Top 54 REST Databases
Compare & Find the Best REST Database For Your Project.
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| Database | Strengths | Weaknesses | Type | Visits | GH | |
|---|---|---|---|---|---|---|
| High availability, Consistent, Reliable | Limited to key-value storage, Not suited for large datasets | Key-Value, Distributed | 16.2k | 47.9k | ||
| Real-time search capabilities, Easy integration with various platforms | Limited advanced query functionalities, Focus on text search primarily | Search Engine | 16.8k | 47.5k | ||
| Fast and Relevant Search, Easy to Use API | Limited Scalability, Development Community | Search Engine | 28.1k | 21.2k | ||
| Offline capabilities, Synchronizes with CouchDB, JavaScript based | Limited scalability, Single-node architecture | Document, Embedded | 16.0k | 16.9k | ||
| Built-in machine learning, Vector-based similarity searches | Limited support for complex queries, Relatively new technology | Vector DBMS | 70.2k | 11.5k | ||
| High-performance, Multi-threaded, Compatible with Redis | Relatively new with a smaller community, Potential compatibility issues with Redis extensions | In-Memory, Key-Value | 9.5k | 11.5k | ||
| Open source, Scalable, Real-time search and analytics | Relatively new, Less enterprise support compared to Elasticsearch | Search Engine, Distributed | 99.1k | 9.8k | ||
| High-performance full-text search, Real-time synchronization with SQL databases, Open-source and community-driven | Limited non-search capabilities, Smaller community compared to other search engines | Search Engine | 5.0k | 9.1k | ||
| Easy replication, Schema-free JSON documents, High availability | Not designed for complex queries, Slower than some NoSQL databases | Document, Distributed | 5.8m | 6.3k | ||
| Highly scalable, Managed cloud service, Fully integrated with IBM Cloud | Limited offline support, Smaller ecosystem compared to other NoSQL databases | Document, Distributed | 13.4m | 6.3k | ||
| Scalable search and recommendation engine, Real-time data processing, Open source | Niche market, Requires specialized knowledge | Distributed, Search Engine | 5.1k | 5.8k | ||
| Strong event sourcing features, Efficient stream processing | Requires expertise in event-driven architectures, Limited traditional RDBMS support | Event Stores, Streaming | 9.8k | 5.3k | ||
| Scalability, Strong consistency, Integrates with Hadoop | Complex configuration, Requires Hadoop | Wide Column, Distributed | 5.8m | 5.2k | ||
| Highly scalable, Optimized for time-series data, Open source | Limited built-in analytics capabilities, Requires third-party tools for visualization | Time Series, Distributed | 0.0 | 1.7k | ||
| Time series focused, High throughput | New entrant in market, Limited community support | Time Series, Distributed | 1.8k | 1.7k | ||
| Vector similarity search, Scalability | Young project, Limited documentation | Distributed, Vector DBMS | 0 | 1.5k | ||
| Full-text search capabilities, Highly scalable and distributed, Flexible and extensible | Complex configuration, Challenging to optimize for large datasets | Search Engine | 5.8m | 1.2k | ||
| Scalability, Distributed caching, Focused on .NET applications | Primarily focused on Windows and .NET environments | In-Memory, Distributed | 7.9k | 650 | ||
| High scalability for time series, Rich analytics features | Complex data model, Steep learning curve | Time Series, Distributed | 47 | 388 | ||
| Lightweight, Fast key-value storage | Limited query capabilities, Not natively distributed | In-Memory, Key-Value | 1.7k | 276 | ||
| Supports JCR API, Repository capabilities | Complex setup, Steep learning curve | Hierarchical, Document, Content Stores | 164.1k | 217 | ||
| Simplified time series data storage, Efficient data recall, Compact data formats | Limited to time-series data, Recently developed | Time Series, Event Stores | 146 | 177 | ||
| Confidential computing, End-to-end encryption, High security | Higher overhead due to encryption, Potentially complex setup for non-security experts | Distributed, Relational | 2.0k | 170 | ||
| High performance, Extensible architecture, Supports SQL standards | Limited community support, Not widely adopted | Analytical, Relational, Distributed | 5.8m | 135 | ||
| Versioned data storage, Metadata management, Data integrity | Not optimized for high-speed transactions, Limited scalability compared to distributed databases | Distributed, Document | 0 | 6 | ||
| Seamless integration with Firebase, Realtime updates, Scalability | Cost can escalate, Limited querying capabilities | Document, Distributed | 6.4b | 0 | ||
2012 | Fast search capabilities, Highly scalable, Easy integration | Limited to search use-cases, Pricing can be expensive for large-scale usage | Search Engine | 429.1k | 0 | |
| Integrated AI capabilities, Part of Azure ecosystem | Dependency on Azure environment, Cost considerations for large data sets | Search Engine | 723.2m | 0 | ||
| Efficient time series data storage, Easy integration with various tools | Lacks advanced analytics features, Limited support for large data volumes | Time Series | 927 | 0 | ||
2001 | Enterprise-grade features, Strong data integration capabilities, Advanced security and data governance | High cost, Learning curve for developers | Document, Native XML DBMS | 9.3k | 0 | |
| Scalable NoSQL database, Fully managed, Integration with other Google Cloud services | Vendor lock-in, Complexity in querying complex relationships | Document, Distributed | 6.4b | 0 | ||
| Scalable NoSQL database, Real-time analytics, Managed service by Google Cloud | Limited to Google Cloud Platform, Complexity in schema design | Distributed, Wide Column | 6.4b | 0 | ||
2005 | Advanced search capabilities, AI-powered relevance | Proprietary platform, Complex pricing model | Search Engine | 64.7k | 0 | |
2014 | Seamless integration with Apple ecosystems, Strong focus on privacy and security, Automatic synchronization | Limited to Apple platforms, Less flexible for non-Apple environments | Document, Key-Value | 420.8m | 0 | |
2013 | Scalability, High performance, In-memory processing | Complex learning curve, Requires extensive memory resources | Distributed, In-Memory | 3.1k | 0 | |
| Lightweight, Object-Oriented database | Limited support for distributed systems, Slower performance with complex queries | Embedded, Object-Oriented | 0 | 0 | ||
2009 | Supports data integration from various sources, User-friendly interface, Strong data preparation and analytics features | Primarily tailored for Hadoop ecosystems, Limited query flexibility compared to SQL | Analytical | 19.7k | 0 | |
2018 | Real-time analytics, Built-in connectors, SQL-powered | Can be costly, Limited to analytical workloads | Analytical, Distributed, Document | 7.6k | 0 | |
2000 | In-memory speed, Scalability, Real-time processing | Cost, Requires proper tuning for optimization | In-Memory, Distributed | 7.2k | 0 | |
2011 | Array-based data storage, Suitable for scientific data, Strong data integrity features | Niche market focus, Limited adoption | Analytical, Distributed | 514 | 0 | |
2003 | Full-text search, Easy setup | Feature limitations, Scaling challenges | Search Engine, Document | 10.1k | 0 | |
| Scalable, High availability, Flexible data model | Limited language support, Complex setup for beginners | Key-Value, Wide Column, Time Series | 1.3m | 0 | ||
2014 | Time Series optimized, Powerful analytics tools | Niche use cases, Steep learning curve | Time Series, Geospatial | 88 | 0 | |
1998 | Cross-platform, Integration with Valentina Studio | Niche market, Limited public documentation | Relational, Document | 9.4k | 0 | |
2015 | Scalable, Designed for time series data, High availability | Complex setup, Limited query language support | Time Series, Key-Value | 2.2k | 0 | |
1978 | Integrated development environment, Object-oriented database | Older technology, Limited to Jade platform | Object-Oriented, Document | 806 | 0 | |
2009 | High-speed data ingestion, Time series analysis | Complex setup, Cost | Distributed, In-Memory, Time Series | 0 | 0 | |
2000 | Robust search capabilities, Fault-tolerant | High initial cost, Complex setup | Search Engine, Content Stores | 33 | 0 | |
| Open-source IoT platform, Flexible and scalable | Complex setup for new users, Requires integration expertise | Distributed | 20 | 0 | ||
2012 | Unified platform, JavaScript support | Limited community support, Niche use cases | Document, In-Memory | 0.0 | 0 | |
2013 | Embedded design, Ease of integration | Limited scalability, Small community support | Document, Embedded | 163 | 0 | |
| Flexible data model, JSON support | Limited commercial support, Basic querying capabilities | Document, Embedded | 0 | 0 | ||
2019 | Geospatial Data Handling, Real-Time Processing | Complex Setup | Time Series, Geospatial | 899 | 0 | |
2012 | Scalable, Optimized for time series metrics | Limited documentation, Niche use case specific | Time Series, Distributed | 0 | 0 |
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