13 repositorios
Mechanisms for connecting proprietary feeds into a workspace.
Explore 13 awesome GitHub repositories matching data & databases · Custom Data Source Integrations. Refine with filters or upvote what's useful.
OpenBB is a financial data platform and investment research terminal designed to aggregate, normalize, and distribute market data across analytical workflows. It functions as a comprehensive ecosystem that bridges disparate financial data providers with custom applications, spreadsheets, and internal modeling infrastructure. The platform distinguishes itself through a provider-based data abstraction layer that normalizes heterogeneous financial APIs into a consistent, schema-driven format. This architecture supports quantitative research automation and the construction of interactive, widget-
Connects proprietary data feeds and external services directly into a workspace to extend analytical capabilities.
React-admin is a framework for building data-driven administrative interfaces that connect to REST or GraphQL backends. It provides a comprehensive suite of tools for managing the full lifecycle of administrative applications, including resource-oriented routing, declarative form scaffolding, and context-driven state management. By utilizing a modular adapter-based architecture, the framework abstracts backend communication, allowing developers to build consistent CRUD interfaces that handle data fetching, authentication, and synchronization automatically. The project distinguishes itself thr
Implements standardized methods to connect administrative interfaces with any REST or GraphQL backend.
Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena
Connects specialized data feed classes to ingest financial market data from various sources.
GitHub Copilot is an AI-powered development platform designed to integrate large language models directly into coding environments. It functions as an interactive assistant and an agentic workflow orchestrator, enabling developers to automate code generation, perform automated code reviews, and execute complex, multi-step development tasks through natural language prompts. The platform distinguishes itself through its autonomous agent capabilities, which allow for repository-level research, implementation planning, and code modifications across multiple files. It supports a modular architectu
Connects AI agents to custom data sources and tools through protocol-based server integrations.
SuperAGI is a comprehensive marketing automation platform and customer data system designed to orchestrate multi-channel engagement workflows. It functions as a no-code workflow orchestrator, allowing users to build complex, automated task sequences triggered by real-time user behavior, transactional data, or scheduled events. By centralizing customer profiles and interaction history, the platform enables businesses to manage end-to-end marketing operations from a single interface. The platform distinguishes itself through its deep integration with e-commerce storefronts and its ability to ex
Syncs customer profiles, purchase history, and event logs from external online stores to maintain a single, accurate record of user activity.
Dask es un framework de computación paralela y un programador de tareas distribuido diseñado para escalar flujos de trabajo de ciencia de datos en Python desde máquinas individuales hasta grandes clústeres. Funciona como un gestor de recursos de clúster que orquesta la lógica computacional representando las tareas y sus dependencias como grafos acíclicos dirigidos. Esta arquitectura permite al sistema automatizar la distribución de cargas de trabajo a través del hardware disponible mientras gestiona requisitos de ejecución complejos. El proyecto se distingue por un motor de evaluación perezosa que difiere las operaciones de datos hasta que se solicitan explícitamente, permitiendo la optimización global del grafo y una asignación eficiente de recursos. Incorpora el volcado de datos consciente de la memoria para evitar fallos del sistema al procesar conjuntos de datos que exceden la memoria disponible, y utiliza la fusión de grafos de tareas para combinar secuencias de operaciones en pasos de ejecución únicos, minimizando la sobrecarga de programación y la comunicación entre nodos. La plataforma proporciona una superficie de capacidades integral para el análisis de datos a gran escala, incluyendo soporte para aprendizaje automático distribuido, integración de computación de alto rendimiento y procesamiento de datos en paralelo. Ofrece herramientas extensas para la gestión del ciclo de vida del clúster, perfilado de rendimiento y monitoreo en tiempo real de la ejecución de tareas. Los usuarios pueden desplegar estos entornos en diversas infraestructuras, incluyendo hardware local, proveedores de nube, sistemas en contenedores y clústeres de computación de alto rendimiento.
Converts between lazy task objects and parallel data structures to bridge custom processing logic with high-level data analysis.
DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono
Ingests metadata from a wide range of enterprise systems using custom connectors to handle high volumes of data.
This project is a collection of responsive CSS Grid dashboard templates and a data visualization UI kit. It provides a set of HTML layouts designed for building analytics interfaces and monitoring views for KPIs and business metrics that adapt to different screen sizes. The toolkit is library-agnostic, allowing the connection of static HTML templates to any external data source or third-party charting library without requiring custom adapter code. It uses a template-driven approach to separate the visual structure of the dashboard from the underlying data. The capabilities cover the assembly
Provides mechanisms for connecting proprietary data feeds into the dashboard workspace.
Nango is an open-source platform that connects applications to external APIs by managing authentication, data synchronization, and custom function execution. It provides a managed runtime for TypeScript integration functions, handling OAuth flows, credential storage, and token refresh for hundreds of external APIs while keeping secrets isolated from application code. The platform distinguishes itself by exposing integration functions as discoverable tools for AI agents through an MCP server or API, with per-user credential isolation that keeps provider secrets out of the agent loop. It offers
Allows integration behavior to be configured differently for each individual customer.
Maplibre GL JS is a WebGL map rendering engine and vector tile map library used to create interactive web maps. It serves as a web-based GIS visualization tool and an interactive map interface framework for rendering geographic data and vector tiles on web pages. The library provides capabilities for 3D terrain rendering and the integration of custom 3D content. It supports complex geospatial data visualization through the use of heatmaps, clusters, and 3D extrusions, while allowing for custom map styling and environmental effect customization. The system covers a broad range of functional a
Integrates diverse geographic data sources including vector tiles, GeoJSON, raster DEM, and video sources.
Falco is an eBPF runtime security monitor and cloud native detection engine that identifies abnormal behavior and security threats across hosts and containers. It functions as a Linux kernel event auditor, capturing system calls and kernel events in real-time to detect malicious activity. The system distinguishes itself through a rule-based threat detection model that evaluates system activity against a library of community-maintained rules and custom security definitions. It enriches raw kernel events with container and Kubernetes metadata to provide observability into isolated environments
Introduces new streams of system events into the processing pipeline to enable monitoring of non-standard data sources.
pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability. The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and
Provides a base class for implementing proprietary or alternative data providers.
Connector-X is a high-performance SQL data extraction library and bridge for transferring relational database records into memory-efficient data structures. It functions as a parallel database connector and federated query engine capable of executing and joining queries across multiple remote database connections to aggregate data locally. The project distinguishes itself through a zero-copy approach to data loading, which transfers SQL query results into memory structures without duplicating data. It maximizes throughput by partitioning SQL queries into threads, employing parallel columnar a
Provides extensibility to integrate new database connectors via custom connection logic, partitioning strategies, and type parsing.