Best Columnar Databases for Windows of 2025

Sadas Engine is the fastest columnar database management system in cloud and on-premise. Sadas Engine is the solution that you are looking for. * Store * Manage * Analyze It takes a lot of data to find the right solution. * BI * DWH * Data Analytics The fastest columnar Database Management System can turn data into information. It is 100 times faster than transactional DBMSs, and can perform searches on large amounts of data for a period that lasts longer than 10 years.

Apache Cassandra is an ideal database solution for situations that require both high scalability and availability while maintaining optimal performance. Its linear scalability and established fault-tolerance capabilities, whether on standard hardware or cloud environments, position it as a top-tier choice for essential data management. Additionally, Cassandra excels in its ability to replicate data across various datacenters, ensuring minimal latency for users and offering reassurance by safeguarding against regional failures. This unique combination of features makes Cassandra a reliable option for businesses that prioritize resilience and efficiency in their data operations.

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.

Greenplum Database® stands out as a sophisticated, comprehensive, and open-source data warehouse solution. It excels in providing swift and robust analytics on data volumes that reach petabyte scales. Designed specifically for big data analytics, Greenplum Database is driven by a highly advanced cost-based query optimizer that ensures exceptional performance for analytical queries on extensive data sets. This project operates under the Apache 2 license, and we extend our gratitude to all current contributors while inviting new ones to join our efforts. In the Greenplum Database community, every contribution is valued, regardless of its size, and we actively encourage diverse forms of involvement. This platform serves as an open-source, massively parallel data environment tailored for analytics, machine learning, and artificial intelligence applications. Users can swiftly develop and implement models aimed at tackling complex challenges in fields such as cybersecurity, predictive maintenance, risk management, and fraud detection, among others. Dive into the experience of a fully integrated, feature-rich open-source analytics platform that empowers innovation.

Explore a diverse array of SQL features that allow you to build applications ranging from straightforward analytics to complex hybrid transactional and analytical processing. If you're eager to uncover insights from your data, striving for efficiency, or facing tight deadlines, MonetDB can deliver query results in just seconds or even faster. For those looking to leverage or modify their own code and requiring specialized functions, MonetDB provides hooks to integrate user-defined functions in SQL, Python, R, or C/C++. Become part of the vibrant MonetDB community that spans over 130 countries, including students, educators, researchers, startups, small businesses, and large corporations. Embrace the forefront of analytical database technology and ride the wave of innovation! Save time with MonetDB’s straightforward installation process, allowing you to quickly get your database management system operational. This accessibility ensures that users of all backgrounds can efficiently harness the power of data for their projects.

QikkDB is a high-performance, GPU-accelerated columnar database designed to excel in complex polygon computations and large-scale data analytics. If you're managing billions of data points and require immediate insights, qikkDB is the solution you need. It is compatible with both Windows and Linux operating systems, ensuring flexibility for developers. The project employs Google Tests for its testing framework, featuring hundreds of unit tests alongside numerous integration tests to maintain robust quality. For those developing on Windows, it is advisable to use Microsoft Visual Studio 2019, with essential dependencies that include at least CUDA version 10.2, CMake 3.15 or a more recent version, vcpkg, and Boost libraries. Meanwhile, Linux developers will also require a minimum of CUDA version 10.2, CMake 3.15 or newer, and Boost for optimal operation. This software is distributed under the Apache License, Version 2.0, allowing for a wide range of usage. To simplify the installation process, users can opt for either an installation script or a Dockerfile to get qikkDB up and running seamlessly. Additionally, this versatility makes it an appealing choice for various development environments.

MariaDB Platform is an enterprise-level open-source database solution. It supports transactional, analytical, and hybrid workloads, as well as relational and JSON data models. It can scale from standalone databases to data warehouses to fully distributed SQL, which can execute millions of transactions per second and perform interactive, ad-hoc analytics on billions upon billions of rows. MariaDB can be deployed on prem-on commodity hardware. It is also available on all major public cloud providers and MariaDB SkySQL, a fully managed cloud database. MariaDB.com provides more information.

A Kudu cluster organizes its data into tables, which resemble the tables found in traditional relational (SQL) databases. These tables can range from straightforward binary key-value pairs to intricate structures featuring hundreds of distinct, strongly-typed attributes. Similar to SQL databases, each table has a primary key composed of one or more columns, which could be a singular column, such as a unique user ID, or a composite key like a tuple of (host, metric, timestamp) typically used in machine time-series databases. Rows can be quickly accessed, modified, or removed using their primary key, ensuring efficient data management. The straightforward data model of Kudu facilitates the migration of legacy systems or the creation of new applications without the hassle of encoding data into binary formats or deciphering complex databases filled with difficult-to-read JSON. Additionally, the tables are self-describing, allowing users to leverage common tools such as SQL engines or Spark for data analysis tasks. The user-friendly APIs provided by Kudu further enhance its accessibility for developers. Overall, Kudu streamlines data handling while maintaining a robust structure.

Parquet was developed to provide the benefits of efficient, compressed columnar data formats to all projects within the Hadoop ecosystem. Designed with intricate nested data structures in consideration, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we view as a more effective method than merely flattening nested namespaces. This format is engineered for optimal compression and encoding, with various projects showcasing the significant performance enhancements achieved through the appropriate application of these techniques. Parquet enables users to define compression schemes at the individual column level and is designed to adapt to new encodings as they emerge and become available. Furthermore, Parquet is intended for universal usage, embracing the diverse array of data processing frameworks in the Hadoop ecosystem without playing favorites among them. By promoting interoperability and flexibility, Parquet aims to empower all users to leverage its capabilities effectively.