Compare the Top Distributed Databases for Windows as of May 2025

What are Distributed Databases for Windows?

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 for Windows currently available using the table below. This list is updated regularly.

  • 1
    eXtremeDB

    eXtremeDB

    McObject

    How is platform independent eXtremeDB different? - Hybrid data storage. Unlike other IMDS, eXtremeDB can be all-in-memory, all-persistent, or have a mix of in-memory tables and persistent tables - Active Replication Fabric™ is unique to eXtremeDB, offering bidirectional replication, multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more - Row & Columnar Flexibility for Time Series Data supports database designs that combine row-based and column-based layouts, in order to best leverage the CPU cache speed - Embedded and Client/Server. Fast, flexible eXtremeDB is data management wherever you need it, and can be deployed as an embedded database system, and/or as a client/server database system -A hard real-time deterministic option in eXtremeDB/rt Designed for use in resource-constrained, mission-critical embedded systems. Found in everything from routers to satellites to trains to stock markets worldwide
  • 2
    Apache Kudu

    Apache Kudu

    The Apache Software Foundation

    A Kudu cluster stores tables that look just like tables you're used to from relational (SQL) databases. A table can be as simple as a binary key and value, or as complex as a few hundred different strongly-typed attributes. Just like SQL, every table has a primary key made up of one or more columns. This might be a single column like a unique user identifier, or a compound key such as a (host, metric, timestamp) tuple for a machine time-series database. Rows can be efficiently read, updated, or deleted by their primary key. Kudu's simple data model makes it a breeze to port legacy applications or build new ones, no need to worry about how to encode your data into binary blobs or make sense of a huge database full of hard-to-interpret JSON. Tables are self-describing, so you can use standard tools like SQL engines or Spark to analyze your data. Kudu's APIs are designed to be easy to use.
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