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258 repositorios

Awesome GitHub RepositoriesReal-Time Data Streaming

Platforms for processing and delivering data streams in real-time.

Distinguishing note: Focuses on event-driven streaming integrated into the database layer.

Explore 258 awesome GitHub repositories matching data & databases · Real-Time Data Streaming. Refine with filters or upvote what's useful.

Awesome Real-Time Data Streaming GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • public-apis/public-apisAvatar de public-apis

    public-apis/public-apis

    441,986Ver en GitHub↗

    Este proyecto es un directorio curado por la comunidad de endpoints de servicios REST y GraphQL diseñado para ayudar a los desarrolladores a descubrir e integrar fuentes de datos de terceros. Funciona como un registro centralizado donde los servicios externos se organizan por dominio para facilitar la creación rápida de prototipos y el desarrollo de aplicaciones. El registro se basa en un modelo de contribución revisado por pares, utilizando control de versiones distribuido para gestionar las actualizaciones y garantizar la precisión de los endpoints listados. Para mantener una alta calidad de los datos, el proyecto emplea validación basada en esquemas para todos los envíos entrantes y compila los datos estructurados en un sitio web estático y buscable para una recuperación eficiente. El directorio cubre un amplio espectro de capacidades de integración, incluyendo recuperación de datos financieros, servicios de geolocalización y varias API de utilidad para tareas como detección de idiomas, procesamiento de medios y verificación de identidad. Al proporcionar un índice centralizado de estos servicios, el proyecto ayuda a los desarrolladores a identificar proveedores de datos confiables para diversos requisitos funcionales.

    Provides structured interfaces for retrieving global weather information.

    Pythonapiapisdataset
    Ver en GitHub↗441,986
  • awesome-selfhosted/awesome-selfhostedAvatar de awesome-selfhosted

    awesome-selfhosted/awesome-selfhosted

    299,516Ver en GitHub↗

    Este proyecto es un directorio curado por la comunidad de software de código abierto diseñado para su implementación en entornos de servidores privados y laboratorios domésticos. Sirve como un recurso integral para descubrir alternativas independientes y autohospedadas a los servicios en la nube convencionales, permitiendo a los usuarios mantener la propiedad total de los datos y el control sobre su infraestructura digital. El directorio está estructurado a través de una taxonomía jerárquica que organiza una vasta colección de aplicaciones en categorías lógicas, que van desde la gestión de medios y análisis de datos hasta la comunicación privada y herramientas de productividad en equipo. Se distingue por un proceso de revisión por pares colaborativo, donde los miembros de la comunidad validan la calidad y relevancia de cada envío para garantizar que el directorio siga siendo preciso y confiable. El proyecto cubre una amplia superficie de capacidades, incluyendo automatización de infraestructura, implementación de servicios basados en contenedores y gestión de configuración declarativa. Estas herramientas ayudan a los usuarios a mantener entornos de servidor reproducibles y gestionar dependencias de servicios complejas en hardware privado. El directorio se mantiene como un repositorio con control de versiones, asegurando que todas las actualizaciones y cambios impulsados por la comunidad sean rastreados y transparentes.

    Routes and publishes live video or audio feeds across multiple protocols to enable low-latency distribution and recording.

    awesomeawesome-listcloud
    Ver en GitHub↗299,516
  • itseez/opencvAvatar de Itseez

    Itseez/opencv

    89,221Ver en GitHub↗

    OpenCV is an open-source computer vision library and visual analysis toolkit. It provides a framework for processing static images and dynamic video frames to analyze visual data and extract information using deep learning. The project functions as a real-time image processing framework, enabling the execution of vision algorithms on live video streams for immediate analysis and data processing. The toolkit covers a broad range of capabilities including image pattern recognition, real-time video analysis, and visual data extraction. It also supports automated visual inspection for detecting

    Executes vision algorithms on live video streams for immediate visual analysis and data processing.

    C++
    Ver en GitHub↗89,221
  • apache/dubboAvatar de apache

    apache/dubbo

    41,519Ver en GitHub↗

    Dubbo is a Java RPC framework and microservices governance platform designed for high-performance remote procedure calls in distributed architectures. It provides the foundational components necessary to connect distributed services across a network, including a binary data serialization library and a distributed service registry. The platform distinguishes itself through a comprehensive governance suite that manages service discovery, load balancing, and traffic routing. It enables precise control over network traffic via conditional routing and a pluggable extension mechanism based on a ser

    Provides high-performance data streaming capabilities between clients and servers using the gRPC protocol.

    Javadistributed-systemsdubboframework
    Ver en GitHub↗41,519
  • google-research/google-researchAvatar de google-research

    google-research/google-research

    38,139Ver en GitHub↗

    This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

    Combines diverse meteorological inputs from satellite and gauge-based sources into a unified framework to improve predictive accuracy.

    Jupyter Notebookaimachine-learningresearch
    Ver en GitHub↗38,139
  • surrealdb/surrealdbAvatar de surrealdb

    surrealdb/surrealdb

    32,397Ver en GitHub↗

    SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer

    Powers reactive, real-time experiences using built-in subscriptions, event triggers, and streaming updates.

    Rustbackend-as-a-servicecloud-databasedatabase
    Ver en GitHub↗32,397
  • hasura/graphql-engineAvatar de hasura

    hasura/graphql-engine

    32,064Ver en GitHub↗

    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

    Converts standard GraphQL queries into live queries that stream real-time updates to clients.

    TypeScriptaccess-controlapiautomatic-api
    Ver en GitHub↗32,064
  • ossrs/srsAvatar de ossrs

    ossrs/srs

    28,971Ver en GitHub↗

    SRS is a real-time media server designed to ingest, route, and distribute live audio and video streams across various transport protocols. It functions as a multi-protocol stream relay, including a dedicated RTMP media gateway and a WebRTC signaling server to coordinate peer-to-peer media exchanges. The system features a multi-protocol relay engine that transforms incoming media packets between different transport formats without re-encoding. This allows it to serve as a video delivery proxy that routes live media from a single source to multiple concurrent viewers using diverse delivery prot

    Provides a multi-protocol relay engine to synchronize and forward live media streams between diverse transport protocols.

    C++audiocc-plus-plus
    Ver en GitHub↗28,971
  • heygen-com/hyperframesAvatar de heygen-com

    heygen-com/hyperframes

    28,209Ver en GitHub↗

    Hyperframes is an HTML-to-video rendering engine and composition tool that transforms web layouts and CSS into encoded video files. It functions as a headless browser video pipeline and a distributed video rendering framework, allowing users to create seekable animations and programmatic motion designs using HTML, CSS, and JavaScript. The project differentiates itself as an AI agent video orchestrator, enabling the automation of video scripts and compositions through natural language prompts. It supports distributed video encoding by splitting rendering tasks across multiple serverless functi

    Pipes captured browser frames directly to the encoder in real time to avoid writing intermediate files to disk.

    TypeScript
    Ver en GitHub↗28,209
  • apache/incubator-seataAvatar de apache

    apache/incubator-seata

    25,984Ver en GitHub↗

    Seata is a distributed transaction coordinator designed to ensure data consistency and atomicity across microservices. It provides a centralized framework for managing global transactions, preventing partial data updates across different databases and services. The project implements multiple transaction modes to balance consistency and performance. This includes an automatic mode that uses rollback logs to coordinate compensation without modifying business logic, a try-confirm-cancel pattern for resources lacking native ACID support, and a saga orchestration engine for managing long-lived bu

    Uses gRPC for high-performance bidirectional data streaming between clients and coordinators.

    Javaatconsistencydistributed-transaction
    Ver en GitHub↗25,984
  • cinnamon/kotaemonAvatar de Cinnamon

    Cinnamon/kotaemon

    25,139Ver en GitHub↗

    Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

    Streams incremental results from reasoning pipelines to the user interface in real-time.

    Pythonchatbotllmsopen-source
    Ver en GitHub↗25,139
  • reactivex/rxswiftAvatar de ReactiveX

    ReactiveX/RxSwift

    24,648Ver en GitHub↗

    RxSwift is a reactive programming library for Swift that provides a framework for managing push-based data flows and composing asynchronous, event-based programs. It utilizes observable sequences and functional operators to transform and filter asynchronous sequences through a declarative approach. The library is distinguished by its ability to link asynchronous data streams directly to user interface elements, automating view updates via reactive data binding. It includes specialized tools for tracking UI control properties and events on the main thread, as well as the ability to encapsulate

    Distributes a single subscription across multiple observers with support for replaying historical elements.

    Swift
    Ver en GitHub↗24,648
  • pubkey/rxdbAvatar de pubkey

    pubkey/rxdb

    23,048Ver en GitHub↗

    This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a robust framework for managing application state by synchronizing data across browsers, mobile devices, and server-side runtimes. By treating local storage as the primary source of truth, it enables applications to remain functional without network connectivity, automatically reconciling changes with remote backends once a connection is restored. The database distinguishes itself through a modular architecture that supports cross-environment synchronization and high-performance d

    Provides observable queries and documents that push state changes to the application for dynamic, responsive user interfaces.

    TypeScriptangularbrowser-databasecouchdb
    Ver en GitHub↗23,048
  • vonng/ddiaAvatar de Vonng

    Vonng/ddia

    22,648Ver en GitHub↗

    This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

    Matches incoming documents against stored queries in real time to notify users when data meets specific criteria.

    Pythonbookdatabaseddia
    Ver en GitHub↗22,648
  • apache/incubator-rocketmqAvatar de apache

    apache/incubator-rocketmq

    22,461Ver en GitHub↗

    RocketMQ is a distributed messaging and streaming platform designed for building event-driven applications. It serves as middleware to decouple services using publish-subscribe and request-reply patterns, and functions as a transactional messaging system that ensures atomicity by linking message delivery to local transaction outcomes. The platform includes specialized capabilities as a Kubernetes-native message broker for container orchestration environments and an MQTT broker for ingesting event data from mobile applications and hardware terminals. The system covers high-throughput data str

    Provides a platform for processing and transforming continuous event streams in real-time to synchronize state and derive insights.

    Java
    Ver en GitHub↗22,461
  • serengil/deepfaceAvatar de serengil

    serengil/deepface

    22,226Ver en GitHub↗

    Deepface is a comprehensive deep learning library for facial recognition and demographic analysis. It provides a modular pipeline that handles the entire lifecycle of facial processing, including detection, geometric alignment, and the transformation of facial images into high-dimensional numerical vector embeddings for identity verification and similarity comparison. The library distinguishes itself through a model ensemble approach, which combines predictions from multiple pre-trained neural networks to improve classification accuracy and reduce bias. It also integrates advanced security fe

    Processes live video feeds to perform continuous face recognition and attribute analysis.

    Pythonage-predictionarcfacedeep-learning
    Ver en GitHub↗22,226
  • redis/go-redisAvatar de redis

    redis/go-redis

    22,159Ver en GitHub↗

    This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

    Supports append-only log structures for real-time event streaming and asynchronous data processing.

    Gogogolangredis
    Ver en GitHub↗22,159
  • vectordotdev/vectorAvatar de vectordotdev

    vectordotdev/vector

    22,071Ver en GitHub↗

    Vector is a high-performance observability data pipeline designed to collect, transform, and route logs, metrics, and traces across distributed infrastructure. It functions as a modular engine that decouples data ingestion from processing and transmission, utilizing a component-based architecture to connect diverse sources to multiple destinations. The project distinguishes itself through a focus on reliability and flow control. It implements backpressure-aware data movement to prevent data loss during traffic spikes and utilizes disk-backed event buffering to ensure durability during network

    Routes processed logs and metrics into message topics for real-time streaming.

    Rusteventsforwarderhacktoberfest
    Ver en GitHub↗22,071
  • vercel/aiAvatar de vercel

    vercel/ai

    21,885Ver en GitHub↗

    This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

    Transmits raw data and text tokens in real time to provide immediate feedback during generation.

    TypeScriptanthropicartificial-intelligencegemini
    Ver en GitHub↗21,885
  • wistbean/learn_python3_spiderAvatar de wistbean

    wistbean/learn_python3_spider

    21,802Ver en GitHub↗

    This project is a comprehensive educational guide and framework for building web scrapers using Python. It provides a course-based approach to data extraction, combining a Python crawler framework with tutorials on web reverse engineering and network traffic analysis. The project distinguishes itself by covering advanced extraction challenges, including the decryption of obfuscated JavaScript and the bypass of anti-scraping measures. It specifically addresses mobile application scraping through the simulation of user interactions and the interception of network traffic. The capability surfac

    Supports capturing rapidly changing information via persistent socket connections for live data streaming.

    Pythonpython-scriptpython-spiderpython3
    Ver en GitHub↗21,802
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Explorar subetiquetas

  • Connector-Based IngestionPulling data from external protocols and services into a streaming engine using pluggable adapters. **Distinct from Real-Time Data Streaming:** Specific to the ingestion mechanism via connectors, whereas Real-Time Data Streaming is the broader platform capability.
  • Crawl Data StreamingStreaming discovered web page data in real-time via websockets or webhooks. **Distinct from Real-Time Data Streaming:** Distinct from general Real-Time Data Streaming: specifically targets the streaming of web crawl results as they are discovered.
  • Cybersecurity Filtering PipelinesReal-time data streaming pipelines that process and classify network data for cybersecurity threat detection. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on cybersecurity-specific filtering and classification of network data, rather than general real-time data streaming platforms.
  • Data Stream Management3 sub-etiquetasManagement of append-only time series streams used for logs, events, and metrics. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the long-term structural management of append-only streams rather than the transport layer.
  • Device Telemetry StreamsProcessing of continuous hardware and system logs for real-time UI updates. **Distinct from Real-Time Data Streaming:** Focuses on device-level logs and performance metrics rather than database event streaming or LLM text streams.
  • Edge Medical Data Inference1 sub-etiquetaRuns real-time AI inference on streaming data from medical devices at the edge. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: specifically targets AI inference on medical device data at the edge, not general data streaming.
  • Exchange Market Data StreamsSubscribes to live trade, order book, and kline data via WebSocket connections with automatic reconnection and multiplexing. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on exchange-specific market data streams with reconnection logic.
  • GraphQL Subscriptions3 sub-etiquetasImplementation of real-time data streams from server to client using a graph-based subscription model. **Distinct from Real-Time Data Streaming:** Distinct from general real-time data streaming by specifically using GraphQL subscriptions for UI updates.
  • Hardware Streaming InterfacesUnified abstraction layers for streaming raw IQ data from software-defined radio peripherals into processing engines. **Distinct from Real-Time Data Streaming:** Distinct from general real-time streaming: focuses on raw IQ data acquisition from radio hardware.
  • Kafka Stream Exporters3 sub-etiquetasConnectors for routing processed document content into message topics for real-time streaming. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the export/egress of data into Kafka topics.
  • Live Stream Buffering3 sub-etiquetasTemporary storage and sequential merging of live broadcast segments into VOD files. **Distinct from Real-Time Data Streaming:** Focuses on capturing live segments into files rather than event-driven database streaming
  • Live Stream Visualizers1 sub-etiquetaVisualization tools that connect to real-time data sources to display streaming time series data as it arrives. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on visualization of live streams, not the streaming infrastructure itself.
  • Map Data ProcessorsSystems that integrate in-memory data structures into real-time processing pipelines. **Distinct from Real-Time Map Data Processors:** Distinct from Real-Time Data Streaming: focuses on streaming data from in-memory maps specifically, rather than general event streams.
  • Media Stream SubscriptionsControls for enabling or disabling the reception of specific audio, video, or data tracks within a communication session. **Distinct from Stream Subscriptions:** Distinct from Stream Subscriptions: focuses on the selective reception of media tracks in a real-time communication room rather than general message listening.
  • Permission Query EvaluatorsServices that evaluate permission queries in tens of milliseconds by traversing stored relation tuples and streaming changes. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on evaluating permission queries rather than general data streaming.
  • Programmable Stream ProcessingExecution of compiled modules to provide high-performance, real-time data transformations. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: specifically covers the programmability and custom logic execution within the stream
  • Public Content Streams1 sub-etiquetaSpecialized streams for receiving public posts based on filters or a global data firehose. **Distinct from Real-Time Data Streaming:** Targets the retrieval of public social content rather than general database event streaming.
  • Query-to-Stream Adapters1 sub-etiquetaConverting database queries into reactive streams for application logic. **Distinct from Real-Time Data Streaming:** Specifically focuses on transforming queries into streams rather than general streaming platforms
  • RAG Stream IngestersIngests and indexes real-time data from streaming sources to enable dynamic, context-aware retrieval for RAG systems. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on ingestion and indexing for RAG, not general event-driven streaming.
  • Real-Time Charting Engines1 sub-etiquetaRendering engines optimized for displaying high-frequency data streams in real-time charts. **Distinct from Real-Time Data Streaming:** Distinct from general streaming platforms: focuses on the visual rendering of high-frequency data rather than backend stream processing.
  • Real-Time Feature Computation2 sub-etiquetasProcesses live event streams joined with historical data to create features for real-time analytics or machine learning. **Distinct from Real-Time Data Streaming:** Focuses on the computation of stateful features for AI/ML from streams, rather than general data delivery.
  • Real-Time Lighting Visualizers1 sub-etiquetaEngines for synchronizing lighting output with high-frequency media, audio, and network data streams. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on visual rendering and lighting synchronization rather than general data stream processing.
  • Real-Time Text Streaming3 sub-etiquetasLow-latency processing of continuous text input streams for real-time AI applications. **Distinct from Real-Time Data Streaming:** Specifically addresses text/token streams for LLMs rather than event-driven database streaming.
  • Real-Time Visual Stream Processors2 sub-etiquetasFrameworks for processing live video data streams for immediate analysis. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on visual/image stream analysis rather than general event-driven data pipelines.
  • Real-time Visualizations8 sub-etiquetasInterfaces for pushing and rendering live data streams into visual formats. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the presentation and dashboarding of the stream rather than the pipeline infrastructure.
  • Scan Result Callbacks1 sub-etiquetaReal-time processing functions that execute for every server response during a discovery session. **Distinct from Real-Time Data Streaming:** Distinct from general data streaming by focusing on a callback mechanism for individual HTTP response events.
  • Search Index SynchronizationStreaming database change logs to a search engine in real-time to maintain an updated index. **Distinct from Real-Time Data Streaming:** Specifically focuses on synchronizing databases with search engines rather than general data streaming or client-side sync.
  • Session Data RecordingCapturing live timing and telemetry streams for offline archival. **Distinct from Real-Time Data Streaming:** Focuses on recording for backup rather than just real-time streaming processing
  • Snapshot-Based BootstrappingCaptures a point-in-time copy of existing table data to seed the stream before real-time capture begins. **Distinct from Real-Time Data Streaming:** Focuses on the initial snapshotting of existing data to seed a stream, rather than the ongoing process of real-time streaming.
  • Social Media Stream ConsumersOpens a persistent connection to receive and filter social media posts in real time from the terminal. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: specifically consumes social media platform streams (e.g., Twitter), not general event-driven database streaming.
  • Stream Computing Engines1 sub-etiquetaLightweight processing engines that transform events in real-time to derive immediate insights. **Distinct from Real-Time Data Streaming:** Distinct from general streaming by its focus on real-time computation and transformation logic.
  • Stream Consumption ControlsAdministrative controls for pausing and resuming real-time data ingestion streams. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the lifecycle control of the stream consumption, not the streaming platform itself.
  • Stream DeduplicationMechanisms for identifying and removing redundant entries within real-time data streams. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the specific logic of removing redundant stream entries rather than the streaming transport itself.
  • Stream ProxiesServices for routing and publishing live media feeds across multiple protocols. **Distinct from Real-Time Data Streaming:** Focuses on media stream proxying, distinct from general data streaming.
  • Stream Relays4 sub-etiquetasMechanisms for synchronizing and forwarding media streams between distributed infrastructure nodes. **Distinct from Real-Time Data Streaming:** Distinct from general data streaming: focuses on media-specific stream synchronization and relaying between servers.
  • Stream Subscriptions2 sub-etiquetasReal-time message listening for multiple concurrent clients. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the subscription mechanism for multiple clients rather than the streaming platform itself.
  • Structured Collection Streams1 sub-etiquetaYields individual items from a generated list incrementally to reduce latency. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on streaming structured list items from LLM outputs rather than general event-driven data.
  • Trade Event Streams1 sub-etiquetaInstant delivery of individual trade execution events as they occur. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: specifically targets the push of raw trade execution events.
  • Visual Masking StreamsSystems for generating interactive segmentation masks in real-time visual data streams. **Distinct from Real-Time Data Streaming:** Distinct from real-time data streaming: focuses on the generation of visual segmentation masks rather than general data stream delivery.
  • Weather Data8 sub-etiquetasAPIs providing real-time and historical meteorological information. **Distinct from Real-Time Data Streaming:** Distinct from Real-Time Data Streaming: focuses on the domain of weather data rather than the streaming infrastructure.
  • Wireless Signal StreamingReal-time streaming of raw wireless sensor data over network protocols. **Distinct from Real-Time Data Streaming:** Focuses on streaming raw CSI sensor data rather than database event streams
  • gRPC Data Streaming2 sub-etiquetasImplementation of high-performance remote procedure calls for streaming data from producers to viewers. **Distinct from Real-Time Data Streaming:** Specifies the use of gRPC as the transport mechanism for real-time data delivery.