Open Source Python Database Software for Mac
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Python Database Software for Mac
View 96 business solutionsBrowse free open source Python Database Software for Mac and projects below. Use the toggles on the left to filter open source Python Database Software for Mac by OS, license, language, programming language, and project status.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Firebird
A powerful, cross platform, SQL database system
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 -
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LibreOffice
A free and powerful office suite
LibreOffice is a free and powerful office suite, and a successor to OpenOffice. Its clean interface and feature-rich tools help you unleash your creativity and enhance your productivity. LibreOffice is Free and Open Source Software (FOSS) – development is open to new talent and new ideas, and our software is tested and used daily by a large and devoted user community. Your documents will look professional and clean, regardless of their purpose: a letter, a master thesis, a brochure, financial reports, marketing presentations, technical drawings and diagrams. LibreOffice makes your work look great while you focus on the content, thanks to its powerful styles system and structuring tools. LibreOffice is compatible with a wide range of document formats such as Microsoft® Word (.doc, .docx), Excel (.xls, .xlsx), PowerPoint (.ppt, .pptx) and Publisher. But LibreOffice goes much further with its native support for a modern and open standard (OpenDocument Format). -
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SQLAlchemy
The Database Toolkit for Python
SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. SQLAlchemy provides a full suite of well known enterprise-level persistence patterns, designed for efficient and high-performing database access, adapted into a simple and Pythonic domain language. An industrial strength ORM, built from the core on the identity map, unit of work, and data mapper patterns. These patterns allow the transparent persistence of objects using a declarative configuration system. Domain models can be constructed and manipulated naturally, and changes are synchronized with the current transaction automatically. A relationally-oriented query system, exposing the full range of SQL's capabilities explicitly, including joins, subqueries, correlation, and most everything else, in terms of the object model. Writing queries with the ORM uses the same techniques of relational composition you use when writing SQL. -
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Grafana
Leading open-source visualization and observability platform
Grafana OSS is the leading open-source platform for visualization and observability. It enables teams to query, visualize, alert on, and explore telemetry data from multiple sources in a single interface. With support for 100+ data source plugins—including Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, and OpenTelemetry—Grafana helps teams correlate metrics, logs, and traces across applications and infrastructure. Users can build interactive dashboards with rich visualizations, template variables, and reusable panels to monitor systems and troubleshoot issues in real time. Grafana includes capabilities such as ad hoc data exploration, alerting, annotations, and flexible query support. Its extensible plugin ecosystem integrates with cloud platforms, databases, and developer tools—allowing teams to build observability workflows without vendor lock-in. The easiest way to get started with Grafana is with Grafana Cloud, our fully managed, full-stack observability platform. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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SpeechRecognition
Speech recognition module for Python
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. -
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OpenViking
Context database designed specifically for AI Agents
OpenViking is an open-source context database engineered for efficient indexing and retrieval of large amounts of unstructured or semi-structured context data used by AI applications. It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is implemented with performance in mind, often leveraging optimized data structures that balance fast reads and writes with minimal resource consumption. Developers can integrate OpenViking into modern AI stacks to unify context storage across services, enabling consistent session history, personalized responses, and richer search experiences. -
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The MonetDB project has moved to http://www.monetdb.org/.
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A HUD and stats tracker for your poker games written in Python. Supports Holdem, Omaha, Stud, Razz and Draw games.
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mysqlclient
MySQL/MariaDB connector for Python
Summary: mysqlclient is a MySQL/MariaDB connector for Python, providing a convenient interface for database interactions. -
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SQL Explorer
Easily share data across your company via SQL queries
SQL Explorer aims to make the flow of data between people fast, simple, and confusion-free. It is a Django-based application that you can add to an existing Django site, or use as a standalone business intelligence tool. Quickly write and share SQL queries in a simple, usable SQL editor, preview the results in the browser, share links, download CSV, JSON, or Excel files (and even expose queries as API endpoints, if desired), and keep the information flowing! Comes with support for multiple connections, to many different SQL database types, a schema explorer, query history (e.g. lightweight version control), a basic security model, in-browser pivot tables, and more. SQL Explorer values simplicity, intuitive use, unobtrusiveness, stability, and the principle of least surprise. SQL Explorer is inspired by any number of great query and reporting tools out there. -
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pagodo
Automate Google Hacking Database scraping and searching
pagodo automates Google searching for potentially vulnerable web pages and applications on the Internet. It replaces manually performing Google dork searches with a web GUI browser. There are 2 parts. The first is ghdb_scraper.py that retrieves the latest Google dorks and the second portion is pagodo.py that leverages the information gathered by ghdb_scraper.py. This version of pagodo also supports native HTTP(S) and SOCKS5 application support, so no more wrapping it in a tool like proxychains4 if you need proxy support. You can specify multiple proxies to use in a round-robin fashion by providing a comma-separated string of proxies using the -p switch. To start off, pagodo.py needs a list of all the current Google dorks. The repo contains a dorks/ directory with the current dorks when the ghdb_scraper.py was last run. It's advised to run ghdb_scraper.py to get the freshest data before running pagodo.py. -
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mycli
CLI for MySQL with auto-completion and syntax highlighting
mycli is an enhanced command-line client for MySQL and MariaDB that features syntax highlighting and intelligent auto-completion for database queries. Built for developers and DBAs, it improves productivity with features like query history, table suggestions, and colored output. The tool is designed to be both powerful and user-friendly, making interactive database work more efficient and visually intuitive. -
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MySQL for Python
MySQL database connector for Python programming
MySQLdb is a Python DB API-2.0-compliant interface; see PEP-249 for details. For up-to-date versions of MySQLdb, use the homepage link. Supported versions: * MySQL versions from 3.23 to 5.5; 5.0 or newer recommended. MariaDB should also work. * Python versions 2.4-2.7; Python 3 support coming soon. ZMySQLDA is a Database Adapter for Zope2. -
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MongoEngine
A Python Object-Document-Mapper for working with MongoDB
MongoEngine is a Python Object-Document Mapper for working with MongoDB. -
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SQLModel
SQL databases in Python, designed for simplicity, compatibility
SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness. SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python-type annotations, and powered by Pydantic and SQLAlchemy. -
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tccutil.py
Command line tool to modify OS X's accessibility database (TCC.db)
Apple has a utility in /usr/bin named tccutil, but it only supports one command, which is to reset the entire database. It has been like this for many versions of macOS. I wanted a command-line utility that would be able to add, remove, list, and take other actions. This tool needs SIP disabled in order to function. The risk of doing so is up to you. tccutil.py can be installed without any additional software. Depending how you have your $PATH variable setup, you can simply type tccutil (instead of the full path) and it will run this utility instead of Apple's. This utility needs super-user priveleges for most operations. It is important that you either run this as root or use sudo, otherwise it won't work and you will end up with “permission denied” errors. -
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Advanced Alchemy
A carefully crafted, thoroughly tested, optimized companion library
advanced-alchemy is an opinionated ORM toolkit built on SQLAlchemy and designed for integration with the Litestar web framework. It simplifies common ORM patterns such as CRUD, pagination, and async support while providing extensibility and best practices out of the box. It’s designed to accelerate backend development for modern Python web apps. -
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Aerich
A database migrations tool for TortoiseORM, ready to production
Aerich is a lightweight database migration tool built for Tortoise ORM, an async ORM for Python. It handles schema evolution through versioned migrations, enabling developers to update and rollback database changes with ease. Designed for asyncio-based applications, Aerich is ideal for fast-moving Python projects that need simple migration control without heavy dependencies. -
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CodeChecker
CodeChecker is an analyzer tooling, defect database
CodeChecker is a static analysis infrastructure built on the LLVM/Clang Static Analyzer toolchain, replacing scan-build in a Linux or macOS (OS X) development environment. Executes Clang-Tidy and Clang Static Analyzer with Cross-Translation Unit analysis, Statistical Analysis (when checkers are available). Creates the JSON compilation database by wiretapping any build process (e.g., CodeChecker log -b "make"). Automatically analyzes GCC cross-compiled projects: detecting GCC or Clang compiler configuration and forming the corresponding clang analyzer invocations. Incremental analysis: Only the changed files and its dependencies need to be reanalyzed. False positive suppression with a possibility to add review comments. Result visualization in command line or in static HTML. Web application for viewing discovered code defects with a streamlined, easy experience (with PostgreSQL, or SQLite backend). -
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Finance Database
This is a database of 300.000+ symbols containing Equities, ETFs, etc.
As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what type of companies or ETFs are available is incredibly challenging with there being millions of companies and derivatives available on the market. Sure, the most traded companies and ETFs can quickly be found simply because they are known to the public (for example, Microsoft, Tesla, S&P500 ETF or an All-World ETF). However, what else is out there is often unknown. This database tries to solve that. It features 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets. It, therefore, allows you to obtain a broad overview of sectors, industries, types of investments and much more. The aim of this database is explicitly not to provide up-to-date fundamentals or stock data as those can be obtained with ease (with the help of this database) by using yfinance, FundamentalAnalysis or ThePassiveInvestor. -
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CogDB
Micro Graph Database for Python Applications
Cog is a lightweight, embedded graph database for Go that provides a simple interface for storing and querying graph-based data structures, making it useful for knowledge representation and graph analytics. -
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MySQL Export Laravel Migrations
A MySQL Workbench plugin which exports a Model to Laravel 5 Migrations
A MySQL Workbench plugin that allows for exporting a model to Laravel 5 migrations that follow PSR-2 coding standards. When exported, each migration is generated and saved in it's own, properly named, migration file. -
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SuperDuperDB
Integrate, train and manage any AI models and APIs with your database
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. Just using Python. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Train models on your data in your datastore simply by querying without additional ingestion and pre-processing. -
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WikiSQL
A large annotated semantic parsing corpus for developing NL interfaces
A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is the dataset released along with our work Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning. Regarding tokenization and Stanza, when WikiSQL was written 3-years ago, it relied on Stanza, a CoreNLP python wrapper that has since been deprecated. If you'd still like to use the tokenizer, please use the docker image. We do not anticipate switching to the current Stanza as changes to the tokenizer would render the previous results not reproducible. -
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data-diff
Efficiently diff rows across two different databases
We're excited to announce the launch of a new open-source product, data-diff that makes comparing datasets across databases fast at any scale. data-diff automates data quality checks for data replication and migration. In modern data platforms, data is constantly moving between systems, and at the modern data volume and complexity, systems go out of sync all the time. Until now, there has not been any tooling to ensure that when the data is correctly copied. Replicating data at scale, across hundreds of tables, with low latency and at a reasonable infrastructure cost is a hard problem, and most data teams we’ve talked to, have faced data quality issues in their replication processes. The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates.
