Best Key-Value Databases for JanusGraph

Find and compare the best Key-Value Databases for JanusGraph in 2024

Use the comparison tool below to compare the top Key-Value Databases for JanusGraph on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

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    Apache Cassandra Reviews

    Apache Cassandra

    Apache Software Foundation

    1 Rating
    The Apache Cassandra database provides high availability and scalability without compromising performance. It is the ideal platform for mission-critical data because it offers linear scalability and demonstrated fault-tolerance with commodity hardware and cloud infrastructure. Cassandra's ability to replicate across multiple datacenters is first-in-class. This provides lower latency for your users, and the peace-of-mind that you can withstand regional outages.
  • 2
    Oracle Berkeley DB Reviews
    Berkeley DB is a set of embedded key-value databases libraries that provide high-performance data management services for applications.
  • 3
    Google Cloud Bigtable Reviews
    Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.
  • 4
    Apache HBase Reviews

    Apache HBase

    The Apache Software Foundation

    Apache HBase™, is used when you need random, real-time read/write access for your Big Data. This project aims to host very large tables, billions of rows and X million columns, on top of clusters of commodity hardware.
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