Best Columnar Databases for Linux

Sadas Engine is the fastest Columnar Database Management System both in Cloud and On Premise. Turn Data into Information with the fastest columnar Database Management System able to perform 100 times faster than transactional DBMSs and able to carry out searches on huge quantities of data over a period even longer than 10 years. Every day we work to ensure impeccable service and appropriate solutions to enhance the activities of your specific business. SADAS srl, a company of the AS Group , is dedicated to the development of Business Intelligence solutions, data analysis applications and DWH tools, relying on cutting-edge technology. The company operates in many sectors: banking, insurance, leasing, commercial, media and telecommunications, and in the public sector. Innovative software solutions for daily management needs and decision-making processes, in any sector

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.

Hypertable delivers scalable database capacity at maximum performance to speed up your big data application and reduce your hardware footprint. Hypertable delivers maximum efficiency and superior performance over the competition which translates into major cost savings. A proven scalable design that powers hundreds of Google services. All the benefits of open source with a strong and thriving community. C++ implementation for optimum performance. 24/7/365 support for your business-critical big data application. Unparalleled access to Hypertable brain power by the employer of all core Hypertable developers. Hypertable was designed for the express purpose of solving the scalability problem, a problem that is not handled well by a traditional RDBMS. Hypertable is based on a design developed by Google to meet their scalability requirements and solves the scale problem better than any of the other NoSQL solutions out there.

InfiniDB is a column-store DBMS optimized for OLAP workloads. It has a distributed architecture to support Massive Paralllel Processing (MPP). It uses MySQL as its front-end such that users familiar with MySQL can quickly migrate to InfiniDB. Due to this fact, users can connect to InfiniDB using any MySQL connector. InfiniDB applies MVCC to do concurrency control. It uses term System Change Number (SCN) to indicate a version of the system. In its Block Resolution Manager (BRM), it utilizes three structures, version buffer, version substitution structure, and version buffer block manager, 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. For each column, InfiniDB applies range partitioning and stores the minimum and maximum value of each partition in a small structure called extent map.

QikkDB is a GPU accelerated columnar database, delivering stellar performance for complex polygon operations and big data analytics. When you count your data in billions and want to see real-time results you need qikkDB. We support Windows and Linux operating systems. We use Google Tests as the testing framework. There are hundreds of unit tests and tens of integration tests in the project. For development on Windows, Microsoft Visual Studio 2019 is recommended, and its dependencies are CUDA version 10.2 minimal, CMake 3.15 or newer, vcpkg, boost. For development on Linux, the dependencies are CUDA version 10.2 minimal, CMake 3.15 or newer, and boost. This project is licensed under the Apache License, Version 2.0. You can use an installation script or dockerfile to install qikkDB.

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.

We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem. Parquet is built from the ground up with complex nested data structures in mind, and uses the record shredding and assembly algorithm described in the Dremel paper. We believe this approach is superior to simple flattening of nested namespaces. Parquet is built to support very efficient compression and encoding schemes. Multiple projects have demonstrated the performance impact of applying the right compression and encoding scheme to the data. Parquet allows compression schemes to be specified on a per-column level, and is future-proofed to allow adding more encodings as they are invented and implemented. Parquet is built to be used by anyone. The Hadoop ecosystem is rich with data processing frameworks, and we are not interested in playing favorites.