Best Distributed Databases for Windows

Objectivity/DB is a massively scalable, high performance, distributed Object Database (ODBMS). It is extremely good at handling complex data, where there are many types of connections between objects and many variants. Objectivity/DB can also serve as a massively scalable, high performance graph database. Its DO query language supports standard data retrieval queries as well as high-performance path-based navigational queries. Objectivity/DB is a distributed database, presenting a Single Logical View of its managed data. Data can be hosted on a single machine or distributed across up to 65,000 machines. Connected items can span machines. Objectivity/DB runs on 32 or 64-bit processors running Windows, Linux, and Mac OS X. APIs include: C++, C#, Java and Python. All platform and language combinations are interoperable. For example, objects stored by a program using C++ on Linux can be read by a C# program on Windows and a Java program on Mac OS X.

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

AllegroGraph is a breakthrough solution that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph utilizes unique federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. AllegroGraph provides users with an integrated version of Gruff, a unique browser-based graph visualization software tool for exploring and discovering connections within enterprise Knowledge Graphs. Franz’s Knowledge Graph Solution includes both technology and services for building industrial strength Entity-Event Knowledge Graphs based on best-of-class tools, products, knowledge, skills and experience.

Corporations need a way to ensure uninterrupted operation of their systems, high speed of data processing, and reliability of storage. The in-memory technologies have proven themselves well in solving these problems. For more than 10 years, Tarantool has been helping companies all over the world build smart caches, data marts, and golden client profiles while saving server capacity. Reduce the cost of storing credentials compared to siloed solutions and improve the service and security of client applications. Reduce data management costs of maintaining a large number of disparate systems that store customer identities. Increase sales by improving the speed and quality of customer recommendations for goods or services through the analysis of user behavior and user data. Improve mobile and web channel service by accelerating frontends to reduce user outflow. IT systems of large organizations operate in a closed loop of a local network, where data circulates unprotected.

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.

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.