26 repositorios
Exposing custom functions to the templating engine to enable complex logic and data fetching during model generation.
Distinct from Python Integration: Distinct from Python integration in HTML: focuses on model generation logic rather than UI template rendering.
Explore 26 awesome GitHub repositories matching data & databases · Custom Python Integration. Refine with filters or upvote what's useful.
graphql-engine is an automated GraphQL API engine that transforms database tables and relationships into a queryable GraphQL schema. It functions as a federation gateway and mapper, instantly generating APIs with built-in filtering, pagination, and mutations from existing databases and remote schemas. The project distinguishes itself through a fine-grained access control layer that enforces row-level and field-level permissions. It further provides a real-time data subscription server that converts standard queries into live streams and a system for triggering event-driven webhooks and notifi
Integrates REST APIs and custom resolvers as actions to handle data validation, enrichment, and complex business workflows.
Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools. The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orches
Enables complex model generation logic by integrating custom Python functions.
Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t
Provides templates for defining processing tasks that require specific execution contexts directly within a pipeline.
This project is a curated directory and reference library of open-source Python applications. It serves as a comprehensive index designed to help developers study real-world software architecture, design patterns, and practical implementation strategies through a diverse collection of community-driven projects. The repository distinguishes itself by focusing on the analysis of production-ready software patterns rather than providing a single tool. It offers a structured way to explore how complex features, such as modular plugin systems, configuration management, and various deployment strate
Enables the execution of custom logic and calculations directly within data transformation pipelines.
Instructor is a framework designed for structured data extraction, validation, and language model integration. It functions as a library that transforms unstructured text into validated, type-safe objects by leveraging schema definitions and model-specific tool-calling capabilities. By acting as a validation middleware, the project ensures that language model outputs strictly conform to defined data structures. The library distinguishes itself through a robust validation-based retry loop that automatically re-submits failed responses with error feedback to iteratively correct schema complianc
Enables adding custom methods to extracted data models for domain-specific post-processing.
XNU es un kernel de sistema operativo híbrido que combina una arquitectura de microkernel con una capa monolítica para servicios del sistema. Proporciona una base para el desarrollo de sistemas operativos, incorporando interfaces de llamadas al sistema estandarizadas, un framework de controladores de dispositivos modular y seguridad de control de acceso obligatorio. La arquitectura cuenta con un microkernel basado en Mach y una capa monolítica basada en BSD. Utiliza un bus de comunicación entre procesos de paso de mensajes para el intercambio seguro de datos entre componentes aislados del kernel y procesos de espacio de usuario, junto con un framework de controladores orientado a objetos que desacopla la lógica específica del hardware del kernel central. El sistema incluye un motor de control de acceso obligatorio para la aplicación de seguridad basada en políticas y un depurador de kernel remoto para inspeccionar la memoria en vivo y analizar los pánicos del sistema. Las capacidades adicionales cubren la programación de multiprocesadores, coordinación de recursos de hardware y un sistema de compilación para generar imágenes arrancables a través de diferentes arquitecturas. El proyecto proporciona herramientas para la gestión de compilación del kernel, generación de símbolos de depuración y un framework para la verificación de llamadas al sistema.
Provides a scripting bridge to create custom debugging commands and type summaries for complex kernel data structures.
Maestro is a declarative mobile and web UI automation framework designed for end-to-end testing. It operates by querying the native accessibility tree of an application, allowing for black-box testing without requiring source code instrumentation or platform-specific dependencies. The framework distinguishes itself through a unified command syntax that abstracts interactions across Android, iOS, and web environments. It features a dynamic synchronization engine that automatically pauses test execution to account for non-deterministic animations and network-dependent content loading, ensuring
Integrates external scripts to handle dynamic data generation, complex conditional logic, or API interactions.
Home Assistant is a local home automation platform and server that acts as an IoT device orchestrator. It integrates diverse smart home hardware by wrapping third-party APIs into a standardized logic layer and stores all system state and historical statistics on local hardware to eliminate cloud dependencies. The system functions as a Matter IoT controller and an MQTT home automation bridge, allowing for local interoperability between different manufacturers. It features a state-based entity model and an internal event bus that decouple physical device logic from system automation. The platf
Executes standard Python string and numeric methods within templates to transform data values.
Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo
Integrates Python plugins with the Rust extraction pipeline via PyO3 with zero-copy buffers.
Albert is a keyboard launcher that opens files, applications, and runs commands by typing search queries into a search bar. It functions as a keyboard-driven workflow tool, enabling users to navigate their file system, launch installed applications, and execute shell commands without touching a mouse. The launcher processes user input through a plugin-based modular architecture, where functionality is extended by dynamically loaded C++ and Python plugins. Queries are dispatched to all enabled handlers in parallel, with results merged and ranked by a combination of match quality and historical
Creates Python modules following a stub file interface to add custom search and action capabilities.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Executes Python code to perform mapping steps via a communication layer between the engine and external processes.
Static Analyzer for Solidity and Vyper
Exposes a Python API for writing custom detectors that integrate directly into the static analysis pipeline.
InvenTree is an open-source inventory management platform built on Django, designed for tracking parts, stock levels, and supply chain operations through a web interface and REST API. The system uses barcodes—including QR codes, 1D barcodes, and Data Matrix codes—as primary identifiers for scanning, linking, and triggering inventory actions, and extends core functionality through a Python plugin framework supporting custom actions, UI panels, barcode handlers, and scheduled tasks. The platform distinguishes itself through a comprehensive plugin-based extensibility system that allows custom in
Integrates custom Python plugins into the system to add new behaviors and features without modifying core code.
billboard.js is a JavaScript charting library built on D3.js that renders interactive data visualizations from a single declarative configuration object. It supports a wide range of chart types including bar, line, pie, scatter, area, spline, step, candlestick, funnel, gauge, heatmap, radar, polar, treemap, bubble, donut, and sparkline charts, and can overlay multiple chart types within a single visualization. The library offers an opt-in Canvas rendering mode for improved performance with large datasets and high-density axis displays, alongside its standard SVG-based rendering. The library d
Provides a Python interface for generating charts from Python environments.
QOwnNotes is a desktop note editor that stores each note as a plain-text Markdown file on the local filesystem, avoiding proprietary formats and enabling direct file access. It functions as a Nextcloud Notes client, syncing notes and metadata with Nextcloud or ownCloud servers through a companion API service for versioning and sharing. The application also integrates with AI providers and exposes a local MCP server for external agents to search and fetch notes, and includes a companion browser extension for capturing web content, bookmarks, and screenshots. The editor distinguishes itself thr
Provides an online script repository for installing extensions that add custom features and AI integrations.
Beancount is a plain-text double-entry accounting system. It enforces zero-sum transactions, organizes accounts into a hierarchical five-type tree, and verifies balances at specific dates using precision-derived tolerances. Transactions are recorded in plain-text files with a strict syntax that supports currency-specific rounding, automatic interpolation of missing amounts, and comprehensive metadata including tags, links, and payee annotations. Beyond core bookkeeping, Beancount offers investment portfolio tracking with lot-based cost basis management, configurable booking strategies (FIFO,
Allows custom Python modules to transform, create, or delete accounting directives.
Ignite es una plataforma de cómputo y rejilla de datos distribuida en memoria. Funciona como una base de datos SQL distribuida y un motor de almacenamiento diseñado para almacenar y procesar grandes conjuntos de datos en RAM para minimizar la latencia y aumentar la velocidad de cálculo. El sistema se distingue por un motor de almacenamiento de varios niveles que gestiona la ubicación de los datos a través de la memoria y el disco para equilibrar el acceso de alta velocidad con una gran capacidad. Cuenta con una rejilla de cómputo distribuida que ejecuta lógica personalizada directamente en los nodos donde residen los datos para reducir el tráfico de red. La plataforma proporciona un amplio conjunto de capacidades, incluyendo gestión de transacciones ACID, consultas SQL estándar y operaciones de clave-valor. Admite la ingesta de datos de alto volumen a través de flujos reactivos y ofrece integración a través de múltiples lenguajes de programación, controladores de base de datos estándar y una API REST. El sistema puede desplegarse como un clúster distribuido utilizando contenedores u orquestarse mediante Kubernetes. El proyecto está escrito en Java y puede instalarse mediante archivos binarios.
Runs custom code across multiple server nodes to process data directly where it resides.
Deep Java Library es un framework de deep learning para Java y motor de inferencia de modelos para la JVM. Proporciona una API de alto nivel para construir y desplegar modelos de deep learning dentro del ecosistema Java, actuando como un runtime multiplataforma para ejecutar modelos en CPUs, GPUs y dispositivos móviles. La librería es agnóstica al motor, permitiendo a los usuarios cambiar entre diferentes motores de deep learning como PyTorch, TensorFlow y MXNet mientras mantienen una única API unificada. Esto permite el despliegue del mismo modelo en diferentes backends sin cambiar el código de la aplicación. El framework soporta el ciclo de vida completo del machine learning, incluyendo la construcción y entrenamiento de arquitecturas de redes neuronales y la ejecución de inferencia en tiempo real. Incluye capacidades para la inferencia de machine learning distribuida para escalar predicciones a través de tuberías de big data y la capacidad de desplegar modelos como microservicios o dentro de aplicaciones cliente. El sistema cubre una amplia gama de dominios, incluyendo visión por computadora para detección de rostros y clasificación de imágenes, y procesamiento de lenguaje natural para análisis de sentimiento y embeddings de oraciones.
Allows the execution of Python scripts for pre-processing and post-processing data during model inference.
MusicBrainz Picard is a metadata tagger and audio tag editor that identifies and tags audio files using the MusicBrainz community music database. It functions as a plugin-extensible tagging framework and a scriptable file organizer capable of reading and writing tags across various audio formats including MP3, FLAC, and WAV. The project is distinguished by its acoustic fingerprint identifier, which matches unknown music files to known recordings via sonic fingerprints. It features a custom scripting language for automating metadata transformations and organizing files into structured folder h
Integrates Python scripts to allow for flexible metadata modification and support for new audio formats.
supabase-js is a comprehensive client library designed to integrate frontend applications with a hosted backend-as-a-service. It provides a unified interface for interacting with a PostgreSQL database, identity management systems, cloud object storage, and real-time data synchronization. The library features an isomorphic client design that operates across both browser and server environments. It distinguishes itself through a type-safe approach, utilizing TypeScript to map database schemas directly to client-side definitions, and employs a PostgREST-based API to translate JavaScript calls in
Executes server-side logic on a globally distributed network to reduce latency.