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39 रिपॉजिटरी

Awesome GitHub RepositoriesData Ingestion Pipelines

Workflows that automate the extraction, transformation, and loading of data from diverse sources into processing systems.

Explore 39 awesome GitHub repositories matching data & databases · Data Ingestion Pipelines. Refine with filters or upvote what's useful.

Awesome Data Ingestion Pipelines GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • vinta/awesome-pythonvinta का अवतार

    vinta/awesome-python

    303,207GitHub पर देखें↗

    यह प्रोजेक्ट एक व्यापक, समुदाय-क्यूरेटेड निर्देशिका है जो पायथन सॉफ्टवेयर लाइब्रेरी, फ्रेमवर्क और टूल के विशाल परिदृश्य को व्यवस्थित करती है। यह पारिस्थितिकी तंत्र नेविगेशन की सुविधा के लिए और पूरे सॉफ्टवेयर विकास लाइफसाइकिल में डेवलपर खोज को गति देने के लिए डिज़ाइन किया गया एक केंद्रीकृत नॉलेज बेस है। निर्देशिका तकनीकी डोमेन द्वारा वर्गीकृत संसाधनों का एक संरचित इंडेक्स प्रदान करके खुद को अलग करती है, जो मूलभूत विकास यूटिलिटी से लेकर विशेष इंजीनियरिंग क्षेत्रों तक फैला हुआ है। यह आर्टिफिशियल इंटेलिजेंस, डेटा साइंस, वेब डेवलपमेंट और इंफ्रास्ट्रक्चर प्रबंधन सहित उच्च-स्तरीय क्षमताओं को कवर करती है, जिससे डेवलपर्स विशिष्ट तकनीकी चुनौतियों के लिए परीक्षित समाधानों की पहचान कर सकते हैं। प्रोजेक्ट में निर्भरता प्रबंधन, स्टेटिक कोड विश्लेषण और स्वचालित परीक्षण के लिए टूल सहित क्षमताओं का एक व्यापक क्षेत्र शामिल है। यह पर्सिस्टेंट डेटा स्टोरेज, क्लाउड इंफ्रास्ट्रक्चर ऑर्केस्ट्रेशन और इंटरफ़ेस डेवलपमेंट के लिए संसाधनों को भी सूचीबद्ध करता है, जो जटिल सॉफ्टवेयर सिस्टम बनाने और बनाए रखने के लिए एक एकीकृत संदर्भ प्रदान करता है।

    Facilitates the movement of information by bridging disparate sources with robust extraction and loading mechanisms.

    Pythonawesomecollectionspython
    GitHub पर देखें↗303,207
  • tensorflow/tensorflowtensorflow का अवतार

    tensorflow/tensorflow

    195,697GitHub पर देखें↗

    TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr

    Organizes reusable components into high-throughput workflows to extract, transform, and load data for training.

    C++deep-learningdeep-neural-networksdistributed
    GitHub पर देखें↗195,697
  • bregman-arie/devops-exercisesbregman-arie का अवतार

    bregman-arie/devops-exercises

    82,879GitHub पर देखें↗

    This project is a comprehensive educational curriculum designed to build proficiency across modern infrastructure, cloud-native technologies, and systems administration. It functions as a reference library and interview preparation resource, offering a structured collection of conceptual questions, practical coding challenges, and hands-on scenarios that cover the full spectrum of software delivery and operational workflows. The repository distinguishes itself through a modular, domain-specific structure that links instructional problem statements with verified implementation examples. By emp

    Describes the use of input, filter, and output plugins to transform data streams within ingestion pipelines.

    Pythonansibleawsazure
    GitHub पर देखें↗82,879
  • openbb-finance/openbbOpenBB-finance का अवतार

    OpenBB-finance/OpenBB

    69,583GitHub पर देखें↗

    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-

    Automates research workflows by extracting and transforming financial information directly through terminal commands.

    Pythonaicryptoderivatives
    GitHub पर देखें↗69,583
  • mintplex-labs/anything-llmMintplex-Labs का अवतार

    Mintplex-Labs/anything-llm

    61,663GitHub पर देखें↗

    This platform serves as a comprehensive environment for managing private language models, document knowledge bases, and automated agent workflows within secure local infrastructure. It functions as a document-aware workspace that enables users to ingest diverse file formats into searchable repositories, ensuring that all data processing and model inference remain within private, local environments to maintain data sovereignty. The system distinguishes itself through a modular agentic engine that allows for the definition of custom skills and external tool execution. By utilizing a multi-model

    Automates the ingestion of raw documents into structured text and metadata to enable efficient semantic search.

    JavaScriptai-agentscustom-ai-agentsdeepseek
    GitHub पर देखें↗61,663
  • zylon-ai/private-gptzylon-ai का अवतार

    zylon-ai/private-gpt

    57,278GitHub पर देखें↗

    This project is a privacy-first backend service designed to facilitate retrieval-augmented generation by processing local documents into searchable vector representations. It provides a modular architecture that allows users to ingest diverse file formats, manage document metadata, and perform semantic searches to provide context-aware responses for chat and completion requests. The system distinguishes itself through a database-agnostic abstraction layer that supports various storage backends, ranging from local disk storage to enterprise-grade vector databases. It offers flexible deployment

    Parses raw files into structured text chunks and metadata to facilitate semantic search and data retrieval.

    Python
    GitHub पर देखें↗57,278
  • ngosang/trackerslistngosang का अवतार

    ngosang/trackerslist

    54,183GitHub पर देखें↗

    This project is a curated, community-driven registry of public BitTorrent trackers designed to facilitate peer-to-peer file sharing. It serves as a centralized resource for network endpoints that coordinate connections between distributed clients, helping users discover and maintain reliable infrastructure for decentralized communication protocols. The repository distinguishes itself through a fully automated orchestration pipeline that ensures the lists remain current and accurate. Every day, background tasks perform distributed health monitoring to verify connectivity and filter out unrespo

    Processes a continuous stream of data through automated pipelines to keep information accurate for downstream consumers.

    bittorrentbittorrent-trackerbittorrent-trackers
    GitHub पर देखें↗54,183
  • stedolan/jqstedolan का अवतार

    stedolan/jq

    34,932GitHub पर देखें↗

    jq is a command-line JSON processor and data transformer. It provides a functional query language used to slice, filter, map, and transform structured JSON data directly within a terminal. The utility functions as a data transformer that reshapes JSON input into different structures or formats based on declarative logic. This allows for the extraction and analysis of structured data from sources such as API responses and system logs.

    Implements efficient chunked reading from standard input to process large JSON files without exhausting memory.

    C
    GitHub पर देखें↗34,932
  • apache/flinkapache का अवतार

    apache/flink

    26,086GitHub पर देखें↗

    Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve

    Automates the extraction, transformation, and loading of data between brokers, databases, and cloud storage.

    Java
    GitHub पर देखें↗26,086
  • redis/go-redisredis का अवतार

    redis/go-redis

    22,159GitHub पर देखें↗

    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

    Provides workflows that automate the extraction, transformation, and loading of data.

    Gogogolangredis
    GitHub पर देखें↗22,159
  • vectordotdev/vectorvectordotdev का अवतार

    vectordotdev/vector

    22,071GitHub पर देखें↗

    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

    Supports reading raw byte streams from standard input for parsing into structured observability events.

    Rusteventsforwarderhacktoberfest
    GitHub पर देखें↗22,071
  • elastic/kibanaelastic का अवतार

    elastic/kibana

    21,148GitHub पर देखें↗

    Kibana is a browser-based data exploration and visualization platform designed for interacting with information stored in distributed search engines. It serves as a centralized interface for analyzing structured and unstructured data, enabling users to build custom dashboards, generate interactive charts, and map complex datasets to uncover trends and actionable insights. Beyond visualization, the platform functions as a comprehensive management console for infrastructure operations. It provides tools for configuring security policies, managing data indices, and monitoring system health. The

    Processes and normalizes incoming data through multi-stage pipelines before storage.

    TypeScriptdashboardselasticsearchhacktoberfest
    GitHub पर देखें↗21,148
  • topoteretes/cogneetopoteretes का अवतार

    topoteretes/cognee

    17,850GitHub पर देखें↗

    Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions. The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orche

    Automates the extraction and transformation of unstructured data into structured knowledge representations.

    Pythonaiai-agentsai-memory
    GitHub पर देखें↗17,850
  • elastic/logstashelastic का अवतार

    elastic/logstash

    14,884GitHub पर देखें↗

    Logstash is a JVM-based event processor and extract, transform, load system designed for log data processing pipelines. It functions as a plugin-based data ingestor that collects, transforms, and delivers logs and event data from multiple sources to various destinations. The system utilizes a modular architecture of interchangeable input, filter, and output components to handle real-time data ingestion and enterprise log aggregation. Users can extend the pipeline's functionality by developing custom plugins to support unique data sources or specific transformation logic. The platform covers

    Implements a complete ETL workflow that automates the extraction, transformation, and loading of event data.

    Java
    GitHub पर देखें↗14,884
  • hkuds/deepcodeHKUDS का अवतार

    HKUDS/DeepCode

    14,539GitHub पर देखें↗

    DeepCode is an agentic development framework designed to orchestrate autonomous AI agents for software engineering tasks. It functions as a multi-agent workflow orchestrator that translates natural language requirements into functional codebases by coordinating specialized agents for architectural planning, intent analysis, and implementation. The platform integrates multiple language models to power these automated routines, providing a unified environment for complex development projects. The system distinguishes itself through its ability to transform academic research papers into executab

    Parses large technical research papers into structured text chunks to maintain semantic integrity for language model processing.

    Pythonagentic-codingllm-agent
    GitHub पर देखें↗14,539
  • druid-io/druiddruid-io का अवतार

    druid-io/druid

    14,020GitHub पर देखें↗

    Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume

    Integrates a lambda-style pipeline combining real-time streaming and batch processing for immediate data consistency.

    Java
    GitHub पर देखें↗14,020
  • unstructured-io/unstructuredUnstructured-IO का अवतार

    Unstructured-IO/unstructured

    14,019GitHub पर देखें↗

    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 to retrieve documents and process them into structured formats for downstream AI applications.

    HTMLdata-pipelinesdeep-learningdocument-image-analysis
    GitHub पर देखें↗14,019
  • aws/aws-cdkaws का अवतार

    aws/aws-cdk

    12,817GitHub पर देखें↗

    The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

    Configures automated workflows to ingest, process, and extract insights from data sources.

    TypeScriptawscloud-infrastructurehacktoberfest
    GitHub पर देखें↗12,817
  • harelba/qharelba का अवतार

    harelba/q

    10,353GitHub पर देखें↗

    q is a command-line utility for the processing, filtering, and aggregation of tabular text and database files using standard SQL syntax. It functions as a query engine that treats CSV and TSV files, as well as standard input, as relational database tables. The tool distinguishes itself by providing a persistent cache layer that stores processed tabular data in a binary format to accelerate repeated queries on large datasets. It also maps individual filenames or stream identifiers to relational table names, enabling SQL joins across disparate text files. The project covers a broad range of da

    Integrates with the shell by treating the stdin stream as a queryable table within the SQL execution context.

    Pythonclicommand-linecommand-line-tool
    GitHub पर देखें↗10,353
  • gwuhaolin/livegogwuhaolin का अवतार

    gwuhaolin/livego

    10,179GitHub पर देखें↗

    livego is a live streaming server written in Go that receives live video streams and broadcasts them to viewers in real time. It functions as a multi-protocol video gateway, serving as a backend for ingesting video and redistributing it through RTMP, HLS, and HTTP-FLV protocols. The server features dynamic protocol transmuxing to convert ingested streams into different formats for device compatibility and low-latency playback. It provides secure stream access and ingestion by validating unique channel keys and using security tokens. The system includes capabilities for encrypted stream deliv

    Supports the ingestion of incoming video streams via standard protocols to prepare them for redistribution.

    Goflashflvgolang
    GitHub पर देखें↗10,179
पिछला12अगला
  1. Home
  2. Data & Databases
  3. Data Processing Pipelines
  4. Data Ingestion Pipelines

सब-टैग एक्सप्लोर करें

  • Document Ingestion PipelinesWorkflows that parse raw files into structured text chunks and metadata to facilitate semantic search and data retrieval.
  • Lambda ArchitecturesHybrid ingestion pipelines that combine batch and real-time streaming processing for consistency and low latency. **Distinct from Data Ingestion Pipelines:** Distinct from general ingestion pipelines by specifically combining streaming and batch layers into a single architectural pattern
  • Standard Input IngestionMechanisms for reading raw byte streams from standard input for pipeline processing. **Distinct from Data Ingestion Pipelines:** Distinct from general ingestion pipelines: focuses specifically on standard input stream consumption.