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15 repositorios

Awesome GitHub RepositoriesStream Ingestion

Processes for writing continuous data streams into tabular formats with transactional guarantees.

Distinct from Table Data Processing: Focuses on the ingestion of live streams into tables, whereas Table Data Processing is general manipulation.

Explore 15 awesome GitHub repositories matching data & databases · Stream Ingestion. Refine with filters or upvote what's useful.

Awesome Stream Ingestion GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • delta-io/deltaAvatar de delta-io

    delta-io/delta

    8,596Ver en GitHub↗

    Delta is a lakehouse table format that brings ACID transactions and data warehouse consistency to large scale data lakes on cloud object storage. It serves as an ACID transaction manager, coordinating atomic commits and serializable isolation for concurrent reads and writes across distributed compute engines. The project provides a multi-engine interoperability layer that uses format translation to allow diverse SQL engines and processing frameworks to read and write the same tables. It functions as a data versioning system, utilizing a transaction log to enable time travel, historical snapsh

    Writes continuous record flows to tables with exactly-once processing guarantees during concurrent operations.

    Scalaacidanalyticsbig-data
    Ver en GitHub↗8,596
  • getmoto/motoAvatar de getmoto

    getmoto/moto

    8,550Ver en GitHub↗

    Moto is a cloud service mockery framework and API mock server that simulates AWS infrastructure locally. It allows developers to test cloud-dependent code and verify infrastructure-as-code templates without deploying real resources or incurring costs. The project functions as an SDK interceptor that can patch existing service clients to redirect requests to a local mock environment. It can also be run as a standalone HTTP server, enabling any programming language to interact with the simulated endpoints. The framework covers a vast array of simulated capabilities, including data storage, com

    Simulates the ingestion of continuous data streams into delivery targets.

    Pythonawsbotoec2
    Ver en GitHub↗8,550
  • gojek/feastAvatar de gojek

    gojek/feast

    7,095Ver en GitHub↗

    Feast is a machine learning feature store and MLOps data infrastructure layer. It provides a centralized system for managing and serving features across offline training and online production environments, utilizing an online feature serving layer for low-latency retrieval. The project centers on a feature registry that acts as a central catalog for defining, governing, and discovering feature services. It employs a unified data access layer to decouple feature retrieval from physical storage and includes a point-in-time data generator to create historically accurate training datasets that pr

    Implements a real-time pipeline that processes event streams and updates the online feature store.

    Python
    Ver en GitHub↗7,095
  • feast-dev/feastAvatar de feast-dev

    feast-dev/feast

    6,727Ver en GitHub↗

    Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

    Consumes data from stream sources and loads it into online and offline stores for feature serving.

    Pythonbig-datadata-engineeringdata-quality
    Ver en GitHub↗6,727
  • materializeinc/materializeAvatar de MaterializeInc

    MaterializeInc/materialize

    6,314Ver en GitHub↗

    Materialize is a streaming SQL database that continuously ingests live data from sources such as Kafka, Redpanda, PostgreSQL, and MySQL, and incrementally maintains materialized views. It provides a PostgreSQL-compatible query engine that accepts standard SQL over the PostgreSQL wire protocol, enabling any existing SQL client or BI tool to query real-time data. The system also includes a Model Context Protocol (MCP) server that exposes live materialized view data to AI agents, providing fresh context without polling. Materialize distinguishes itself through its ability to offer configurable c

    Continuously ingests data from Kafka and Redpanda topics into streaming SQL sources.

    Rust
    Ver en GitHub↗6,314
  • apache/pinotAvatar de apache

    apache/pinot

    6,098Ver en GitHub↗

    Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

    Processes continuous data streams from real-time sources into queryable tabular formats.

    Java
    Ver en GitHub↗6,098
  • apache/hudiAvatar de apache

    apache/hudi

    6,097Ver en GitHub↗

    Apache Hudi is an open-source table format that brings ACID transactions, incremental processing, and multi-modal indexing to data lakes. It provides atomic commits with snapshot isolation, rollback, and optimistic concurrency control for reliable data lake operations, while supporting upserts, record-level updates, and deletions in large analytical datasets. The project distinguishes itself through a timeline-based architecture that coordinates all write operations, enabling features like time-travel querying, incremental change streaming, and multi-modal query views that include snapshot, i

    Ingests both streaming and batch data from Spark, Flink, and Kafka into a transactional data lake.

    Javaapacheflinkapachehudiapachespark
    Ver en GitHub↗6,097
  • greptimeteam/greptimedbAvatar de GreptimeTeam

    GreptimeTeam/greptimedb

    5,968Ver en GitHub↗

    GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

    Reads metrics formatted in InfluxDB line protocol from Kafka topics and ingests them.

    Rustanalyticscloud-nativedatabase
    Ver en GitHub↗5,968
  • dlt-hub/dltAvatar de dlt-hub

    dlt-hub/dlt

    5,472Ver en GitHub↗

    dlt es una herramienta de ingesta de datos en Python y framework de pipeline ETL diseñado para obtener datos de diversas fuentes y persistirlos en destinos estructurados. Funciona como un motor de inferencia de esquemas que detecta automáticamente tipos de datos y aplana estructuras JSON anidadas en tablas relacionales, moviendo datos desde fuentes a lakehouses, almacenes de datos o bases de datos vectoriales. El proyecto destaca por la generación de pipelines impulsada por IA, utilizando modelos de lenguaje de gran tamaño para crear código de extracción y conectores para APIs REST. También admite almacenamiento vectorial multimodal y población especializada de bases de datos vectoriales para soportar aplicaciones de IA y machine learning. El framework cubre una amplia gama de capacidades, incluyendo evolución automática de esquemas, carga incremental de datos mediante seguimiento de estado y validación de calidad de datos mediante la aplicación de contratos de datos. Proporciona herramientas para la normalización de datos relacionales, transformaciones pre y post-carga, y una variedad de adaptadores de destino para bases de datos SQL y almacenes de objetos en la nube. La observabilidad se maneja a través de paneles de ejecución de pipelines, seguimiento de linaje de columnas y verificación de versiones de esquema mediante hashes basados en contenido.

    Processes streams with multiple item types using discriminated unions to dispatch data to specific tables.

    Pythondatadata-engineeringdata-lake
    Ver en GitHub↗5,472
  • openpanel-dev/openpanelAvatar de Openpanel-dev

    Openpanel-dev/openpanel

    5,349Ver en GitHub↗

    OpenPanel is a self-hosted product analytics platform designed for tracking user behavior and visualizing product metrics on private infrastructure. It provides a comprehensive system for collecting events across web, mobile, and server environments while ensuring complete ownership of data. The platform distinguishes itself through a privacy-first approach, utilizing cookieless event tracking and regional data residency to simplify regulatory compliance. It integrates large language models via the Model Context Protocol, enabling users to query behavioral data and analyze trends using natura

    Ingests high-throughput event data through a Kafka-compatible broker for parallel processing.

    TypeScriptalternativeanalyticsopen-source
    Ver en GitHub↗5,349
  • arroyosystems/arroyoAvatar de ArroyoSystems

    ArroyoSystems/arroyo

    4,819Ver en GitHub↗

    Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming data with event-time semantics, enabling accurate windowed aggregations, joins, and stateful computations on unbounded event streams. The platform uses native Rust execution for high throughput and low latency, with periodic checkpointing for exactly-once fault tolerance and horizontal scaling across distributed workers. The system integrates deeply with Kafka for reading and writing topics with exactly-once delivery and supports change data capture (CDC) from MySQL and Postg

    Consumes and produces messages from Kafka topics with exactly-once semantics using SQL queries.

    Rustdatadata-stream-processingdev-tools
    Ver en GitHub↗4,819
  • intelowlproject/intelowlAvatar de intelowlproject

    intelowlproject/IntelOwl

    4,605Ver en GitHub↗

    IntelOwl es una plataforma de inteligencia de amenazas y motor de orquestación de seguridad diseñado para agregar, analizar y enriquecer observables de seguridad. Funciona como una herramienta de investigación de incidentes de seguridad y un agregador de inteligencia de amenazas, recopilando datos sobre archivos, dominios y direcciones IP de diversas fuentes internas y externas. El sistema se diferencia por su automatización de flujos de trabajo basada en playbooks, permitiendo a los usuarios definir secuencias reutilizables de tareas de análisis que activan trabajos posteriores basados en resultados previos. Unifica datos de seguridad dispares en un esquema común y utiliza controles de acceso a nivel de protocolo para restringir la ejecución de analizadores según la sensibilidad de los datos. La plataforma cubre una amplia gama de capacidades, incluyendo la ingesta de indicadores basada en eventos, flujos de trabajo automatizados para centros de operaciones de seguridad (SOC) y enriquecimiento de inteligencia. Proporciona herramientas para la organización de investigaciones y la visualización de resultados de análisis a través de paneles para correlacionar hallazgos. El proyecto está implementado en Python.

    Imports streams of observables or files automatically for immediate processing and analysis.

    Pythoncyber-securitycyber-threat-intelligencecybersecurity
    Ver en GitHub↗4,605
  • hellodigua/chatlabAvatar de hellodigua

    hellodigua/ChatLab

    4,522Ver en GitHub↗

    ChatLab is a self-hosted chat database and data pipeline designed to normalize, store, and analyze large-scale social conversation histories. It functions as an analytics platform that uses large language models to extract patterns and insights from messaging data imported from multiple platforms. The system distinguishes itself through an AI-powered analysis engine that utilizes vector-based history analysis and agent-based function calling to summarize conversation trends. It further identifies behavioral patterns by generating visual analytics, including heatmaps, word clouds, and activity

    Processes large chat imports by streaming data through multiple worker threads to maintain responsiveness at scale.

    TypeScriptaichat-analysischat-history
    Ver en GitHub↗4,522
  • uptrace/uptraceAvatar de uptrace

    uptrace/uptrace

    4,098Ver en GitHub↗

    Uptrace is an OpenTelemetry-based observability platform designed to collect, store, and analyze distributed traces, metrics, and logs. It functions as a centralized logging backend, a distributed tracing system, and a metrics engine to monitor application performance and system health. The platform is distinguished by AI-powered operational capabilities, allowing users to query telemetry data and manage monitoring dashboards using natural language. It specifically includes specialized monitoring for generative AI pipelines, tracking token usage and response quality for LLM interactions and r

    Uses a message queue to retain telemetry data during database downtime and optimize insert throughput.

    Goapmapplication-monitoringclickhouse
    Ver en GitHub↗4,098
  • fasterxml/jackson-databindAvatar de FasterXML

    FasterXML/jackson-databind

    3,729Ver en GitHub↗

    Jackson-databind is a Java serialization framework and JSON data binding library used to convert Java objects to JSON and vice versa. It functions as a JSON streaming API that generates and parses content as a sequence of tokens, and as a hierarchical data tree mapper that reads data into a node structure for dynamic modification without predefined classes. The project provides a structured mapping process to bind data to objects, allowing for the transformation of complex Java objects into data formats and the reconstruction of objects from those formats. It supports custom data format mappi

    Allows adjusting deserialization behavior, such as ignoring unknown properties or coercing empty strings to null.

    Javahacktoberfestjacksonjackson-databind
    Ver en GitHub↗3,729
  1. Home
  2. Data & Databases
  3. Table Data Processing
  4. Stream Ingestion

Explorar subetiquetas

  • Polymorphic Stream ValidationValidation and dispatching of multi-type event streams into specific tables using discriminated unions. **Distinct from Stream Ingestion:** Focuses on the structural validation and routing of polymorphic events, whereas Stream Ingestion is the general act of writing streams to tables.
  • Rate-Limited Stream WritesMaintaining a persistent connection to continuously ingest data from multiple tables while capping the ingestion rate. **Distinct from Stream Ingestion:** Distinct from Stream Ingestion: adds rate limiting to the stream ingestion process, not just general stream writing.
  • Stream-to-Online Store Ingestion1 sub-etiquetaWriting feature values from a stream source to the online store for low-latency serving using user-managed ingestion jobs. **Distinct from Stream Ingestion:** Distinct from Stream Ingestion: focuses on writing streaming data specifically to an online feature store, not general tabular ingestion.