potential of Oracle NoSQL Database Cloud within Oracle's vast ecosystem of services captured my attention. Its robustness and scalability set it apart, making it particularly well-suited to businesses that rely heavily on Oracle services. It’s this ecosystem integration that, in my opinion, makes it the best choice for businesses heavily immersed in Oracle's ecosystem.
Standout Features & Integrations:
Oracle NoSQL Database Cloud offers multi-region replication, automatic sharding, and ACID transactions. Additionally, it includes security features like encryption, IAM integration, and private endpoints. Regarding integrations, Oracle NoSQL Database Cloud can work quickly with all services in the Oracle ecosystem, including Oracle Cloud Infrastructure, Oracle Analytics Cloud, and Oracle Functions.
Pros and cons
Pros:
- Robust security features built-in
- Offers multi-region replication for data redundancy
- Strong integration with Oracle's ecosystem
Cons:
- Some users report a complex user interface
- Pricing could be steep for smaller organizations
- Might not be the best fit for non-Oracle users
Amazon DynamoDB is a fully managed, serverless NoSQL database service provided by Amazon Web Services (AWS). It delivers rapid, single-digit millisecond performance at any scale, ideal for businesses deeply integrated with AWS that require a scalable NoSQL solution.
Why I Picked Amazon DynamoDB: I selected Amazon DynamoDB for its scalability, robust feature set, and easy integration with other AWS services. As a part of the AWS ecosystem, DynamoDB delivers compelling value for businesses already invested in AWS. It’s best for those needing a NoSQL solution due to its ability to scale effortlessly to accommodate large volumes of data.
Standout Features & Integrations:
Amazon DynamoDB offers features like automatic scaling, built-in security, test Android, iOS, and web apps on real devices, backup and restore, and in-memory caching. Furthermore, it also provides a serverless architecture that removes the operational burden. DynamoDB integrates well with other AWS services like AWS Lambda, Amazon Redshift, and Amazon SageMaker.
Pros and cons
Pros:
- Robust security and reliability features
- Highly scalable and performant
- Easy integration with AWS ecosystem
Cons:
- Could be complex for beginners to configure properly
- Requires AWS knowledge for optimal utilization
- Might be less cost-effective for smaller workloads
Best for organizations needing high-availability and data reduction capabilities
Pure Storage is a data storage platform that offers high-availability, data reduction, and high-speed connectivity for enterprises. It caters to organizations that need constant access to their data while optimizing storage space, making it especially useful for those needing high-availability and data reduction capabilities.
Why I Picked Pure Storage: Pure Storage caught my attention due to its commitment to providing uninterrupted data access and efficient data management. Its data reduction features make it stand out, allowing for improved storage usage. I determined that Pure Storage is the best for organizations requiring high-availability and data reduction capabilities due to its robust design and enterprise-grade features, addressing both needs effectively.
Standout Features & Integrations:
Pure Storage has features like Evergreen architecture for constant data availability, Pure1 cloud-based management and support, and a Purity operating environment for efficient data reduction. Integration-wise, Pure Storage supports various platforms, including VMware, Microsoft SQL Server, and Oracle, ensuring smooth data transfer and interoperability.
Pros and cons
Pros:
- Supports a wide array of platform integrations
- Efficient data reduction for improved storage usage
- Exceptional high-availability capabilities
Cons:
- The need for specialized training to fully utilize features
- Might be overkill for smaller organizations
- Pricing is not transparent
Aerospike is a powerful, real-time, distributed NoSQL database offering a robust high-speed transaction platform. Its unique hybrid memory architecture delivers predictable high performance for mission-critical applications at scale.
Why I Picked Aerospike: I chose Aerospike for its exceptional ability to rapidly handle real-time transaction processing. Its impressive architecture and intelligent optimization makes it stand out in a crowd of key-value databases. It's adept at dealing with large volumes of data and maintaining high throughput - factors that make it the best fit for high-speed, real-time transaction processing.
Standout Features & Integrations:
Aerospike offers features like ACID transactions, linear scalability, and a patented hybrid memory system that enhances processing speeds. These combined capabilities make it ideal for high-speed, real-time applications. In terms of integrations, it plays well with various programming languages like Python, Java, C#, Go, and more. Aerospike also integrates with Spark, Kafka, and Jupyter, which enhances its data analytics capabilities.
Pros and cons
Pros:
- Broad integration support with programming languages and data analytics tools
- Highly scalable for growing businesses
- Supports ACID transactions for reliable data integrity
Cons:
- Some users have reported difficulties with complex queries
- Pricing can become steep as the user base grows
- The learning curve could be steep for beginners
Redis Enterprise is an in-memory, NoSQL database platform designed to deliver high performance, easy scalability, and robust reliability. As it's improved explicitly for in-memory operations, it's ideal for applications that demand swift data storage and caching.
Why I Picked Redis Enterprise: When selecting this tool, I was particularly impressed by its ultra-fast performance and wide adoption in the industry. What differentiates Redis Enterprise is its high-speed, in-memory design, making it a go-to choice for real-time applications requiring swift data access. Given its unique design and features, I believe it best for in-memory data storage and caching needs.
Standout Features & Integrations:
Redis Enterprise has features like auto-sharding for linear scaling, in-memory replication for high availability, and data persistence for durability. It also provides modules for additional functionalities like search, graph processing, and JSON document storage. Notable integrations include programming languages like Java, Python, and .NET and platforms like Docker, Kubernetes, and Pivotal Cloud Foundry.
Pros and cons
Pros:
- Extensive feature set and integrations
- High availability and durability
- Ultra-fast in-memory data access
Cons:
- Complexity increases with the usage of advanced modules
- Potentially high cost for larger deployments
- Can be overkill for simple caching needs
Other Noteworthy Key Value Database
Below is a list of additional key value database that I shortlisted but did not make it to the top 10. Definitely worth checking them out.
- Lucid
For visual database design and diagramming
- Azure Table Storage
For businesses deeply integrated with Microsoft Azure
- BangDB
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- CASSANDRA
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- Pivotal Gemfire
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- BoltDB
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- Azure Redis Cache
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- GigaSpaces
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- Berkeley DB 12c
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- Memcached
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- Accumulo
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- InterSystem Cache
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- GridDB
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- Oracle Coherence
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- RocksDB
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Selection Criteria For Choosing Key Value Database
Choosing the right database software can significantly impact your organization's efficiency and success. In my experience, I've tested and researched countless tools, but I always have specific criteria in mind when it comes to database systems.
Core Functionality
A good database system should enable you to:
- Store and retrieve data securely and efficiently.
- Scale as your data grows.
- Support simultaneous access from multiple users.
- Ensure data integrity and consistency.
Key Features
Key features to look out for in database software include:
- Data Redundancy: This refers to the duplication of data to ensure it's not lost in case of a failure
- Security Measures: Features like encryption, user access controls, and audit logs to keep your data secure
- Query Processing: The ability to efficiently process complex queries
- Backup and Recovery: Tools to back up your data and recover it in case of data loss
Usability
When it comes to usability, the following aspects are vital:
- Intuitive Design: A well-designed interface can greatly simplify database management. Look for clear menus, easy navigation, and helpful tooltips.
- Easy Onboarding: Database software should include comprehensive tutorials, documentation, and support to help new users get started.
- Customer Support: Prompt and knowledgeable customer support can be crucial when dealing with complex systems like databases.
- Interface Flexibility: The software should offer graphical and command-line interfaces to cater to user preferences and use cases.
- Role-Based Access: The ability to easily configure access based on roles is a must-have feature for larger teams or sensitive data.
Most Common Questions Regarding Key Value Database (FAQs)
What are the benefits of using a key-value database?
Key-value databases offer several advantages:
- Efficiency: Key-value stores are designed for rapid data access, making them ideal for applications that require high performance.
- Scalability: These databases are highly scalable, allowing you to store and manage large volumes of data.
- Flexibility: Unlike relational databases, key-value stores don’t require a fixed data model, offering flexibility in storing and managing data.
- Simplicity: The simple key-value data model makes it easy to learn and use.
- Distributed Computing: Many key-value databases support distributed computing out of the box, providing resilience and high availability.
How much do key-value database tools typically cost?
Pricing for key-value database tools can vary significantly based on the product and the user’s specific needs. Some are open-source and free to use, while others come with a cost.
What are the pricing models for key-value databases?
The pricing models for these tools can be diverse, ranging from open-source, and freemium, to subscription-based models. Some vendors offer tiered pricing structures based on storage, the number of requests, or the features included. Some also charge based on the number of servers or the amount of data transferred.
What is the typical range of pricing for key-value database tools?
Typically, the pricing for these tools can start as low as free for open-source tools and can go up to several thousand dollars per month for enterprise-level solutions.
Which are the cheapest and most expensive key-value database tools?
Among the cheapest are open-source solutions like Redis and RocksDB which are free to use. On the more expensive end are enterprise-grade solutions like Oracle Coherence, which can cost several thousand dollars monthly.
Are there free key-value database tools?
Yes, there are several free key-value databases available. Examples include open-source solutions such as Redis, RocksDB, and Cassandra. These tools are free to use but may require technical expertise to set up and maintain.
Summary
In conclusion, key-value databases provide fast, efficient data access with simple, flexible data models, making them ideal for applications that need high performance. However, the best tool will always depend on your specific use case and requirements.
Key takeaways:
- Understand Your Needs: It's crucial to identify your requirements before choosing a key-value database. The right tool will depend on factors like the amount of data you need to handle, the speed of access required, and the complexity of your data structures.
- Consider the Learning Curve and Support: Some tools may require a steeper learning curve or more technical knowledge. Also, consider the level of support provided by the tool's community or the vendor.
- Don't Overlook Pricing: Pricing can vary significantly among key-value database tools. Consider your budget, and be aware of the different pricing models, from open-source to subscription-based. Remember, free tools sometimes come with hidden costs, such as setup or maintenance time and resources.
What Do You Think?
I hope this guide gives you a good starting point to select the best key value database for your use case. However, technology is an ever-evolving field, and new tools are always being developed. So, I invite all readers to share their experiences or recommend any tools that they feel could have made the list. Your input can enrich this guide and help other readers make a more informed decision.