Best Distributed Databases

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.

ClickHouse is a fast open-source OLAP database management system. It is column-oriented and allows to generate analytical reports using SQL queries in real-time. ClickHouse's performance exceeds comparable column-oriented database management systems currently available on the market. It processes hundreds of millions to more than a billion rows and tens of gigabytes of data per single server per second. ClickHouse uses all available hardware to its full potential to process each query as fast as possible. Peak processing performance for a single query stands at more than 2 terabytes per second (after decompression, only used columns). In distributed setup reads are automatically balanced among healthy replicas to avoid increasing latency. ClickHouse supports multi-master asynchronous replication and can be deployed across multiple datacenters. All nodes are equal, which allows avoiding having single points of failure.

Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes. Greenplum Database® project is released under the Apache 2 license. We want to thank all our current community contributors and are interested in all new potential contributions. For the Greenplum Database community no contribution is too small, we encourage all types of contributions. An open-source massively parallel data platform for analytics, machine learning and AI. Rapidly create and deploy models for complex applications in cybersecurity, predictive maintenance, risk management, fraud detection, and many other areas. Experience the fully featured, integrated, open source analytics platform.

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.

Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Automatic failover support between RegionServers. Easy to use Java API for client access. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX.

Google Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Fast and performant: Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics. Seamless scaling and replication: Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps. Simple and integrated: Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started.

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.