Best On-Premise Columnar Databases of 2024

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

We make BI and Big Data analytics easier and more efficient. Our goal is to empower business users, make BI specialists and always-busy business more independent when solving data-driven business problems. Querona is a solution for those who have ever been frustrated by a lack in data, slow or tedious report generation, or a long queue to their BI specialist. Querona has a built-in Big Data engine that can handle increasing data volumes. Repeatable queries can be stored and calculated in advance. Querona automatically suggests improvements to queries, making optimization easier. Querona empowers data scientists and business analysts by giving them self-service. They can quickly create and prototype data models, add data sources, optimize queries, and dig into raw data. It is possible to use less IT. Users can now access live data regardless of where it is stored. Querona can cache data if databases are too busy to query live.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

The Open, Multi-Cloud Stack to Modern Data Apps. Built on Apache Cassandra™, an open-source Apache Cassandra™. Global scale and 100% uptime without vendor lock in You can deploy on multi-clouds, open-source, on-prem and Kubernetes. For a lower TCO, use elastic and pay-as you-go. Stargate APIs allow you to build faster with NoSQL, reactive, JSON and REST. Avoid the complexity of multiple OSS projects or APIs that don’t scale. It is ideal for commerce, mobile and AI/ML. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Richly interactive apps that are viral-ready and elastic using REST, GraphQL and JSON. Pay-as you-go Apache Cassandra DBaaS which scales easily and affordably

Azure Table storage can store petabytes semi-structured data at low costs and keeps costs down. Table storage is able to scale up, unlike many cloud-based or on-premise data stores. Also, availability is not a concern. With geo-redundant storage, data can be replicated three times within one region and three times in another region hundreds of miles away. Flexible data such as web app user data, address books, device data and other metadata can be stored in table storage. You can also use table storage to build cloud applications without having to lock down the data model to specific schemas. Different rows can have different structures in the same table, so you can easily change your application and table schema without having to take it offline. Table storage embraces a strong consistency model.

Kudu clusters store tables that look exactly like the tables in relational (SQL), databases. A table can have a single binary key and value or a multitude of strongly-typed attributes. Every table has a primary key that is made up of one or more columns, just like SQL. This could be a single column, such as a unique user ID, or a compound key, such as a (host.metric.timestamp) tuple to a machine-time-series database. Rows can be easily read, updated, and deleted by their primary keys. Kudu's data model is simple and easy to use. It makes it easy to port legacy applications and build new ones. You can use standard tools such as Spark or SQL engines to analyze your tables. Tables are self-describing. Kudu's APIs were designed to be simple to use.