27 Repos
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 27 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 ist ein hybrider Betriebssystemkern, der eine Mikrokernel-Architektur mit einer monolithischen Schicht für Systemdienste kombiniert. Er bietet eine Grundlage für die Betriebssystementwicklung und integriert standardisierte Systemaufruf-Schnittstellen, ein modulares Gerätetreiber-Framework und eine obligatorische Zugriffskontrollsicherheit. Die Architektur verfügt über einen Mach-basierten Mikrokernel und eine BSD-basierte monolithische Schicht. Sie nutzt einen nachrichtenbasierten Inter-Prozess-Kommunikationsbus für den sicheren Datenaustausch zwischen isolierten Kernel-Komponenten und Benutzerprozessen, neben einem objektorientierten Treiber-Framework, das hardwarespezifische Logik vom Kern-Kernel entkoppelt. Das System enthält eine Engine für obligatorische Zugriffskontrolle zur richtlinienbasierten Sicherheitsdurchsetzung und einen Remote-Kernel-Debugger zur Inspektion von Live-Speicher und zur Analyse von System-Panics. Zusätzliche Fähigkeiten decken die Multiprozessor-Planung, die Koordination von Hardwareressourcen und ein Build-System zur Generierung bootfähiger Images über verschiedene Architekturen hinweg ab. Das Projekt bietet Tools für das Kernel-Build-Management, die Generierung von Debug-Symbolen und ein Framework für die Systemaufruf-Verifizierung.
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 ist eine verteilte In-Memory-Daten-Grid- und Rechenplattform. Es fungiert als verteilte SQL-Datenbank und Speicher-Engine, die entwickelt wurde, um große Datensätze im RAM zu speichern und zu verarbeiten, um Latenzzeiten zu minimieren und die Berechnungsgeschwindigkeit zu erhöhen. Das System zeichnet sich durch eine mehrstufige Speicher-Engine aus, die die Datenplatzierung über Speicher und Festplatte verwaltet, um Hochgeschwindigkeitszugriff mit großer Kapazität in Einklang zu bringen. Es verfügt über ein verteiltes Rechen-Grid, das benutzerdefinierte Logik direkt auf den Knoten ausführt, auf denen sich die Daten befinden, um den Netzwerkverkehr zu reduzieren. Die Plattform bietet ein breites Spektrum an Funktionen, einschließlich ACID-Transaktionsmanagement, Standard-SQL-Abfragen und Key-Value-Operationen. Sie unterstützt die Aufnahme großer Datenmengen über reaktive Streams und bietet Integration durch mehrere Programmiersprachen, Standard-Datenbanktreiber und eine REST-API. Das System kann als verteilter Cluster mithilfe von Containern bereitgestellt oder über Kubernetes orchestriert werden. Das Projekt ist in Java geschrieben und kann über Binärarchive installiert werden.
Runs custom code across multiple server nodes to process data directly where it resides.
Deep Java Library (DJL) ist ein Java-Deep-Learning-Framework und eine JVM-Modell-Inferenz-Engine. Es bietet eine High-Level-API für den Aufbau und das Deployment von Deep-Learning-Modellen innerhalb des Java-Ökosystems und fungiert als plattformübergreifende Runtime für die Ausführung von Modellen auf CPUs, GPUs und Mobilgeräten. Die Bibliothek ist Engine-agnostisch, was es Benutzern ermöglicht, zwischen verschiedenen Deep-Learning-Engines wie PyTorch, TensorFlow und MXNet zu wechseln, während eine einheitliche API beibehalten wird. Dies ermöglicht das Deployment desselben Modells auf verschiedenen Backends, ohne den Anwendungscode zu ändern. Das Framework unterstützt den gesamten Machine-Learning-Lebenszyklus, einschließlich Aufbau und Training neuronaler Netzwerkarchitekturen sowie der Ausführung von Echtzeit-Inferenz. Es enthält Funktionen für verteiltes Machine-Learning-Inferenz-Scaling über Big-Data-Pipelines hinweg sowie die Möglichkeit, Modelle als Microservices oder innerhalb von Client-Anwendungen bereitzustellen. Das System deckt ein breites Spektrum an Domänen ab, einschließlich Computer Vision für Gesichtserkennung und Bildklassifizierung sowie Natural Language Processing für Sentiment-Analyse und Satz-Embeddings.
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 ist eine umfassende Client-Bibliothek, die entwickelt wurde, um Frontend-Anwendungen mit einem gehosteten Backend-as-a-Service zu integrieren. Sie bietet ein einheitliches Interface für die Interaktion mit einer PostgreSQL-Datenbank, Identitätsmanagementsystemen, Cloud-Objektspeicherung und Echtzeit-Datensynchronisierung. Die Bibliothek bietet ein isomorphes Client-Design, das sowohl in Browser- als auch in Serverumgebungen funktioniert. Sie zeichnet sich durch einen Typ-sicheren Ansatz aus, nutzt TypeScript, um Datenbankschemata direkt auf Client-seitige Definitionen zu mappen, und verwendet eine PostgREST-basierte API, um JavaScript-Aufrufe in RESTful-Requests zu übersetzen. Der Client deckt ein breites Spektrum an Funktionen ab, einschließlich Nutzerauthentifizierung via OAuth, OIDC und Passkeys sowie Sitzungsmanagement unter Verwendung signierter Token. Er verwaltet große Binärdaten über ein S3-kompatibles Speicher-Interface und ermöglicht Live-Anwendungsupdates via WebSocket-basierter Abonnements für Datenbankänderungen und Präsenzsynchronisierung. Zusätzliche Funktionalität umfasst den Aufruf von Serverless-Edge-Functions und die Durchführung von Ähnlichkeitssuchen unter Verwendung von Vektor-Embeddings.
Executes server-side logic on a globally distributed network to reduce latency.