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60 repository-uri

Awesome GitHub RepositoriesData Ingestion

Tools for loading, parsing, and preparing unstructured data from diverse external sources for downstream processing.

Distinguishing note: Focuses on the preparation of raw data for AI consumption, distinct from general ETL or database migration tools.

Explore 60 awesome GitHub repositories matching data & databases · Data Ingestion. Refine with filters or upvote what's useful.

Awesome Data Ingestion GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • imartinez/privategptAvatar imartinez

    imartinez/privateGPT

    57,281Vezi pe GitHub↗

    PrivateGPT is a private AI document assistant and local knowledge base manager designed for querying private files and documents using retrieval-augmented generation. It functions as a local language model application and API gateway, allowing users to obtain cited answers from unstructured data without sending information to external servers. The system differentiates itself by acting as a tool integrator that connects language models to external functions, including web search, tabular data analysis, and custom action extensions. It provides a standardized API layer that allows local infere

    Processes and prepares raw unstructured data from local files for consumption by AI models.

    Python
    Vezi pe GitHub↗57,281
  • run-llama/llama_indexAvatar run-llama

    run-llama/llama_index

    50,306Vezi pe GitHub↗

    LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, i

    LlamaIndex loads data from external sources, parses documents into manageable chunks, and processes them through ingestion pipelines for downstream indexing.

    Pythonagentsapplicationdata
    Vezi pe GitHub↗50,306
  • automattic/mongooseAvatar Automattic

    Automattic/mongoose

    27,479Vezi pe GitHub↗

    Mongoose is an object data modeling library and framework for Node.js that maps application objects to MongoDB documents. It functions as a document mapper and schema validator, ensuring consistent data types and validation rules for records stored in MongoDB. The project provides a system for defining structured schemas to model application data, including the ability to create hierarchical data structures through nested schema composition. It implements a middleware-based hook system that allows for the interception and modification of data states during the lifecycle of database operations

    Functions as an object data modeling library that maps application objects to MongoDB documents using structured schemas.

    JavaScript
    Vezi pe GitHub↗27,479
  • supermemoryai/supermemoryAvatar supermemoryai

    supermemoryai/supermemory

    27,334Vezi pe GitHub↗

    Supermemory is an artificial intelligence memory management platform designed to provide autonomous agents with persistent, long-term knowledge bases. It functions as a centralized repository that synchronizes multimodal data, enabling agents to maintain context and historical information across complex, multi-session workflows. By serving as a knowledge graph engine and vector database orchestrator, the platform ensures that information remains accessible and relevant for automated tasks. The system distinguishes itself through its hybrid indexing approach, which combines vector similarity s

    Automates the extraction and processing of diverse media formats into structured data for AI consumption.

    TypeScriptcloudflare-kvcloudflare-pagescloudflare-workers
    Vezi pe GitHub↗27,334
  • sirupsen/logrusAvatar sirupsen

    sirupsen/logrus

    25,736Vezi pe GitHub↗

    Logrus is a structured logging library for Go that produces machine-readable output using key-value pairs and JSON formatting. It serves as a pluggable logging framework providing a thread-safe event logger with configurable mutex locking to manage concurrent writes across multiple goroutines. The project distinguishes itself through a pluggable hook system that routes log entries to external services or custom destinations. It also features a contextual logger capable of attaching persistent metadata and request-scoped fields to entries to improve traceability. The framework covers broad ob

    Sends log entries to local or remote syslog daemons using standard network protocols and priority levels.

    Gogologginglogrus
    Vezi pe GitHub↗25,736
  • cinnamon/kotaemonAvatar Cinnamon

    Cinnamon/kotaemon

    25,139Vezi pe 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

    Ingests and parses diverse file formats into structured text for downstream processing and AI consumption.

    Pythonchatbotllmsopen-source
    Vezi pe GitHub↗25,139
  • taosdata/tdengineAvatar taosdata

    taosdata/TDengine

    24,734Vezi pe GitHub↗

    TDengine is a distributed time-series database designed for the high-speed ingestion, compression, and retrieval of timestamped metrics and sensor data. It functions as a SQL-compatible analytics engine, allowing users to perform complex operations on massive volumes of time-ordered information using standard relational syntax. The platform is built to serve as a backend foundation for industrial IoT environments, managing real-time data streams and device metadata through a cluster-based architecture. The system distinguishes itself through a distributed sharding architecture that uses consi

    Integrates third-party tools to ingest and process data from various sources.

    Cbigdatacloud-nativecluster
    Vezi pe GitHub↗24,734
  • winstonjs/winstonAvatar winstonjs

    winstonjs/winston

    24,478Vezi pe GitHub↗

    Winston is a versatile logging library for Node.js designed to record system events and metadata. It functions as a multi-transport log manager that routes data to various destinations and a structured log formatter that transforms entries into JSON or plain text. The project is distinguished by its pluggable transport architecture, which decouples the logging interface from delivery mechanisms. This allows for the creation of custom transport extensions and the use of hierarchical logger instances to inherit configurations while attaching persistent metadata to downstream messages. The libr

    Persists log entries into MongoDB collections with support for capped collections and TTL expiration.

    JavaScript
    Vezi pe GitHub↗24,478
  • vectordotdev/vectorAvatar vectordotdev

    vectordotdev/vector

    22,071Vezi pe 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

    Consumes log, metric, and trace data from Kafka topics using configurable consumer groups and secure authentication.

    Rusteventsforwarderhacktoberfest
    Vezi pe GitHub↗22,071
  • prefecthq/prefectAvatar PrefectHQ

    PrefectHQ/prefect

    21,640Vezi pe GitHub↗

    Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep

    Manages secure connections to data environments using personal access tokens or service principal credentials.

    Pythonautomationdatadata-engineering
    Vezi pe GitHub↗21,640
  • 1panel-dev/maxkbAvatar 1Panel-dev

    1Panel-dev/MaxKB

    21,337Vezi pe GitHub↗

    MaxKB is a self-hosted retrieval-augmented generation platform designed to connect internal document repositories with large language models. It functions as an enterprise knowledge management system that enables organizations to query private data through a conversational interface, providing automated responses based on uploaded files and internal business information. The platform distinguishes itself by normalizing diverse data sources into a unified index, which is then processed through chunking and vector-based retrieval to ensure context-aware results. It manages session state and pro

    Parses and normalizes diverse file formats and web content into a unified representation for AI processing.

    Pythonagentagentic-aichatbot
    Vezi pe GitHub↗21,337
  • probelabs/goreplayAvatar probelabs

    probelabs/goreplay

    19,286Vezi pe GitHub↗

    Goreplay is an HTTP traffic mirroring tool designed to capture live network traffic from production environments and replay it into test systems for validation. It includes a specialized Kubernetes traffic capturer that operates as a daemonset to mirror traffic from specific pods using label selectors and namespace filters, alongside a TCP traffic recorder for intercepting raw network packets. The project features a Kafka traffic pipeline for streaming captured payloads to topics or ingesting messages for playback, and an HTTP request transformer to mask sensitive data or rewrite headers and

    Consumes messages from Kafka topics and transforms them into HTTP payloads for playback into a target environment.

    Go
    Vezi pe GitHub↗19,286
  • camel-ai/camelAvatar camel-ai

    camel-ai/camel

    17,253Vezi pe GitHub↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Loads, parses, and prepares unstructured data from diverse external sources for downstream agent processing.

    Pythonagentai-societiesartificial-intelligence
    Vezi pe GitHub↗17,253
  • victoriametrics/victoriametricsAvatar VictoriaMetrics

    VictoriaMetrics/VictoriaMetrics

    16,343Vezi pe GitHub↗

    VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term storage and analysis of metric, log, and trace data. It functions as a unified backend for monitoring ecosystems, offering full compatibility with industry-standard protocols and query languages. The system is built to handle massive data volumes through a distributed architecture that supports horizontal scaling and efficient data lifecycle management. The platform distinguishes itself through a storage engine that utilizes consistent hashing for data sharding and log-struct

    Supports ingestion of log messages over standard TCP or UDP syslog protocols.

    Godatabasegrafanagraphite
    Vezi pe GitHub↗16,343
  • dagster-io/dagsterAvatar dagster-io

    dagster-io/dagster

    14,974Vezi pe GitHub↗

    Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative modeling and version-controlled code. It functions as a workflow engine that treats data assets as first-class primitives, allowing teams to define, schedule, and monitor complex pipelines while maintaining clear visibility into lineage, dependencies, and data quality. The platform distinguishes itself by using a code-as-configuration framework that enables standard software engineering practices, such as unit testing and local mocking, to be applied directly to data workflows.

    Provides modular tools to ingest and transform raw information from various sources into usable assets for downstream analysis.

    Pythonanalyticsdagsterdata-engineering
    Vezi pe GitHub↗14,974
  • modsetter/surfsenseAvatar MODSetter

    MODSetter/SurfSense

    14,816Vezi pe GitHub↗

    SurfSense is a self-hosted platform designed for building retrieval-augmented generation pipelines and managing private knowledge bases. It functions as a containerized research stack that allows users to index diverse data sources and query them using language models, ensuring that all information retrieval is grounded in specific source citations. The platform distinguishes itself through its modular architecture, which supports the integration of custom tools and diverse language models via a unified abstraction layer. It facilitates secure, collaborative research environments by implement

    Processes and indexes diverse file formats and web sources to build a searchable repository of information.

    Pythonaceternity-uiagentagents
    Vezi pe GitHub↗14,816
  • unstructured-io/unstructuredAvatar Unstructured-IO

    Unstructured-IO/unstructured

    14,019Vezi pe GitHub↗

    Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

    Connects to cloud storage containers to retrieve and process files, supporting recursive directory traversal and enterprise authentication.

    HTMLdata-pipelinesdeep-learningdocument-image-analysis
    Vezi pe GitHub↗14,019
  • fluent/fluentdAvatar fluent

    fluent/fluentd

    13,554Vezi pe GitHub↗

    Fluentd is a unified logging layer and distributed event router that collects, parses, and routes log data from diverse sources to various storage backends. It functions as a log forwarding agent and pipeline orchestrator, transforming raw unstructured log strings into formatted objects using structured log parsing. The project utilizes a plugin-based pipeline architecture to route data through independent input, filter, and output stages. It differentiates itself through tag-based event routing, which uses regular expression patterns to direct specific data streams to their intended destinat

    Processes system log messages following RFC3164 and RFC5424 formatting standards.

    Ruby
    Vezi pe GitHub↗13,554
  • ibm/saramaAvatar IBM

    IBM/sarama

    12,490Vezi pe GitHub↗

    Sarama is an Apache Kafka Go client library that provides native support for the Kafka protocol. It includes a protocol client for managing offsets and timestamps, a producer implementation for sending messages, and a consumer group coordinator to balance workloads across multiple instances. The library enables high throughput data streaming through concurrent message production and maintains strict partition ordering during network retries. It supports secure communication with Kafka brokers using certificate-based encryption to protect data traffic. The project covers a broad range of dist

    Serves as a comprehensive Go client library for producing and consuming messages from Kafka clusters.

    Gogokafkakafka-client
    Vezi pe GitHub↗12,490
  • jd-opensource/joyagent-jdgenieAvatar jd-opensource

    jd-opensource/joyagent-jdgenie

    11,350Vezi pe GitHub↗

    Joyagent-jdgenie is an automated data orchestrator designed to centralize the retrieval and processing of information from disparate remote sources. It functions as a framework for building repeatable data pipelines that fetch, clean, and normalize raw input into consistent, structured formats. The system utilizes a schema-driven engine to apply validation rules and structural templates to incoming data, ensuring compatibility across enterprise systems. By employing configuration-based workflow definitions, it allows for the orchestration of modular tasks into automated execution flows, separ

    Maps diverse remote provider protocols into a unified internal data structure for consistent processing.

    Java
    Vezi pe GitHub↗11,350
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  3. Data Ingestion

Explorează sub-etichetele

  • Confluence Connectors1 sub-tagIntegrations for retrieving and processing content from collaboration platforms. **Distinct from Data Ingestion:** Focuses on Confluence-specific API integration for document ingestion, distinct from general data ingestion.
  • Couchbase Connectors3 sub-tag-uriIntegrations for retrieving documents from database instances for processing. **Distinct from Data Ingestion:** Focuses on Couchbase-specific database ingestion, distinct from general data ingestion tools.
  • Database-to-Kafka ConnectorsConnectors specifically designed to stream database change events into Kafka topics. **Distinct from Kafka Connectors:** Focuses on database change capture as the source, unlike general Kafka connectors that might ingest unstructured documents.
  • Databricks Connectors2 sub-tag-uriIntegrations for retrieving unstructured files from data volumes. **Distinct from Data Ingestion:** Focuses on Databricks-specific file ingestion, distinct from general data ingestion.
  • DiagnosticIngestion and normalization of standardized diagnostic formats like SARIF for analysis tools. **Distinct from Data Ingestion:** Focuses on static analysis diagnostics (SARIF/XML) rather than general database or AI data ingestion.
  • Jira Connectors2 sub-tag-uriIntegrations for retrieving project management data and attachments from issue tracking systems. **Distinct from Data Ingestion:** Focuses on Jira-specific data ingestion, distinct from general data ingestion tools.
  • Kafka Connectors3 sub-tag-uriIntegrations for streaming unstructured documents from message queues. **Distinct from Data Ingestion:** Focuses on Kafka-specific stream ingestion, distinct from general data ingestion.
  • MongoDB Connectors1 sub-tagIntegrations for retrieving documents and collections from database instances. **Distinct from Data Ingestion:** Focuses on MongoDB-specific database ingestion, distinct from general data ingestion.
  • Private Network IngestionSecure data retrieval over private network links. **Distinct from Data Ingestion:** Distinct from general data ingestion: focuses on private network connectivity for secure data sources.
  • Sensor2 sub-tag-uriHigh-bandwidth ingestion of raw data from physical sensors for real-time processing. **Distinct from Data Ingestion:** Focuses on the high-speed capture of sensor data over Ethernet, distinct from general unstructured data parsing
  • Syslog Ingestion3 sub-tag-uriMechanisms for receiving and processing log messages via TCP or UDP protocols. **Distinct from Data Ingestion:** Distinct from Data Ingestion: focuses specifically on the Syslog protocol implementation.
  • Syslog-ng Daemon ManagersConfigures and monitors the syslog-ng daemon through a web interface. **Distinct from Syslog Ingestion:** Distinct from Syslog Ingestion: focuses on configuration and monitoring of the syslog-ng daemon itself, not just receiving logs.
  • VASTConnectors for retrieving and processing unstructured files from storage systems. **Distinct from Data Ingestion:** Specific connector for VAST storage systems, distinct from general data ingestion.
  • Zendesk2 sub-tag-uriConnectors for retrieving and processing support tickets and help center articles. **Distinct from Data Ingestion:** Specific connector for Zendesk support instances, distinct from general data ingestion.