Best Columnar Databases for Linux of 2024

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.

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.

Greenplum Database®, an open-source data warehouse, is a fully featured, advanced, and fully functional data warehouse. It offers powerful and fast analytics on petabyte-scale data volumes. Greenplum Database is uniquely designed for big data analytics. It is powered by the most advanced cost-based query optimizer in the world, delivering high analytical query performance with large data volumes. The Apache 2 license is used to release Greenplum Database®. We would like to thank all of our community contributors. We are also open to new contributions. We encourage all contributions to the Greenplum Database community, no matter how small. Open-source, massively parallel data platform for machine learning, analytics, and AI. Rapidly create and deploy models to support complex applications in cybersecurity, predictive management, risk management, fraud detection, among other areas. The fully integrated, open-source analytics platform is now available.

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.

Parquet was created to provide the Hadoop ecosystem with the benefits of columnar, compressed data representation. Parquet was built with complex nested data structures and uses the Dremel paper's record shredding/assemblage algorithm. This approach is better than flattening nested namespaces. Parquet is designed to support efficient compression and encoding strategies. Multiple projects have shown the positive impact of the right compression and encoding scheme on data performance. Parquet allows for compression schemes to be specified per-column. It is future-proofed to allow for more encodings to be added as they are developed and implemented. Parquet was designed to be used by everyone. We don't want to play favorites in the Hadoop ecosystem.

Hypertable provides scalable database capacity at maximum speed to speed up big data applications and reduce your hardware footprint. Hypertable offers superior performance and efficiency over other competitors, which can translate into significant cost savings. It is a proven, scalable design that powers hundreds Google services. Open source brings all the benefits of open-source with a vibrant community. C++ implementation for optimal performance. Support for your business-critical big-data application is available 24/7/365 The employer of all core Hypertable developers provides unrivalled access to the Hypertable brain power. Hypertable was created to solve the scalability issue. This problem is not well handled by traditional RDBMSs. Hypertable is a Google-developed design that meets their scalability requirements. It solves the scale problem better then any other NoSQL solutions.

InfiniDB is a column-store DBMS that is optimized for OLAP workloads. It supports Massive Paralllel Processing (MPP) thanks to its distributed architecture. It uses MySQL as its front end so that MySQL-savvy users can migrate to InfiniDB quickly. Users can connect to InfiniDB with any MySQL connector. InfiniDB applies MVCC to do concurrency control. It uses the term System Change Number (SCN), to indicate a particular version of the system. It uses three structures in its Block Resolution Manager (BRM), version buffer, version substitution, and version buffer block manger, to manage multiple versions. InfiniDB applies deadlock detection to resolve conflicts. InfiniDB uses MySQL as its front end and supports all MySQL syntaxes including foreign keys. InfiniDB is a columnar DBMS. InfiniDB applies range partitioning to each column and stores the minimum and maximal values of each partition in a small structure called an extent map.

QikkDB is an GPU-accelerated columnar database that delivers outstanding performance for complex polygon operations as well as big data analytics. qikkDB is the best choice if you want to count your data in billions, and see real-time results. We are compatible with both Windows and Linux operating systems. Google Tests is our testing framework. The project contains hundreds of unit and tens integration tests. Microsoft Visual Studio 2019 is recommended for Windows development. Its dependencies include CUDA version 10.2 minimum, CMake 3.15 and newer, vcpkg., boost. The dependencies for Linux development are CUDA version 10.2 minimum, CMake 3.15 and newer, boost, and vcpkg. This project is licensed under Version 2.0 of the Apache License. To install qikkDB, you can use an installation script (or dockerfile).