Best Distributed Databases
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Compare the Top Distributed Databases as of June 2025
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What are Distributed Databases?
Distributed databases store data across multiple physical locations, often across different servers or even geographical regions, allowing for high availability and scalability. Unlike traditional databases, distributed databases divide data and workloads among nodes in a network, providing faster access and load balancing. They are designed to be resilient, with redundancy and data replication ensuring that data remains accessible even if some nodes fail. Distributed databases are essential for applications that require quick access to large volumes of data across multiple locations, such as global eCommerce, finance, and social media. By decentralizing data storage, they support high-performance, fault-tolerant operations that scale with an organization’s needs. Compare and read user reviews of the best Distributed Databases currently available using the table below. This list is updated regularly.
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CrateDB
CrateDB
The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data. -
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rqlite
rqlite
The lightweight, user-friendly, distributed relational database built on SQLite. Fault tolerance and high availability with zero hassle. rqlite is a distributed relational database that combines the simplicity of SQLite with the robustness of a fault-tolerant, highly available system. It's developer-friendly, its operation is straightforward, and it's designed for reliability with minimal complexity. Deploy in seconds, with no complex configurations. Seamlessly integrates with modern cloud infrastructures. Built on SQLite, the world’s most popular database. Supports full-text search, Vector Search, and JSON documents. Access controls and encryption for secure deployments. Rigorous, automated testing ensures high quality. Clustering provides high availability and fault tolerance. Automatic node discovery simplifies clustering.
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