Open Source Python Database Engines/Servers for Linux

Firebird RDBMS offers ANSI SQL features & runs on Linux, Windows & several Unix platforms. Features excellent concurrency & performance & powerful stored procedure & trigger language. More information and downloads at https://firebirdsql.org/, issue tracker is https://github.com/FirebirdSQL/firebird/issues

Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using pip install SpeechRecognition. The first software requirement is Python 2.6, 2.7, or Python 3.3+. This is required to use the library. PyAudio is required if and only if you want to use microphone input (Microphone). PyAudio version 0.2.11+ is required, as earlier versions have known memory management bugs when recording from microphones in certain situations. To hack on this library, first make sure you have all the requirements listed in the "Requirements" section.

The goal of PyTables is to enable the end user to efficiently and easily manipulate large datasets (both homogenous, i.e. arrays, and heterogenous, i.e. tables) on a persistent, hierarchical way.

KInterbasDB is a Python DB API 2.0 module for the relational databases Firebird and Interbase. KInterbasDB also exposes most of the native client API of the database engine, including two-phase commit, server-side events, and an administrative API.

PyDbLite is a pure-Python, fast, in-memory database with a Pythonic syntax (no SQL)

FLAG was designed to simplify the process of log file analysis and forensic investigations. FLAG facilitates efficient analysis of large quantities of data within an interactive environment. PyFlag is the reimplementation of FLAG in Python.

Cheshire3 is a fast Z39.50, SRW, XML search engine, written in Python for extensability and using C libraries for speed. Next generation of the Cheshire system (http://cheshire.berkeley.edu) and designed around a distributable, object oriented model.

Highly portable SQL query engine based in Python with transactions, recovery and client server mode.

This is an experiment in creating the best database API I can come up with. It is an alternative to query languages. The latest version is being written in Scala using Berkeley DB Java and will run on the Java VM.

This is a testbed for projects of personal interests, especially academic projects.

A toolbox for searching and extracting text in Bible databases. Currently in the process of converting the code repository to subversion. It may or may not live at sf.net afterward.

BlackRay is a persistent in-memory high performance relational database written in C++. It combines regular database features with search engine like performance. Please visit https://forge.softmethod.de for the main development site.

CUBRID QA project contains a test-automation tool and many kinds of test cases for CUBRID database.

OLAP aggregation engine with MDX support, web interface (slice & dice) and relational database backend. See "Documentation" for more. Presentation: http://cubulus.sourceforge.net/. See online demo courtesy of Webfaction, http://alxtoth.webfactional.com

An implementation of a truly relational database language with Python. Inspired by Date and Darwen's 'Third Manifesto'.

DirectoryStorage - a ZODB Storage that stores objects in many files, one file per revision of each object. Designed to be robust, manageable, fault tolerant, and scalable.

FGL is a tightly-integrated self-contained development & execution environment utilizing best-of-breed programming tools and methodologies, optimized web/application server, highly-scalable relational/object database, and robust extension interface.

FlightFeather's goal is "social networking for everyone". This means that anyone should have a chance to run a popular social networking site -- on minimal hardware, and without wasting bandwidth.

In Green Cloud here is a central global server which does the work of global database management along with providing complex analysis services for the users (farmers, scientists) through the client machines and the public access web portal. The existing agricultural databases throughout the world can also be used for inference purposes and their data may also be updated by our server. The meteorological servers can be accessed for climate data.This server-client architecture mandatory in present and even near-future circumstances as the client machine has to be low-power and low-cost for the feasibility of the system in Indian context. Therefore, the complex analysis (Expert system analysis in accordance with world-wide data) has to be done on server. The environment data is collected via sensors is uploaded to the website

Information Meta View (IMV) system attempts to create a web standard for information storage in a decentralized database. Information is stored as a graph like structure spanning several service providers.

A simple database management system developed by students of the Faculty of Organization and Informatics, University of Zagreb.

KML is a knowledge base with support of logical modeling. Advanced model is used to represent knowledge as a set of statements similar to natural language sentences. This project hosts a set of model storage library and server (vrb-ols) and clients.

L2jdertin, Stable L2J Cliente: Interlude.

LAMP eGovernment Database Project offers state and local governments a free open source, web-enabled system for use in developing public information sites. You can also use this system for government-to-government systems as well.

Embedding the meaning of the datas in the storage can improve the consistency of the stored information and simplify the its exchange: the semantic structure is defined through a strong semantic typing of the data and the use of external dictionaries.