[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-485":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":20,"hasPages":20,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":39,"readmeContent":40,"aiSummary":41,"trendingCount":16,"starSnapshotCount":16,"syncStatus":42,"lastSyncTime":43,"discoverSource":44},485,"pathway","pathwaycom\u002Fpathway","pathwaycom","Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.","https:\u002F\u002Fpathway.com",null,"Python",63034,1679,116,33,0,9,44.68,"Other",false,"main",[23,24,25,26,27,28,29,30,31,32,5,33,34,35,36,37,38],"batch-processing","data-analytics","data-pipelines","data-processing","dataflow","etl","etl-framework","iot-analytics","kafka","machine-learning-algorithms","python","real-time","rust","stream-processing","streaming","time-series-analysis","2026-06-12 02:00:14","\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpathway.com\u002F\">\n    \u003Cpicture>\n      \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"https:\u002F\u002Fpathway.com\u002Flogo-light.svg\">\n      \u003Cimg src=\"https:\u002F\u002Fpathway.com\u002Flogo-dark.svg\">\n    \u003C\u002Fpicture>\n  \u003C\u002Fa>\n  \u003Cbr \u002F>\u003Cbr \u002F>\n  \u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F10388\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F10388\" alt=\"pathwaycom%2Fpathway | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n  \u003Cbr \u002F>\u003Cbr \u002F>\n\u003C\u002Fdiv>\n\u003Cp align=\"center\">\n        \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Factions\u002Fworkflows\u002Fubuntu_test.yml\">\n        \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Factions\u002Fworkflows\u002Fubuntu_test.yml\u002Fbadge.svg\" alt=\"ubuntu\"\u002F>\n        \u003Cbr>\n        \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Factions\u002Fworkflows\u002Frelease.yml\">\n        \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Factions\u002Fworkflows\u002Frelease.yml\u002Fbadge.svg\" alt=\"Last release\"\u002F>\u003C\u002Fa>\n        \u003Ca href=\"https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fpathway\">\u003Cimg src=\"https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fpathway.svg\" alt=\"PyPI version\" height=\"18\">\u003C\u002Fa>\n        \u003Ca href=\"https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fpathway\">\u003Cimg src=\"https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fpathway\" alt=\"PyPI downloads\" height=\"18\">\u003C\u002Fa>\n        \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Fblob\u002Fmain\u002FLICENSE.txt\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-BSL-green\" alt=\"License: BSL\"\u002F>\u003C\u002Fa>\n      \u003Cbr>\n        \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002Fpathway\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1042405378304004156?logo=discord\"\n            alt=\"chat on Discord\">\u003C\u002Fa>\n        \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fintent\u002Ffollow?screen_name=pathway_com\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fpathwaycom\"\n            alt=\"follow on Twitter\">\u003C\u002Fa>\n        \u003Ca href=\"https:\u002F\u002Flinkedin.com\u002Fcompany\u002Fpathway\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpathway-0077B5?style=social&logo=linkedin\" alt=\"follow on LinkedIn\">\u003C\u002Fa>\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdylanhogg\u002Fawesome-python\u002Fblob\u002Fmain\u002FREADME.md\">\n      \u003Cimg src=\"https:\u002F\u002Fawesome.re\u002Fbadge.svg\" alt=\"Awesome Python\">\u003C\u002Fa>\n      \u003Ca href=\"https:\u002F\u002Fgurubase.io\u002Fg\u002Fpathway\">\n      \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGurubase-Ask%20Pathway%20Guru-006BFF\" alt=\"Pathway Guru\">\u003C\u002Fa>\n    \u003Cbr>\n    \u003Ca href=\"#getting-started\">Getting Started\u003C\u002Fa> |\n    \u003Ca href=\"#deployment\">Deployment\u003C\u002Fa> |\n    \u003Ca href=\"#resources\">Documentation and Support\u003C\u002Fa> |\n    \u003Ca href=\"https:\u002F\u002Fpathway.com\u002Fblog\u002F\">Blog\u003C\u002Fa> |\n    \u003Ca href=\"#license\">License\u003C\u002Fa>\n\n  \n\u003C\u002Fp>\n\n# Pathway\u003Ca id=\"pathway\"> Live Data Framework\u003C\u002Fa>\n\n[Pathway](https:\u002F\u002Fpathway.com) is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.\n\nPathway comes with an **easy-to-use Python API**, allowing you to seamlessly integrate your favorite Python ML libraries.\nPathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**.\nThe same code can be used for local development, CI\u002FCD tests, running batch jobs, handling stream replays, and processing data streams.\n\nPathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation.\nYour Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations.\nAll the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**.\n\nYou can install Pathway with pip:\n```\npip install -U pathway\n```\n\nFor any questions, you will find the community and team behind the project [on Discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fpathway).\n\n## Use-cases and templates\n\nReady to see what Pathway can do?\n\n[Try one of our easy-to-run examples](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates)!\n\nAvailable in both notebook and docker formats, these ready-to-launch examples can be launched in just a few clicks. Pick one and start your hands-on experience with Pathway today!\n\n### Event processing and real-time analytics pipelines\nWith its unified engine for batch and streaming and its full Python compatibility, Pathway makes data processing as easy as possible. It's the ideal solution for a wide range of data processing pipelines, including:\n\n- [Showcase: Real-time ETL.](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Fkafka-etl)\n- [Showcase: Event-driven pipelines with alerting.](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Frealtime-log-monitoring)\n- [Showcase: Realtime analytics.](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Flinear_regression_with_kafka)\n- [Docs: Switch from batch to streaming.](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Fuser-guide\u002Fconnecting-to-data\u002Fswitch-from-batch-to-streaming)\n\n\n\n### AI Pipelines\n\nPathway provides dedicated LLM tooling to build live LLM and RAG pipelines. Wrappers for most common LLM services and utilities are included, making working with LLMs and RAGs pipelines incredibly easy. Check out our [LLM xpack documentation](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Fuser-guide\u002Fllm-xpack\u002Foverview).\n\nDon't hesitate to try one of our runnable examples featuring LLM tooling.\nYou can find such examples [here](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Fuser-guide\u002Fllm-xpack\u002Fllm-examples).\n\n  - [Template: Unstructured data to SQL on-the-fly.](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Funstructured-to-structured)\n  - [Template: Private RAG with Ollama and Mistral AI](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Fprivate-rag-ollama-mistral)\n  - [Template: Adaptive RAG](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Fadaptive-rag)\n  - [Template: Multimodal RAG with gpt-4o](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Ftemplates\u002Fmultimodal-rag)\n\n## Features\n\n- **A wide range of connectors**: Pathway comes with connectors that connect to external data sources such as Kafka, GDrive, PostgreSQL, or SharePoint. Its Airbyte connector allows you to connect to more than 300 different data sources. If the connector you want is not available, you can build your own custom connector using Pathway Python connector.\n- **Stateless and stateful transformations**: Pathway supports stateful transformations such as joins, windowing, and sorting. It provides many transformations directly implemented in Rust. In addition to the provided transformation, you can use any Python function. You can implement your own or you can use any Python library to process your data.\n- **Persistence**: Pathway provides persistence to save the state of the computation. This allows you to restart your pipeline after an update or a crash. Your pipelines are in good hands with Pathway!\n- **Consistency**: Pathway handles the time for you, making sure all your computations are consistent. In particular, Pathway manages late and out-of-order points by updating its results whenever new (or late, in this case) data points come into the system. The free version of Pathway gives the \"at least once\" consistency while the enterprise version provides the \"exactly once\" consistency.\n- **Scalable Rust engine**: with Pathway Rust engine, you are free from the usual limits imposed by Python. You can easily do multithreading, multiprocessing, and distributed computations.\n- **LLM helpers**: Pathway provides an LLM extension with all the utilities to integrate LLMs with your data pipelines (LLM wrappers, parsers, embedders, splitters), including an in-memory real-time Vector Index, and integrations with LLamaIndex and LangChain. You can quickly build and deploy RAG applications with your live documents.\n\n\n## Getting started\u003Ca id=\"getting-started\">\u003C\u002Fa>\n\n### Installation\u003Ca id=\"installation\">\u003C\u002Fa>\n\nPathway requires Python 3.10 or above.\n\nYou can install the current release of Pathway using `pip`:\n\n```\n$ pip install -U pathway\n```\n\n⚠️ Pathway is available on MacOS and Linux. Users of other systems should run Pathway on a Virtual Machine.\n\n\n### Example: computing the sum of positive values in real time.\u003Ca id=\"example\">\u003C\u002Fa>\n\n```python\nimport pathway as pw\n\n# Define the schema of your data (Optional)\nclass InputSchema(pw.Schema):\n  value: int\n\n# Connect to your data using connectors\ninput_table = pw.io.csv.read(\n  \".\u002Finput\u002F\",\n  schema=InputSchema\n)\n\n#Define your operations on the data\nfiltered_table = input_table.filter(input_table.value>=0)\nresult_table = filtered_table.reduce(\n  sum_value = pw.reducers.sum(filtered_table.value)\n)\n\n# Load your results to external systems\npw.io.jsonlines.write(result_table, \"output.jsonl\")\n\n# Run the computation\npw.run()\n```\n\nRun Pathway [in Google Colab](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1aBIJ2HCng-YEUOMrr0qtj0NeZMEyRz55?usp=sharing).\n\nYou can find more examples [here](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Ftree\u002Fmain\u002Fexamples).\n\n\n## Deployment\u003Ca id=\"deployment\">\u003C\u002Fa>\n\n### Locally\u003Ca id=\"running-pathway-locally\">\u003C\u002Fa>\n\nTo use Pathway, you only need to import it:\n\n```python\nimport pathway as pw\n```\n\nNow, you can easily create your processing pipeline, and let Pathway handle the updates. Once your pipeline is created, you can launch the computation on streaming data with a one-line command:\n\n```python\npw.run()\n```\n\nYou can then run your Pathway project (say, `main.py`) just like a normal Python script: `$ python main.py`.\nPathway comes with a monitoring dashboard that allows you to keep track of the number of messages sent by each connector and the latency of the system. The dashboard also includes log messages. \n\n\u003Cimg src=\"https:\u002F\u002Fd14l3brkh44201.cloudfront.net\u002Fpathway-dashboard.png\" width=\"1326\" alt=\"Pathway dashboard\"\u002F>\n\nAlternatively, you can use the pathway'ish version:\n\n```\n$ pathway spawn python main.py\n```\n\nPathway natively supports multithreading.\nTo launch your application with 3 threads, you can do as follows:\n```\n$ pathway spawn --threads 3 python main.py\n```\n\nTo jumpstart a Pathway project, you can use our [cookiecutter template](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fcookiecutter-pathway).\n\n\n### Docker\u003Ca id=\"docker\">\u003C\u002Fa>\n\nYou can easily run Pathway using docker.\n\n#### Pathway image\n\nYou can use the [Pathway docker image](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fpathwaycom\u002Fpathway), using a Dockerfile:\n\n```dockerfile\nFROM pathwaycom\u002Fpathway:latest\n\nWORKDIR \u002Fapp\n\nCOPY requirements.txt .\u002F\nRUN pip install --no-cache-dir -r requirements.txt\n\nCOPY . .\n\nCMD [ \"python\", \".\u002Fyour-script.py\" ]\n```\n\nYou can then build and run the Docker image:\n\n```console\ndocker build -t my-pathway-app .\ndocker run -it --rm --name my-pathway-app my-pathway-app\n```\n\n#### Run a single Python script\n\nWhen dealing with single-file projects, creating a full-fledged `Dockerfile`\nmight seem unnecessary. In such scenarios, you can execute a\nPython script directly using the Pathway Docker image. For example:\n\n```console\ndocker run -it --rm --name my-pathway-app -v \"$PWD\":\u002Fapp pathwaycom\u002Fpathway:latest python my-pathway-app.py\n```\n\n#### Python docker image\n\nYou can also use a standard Python image and install Pathway using pip with a Dockerfile:\n\n```dockerfile\nFROM --platform=linux\u002Fx86_64 python:3.10\n\nRUN pip install -U pathway\nCOPY .\u002Fpathway-script.py pathway-script.py\n\nCMD [\"python\", \"-u\", \"pathway-script.py\"]\n```\n\n### Kubernetes and cloud\u003Ca id=\"k8s\">\u003C\u002Fa>\n\nDocker containers are ideally suited for deployment on the cloud with Kubernetes.\nIf you want to scale your Pathway application, you may be interested in our Pathway for Enterprise.\nPathway for Enterprise is specially tailored towards end-to-end data processing and real time intelligent analytics.\nIt scales using distributed computing on the cloud and supports distributed Kubernetes deployment, with external persistence setup.\n\nYou can easily deploy Pathway using services like Render: see [how to deploy Pathway in a few clicks](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Fuser-guide\u002Fdeployment\u002Frender-deploy\u002F).\n\nIf you are interested, don't hesitate to [contact us](mailto:contact@pathway.com) to learn more.\n\n## Performance\u003Ca id=\"performance\">\u003C\u002Fa>\n\nPathway is made to outperform state-of-the-art technologies designed for streaming and batch data processing tasks, including: Flink, Spark, and Kafka Streaming. It also makes it possible to implement a lot of algorithms\u002FUDF's in streaming mode which are not readily supported by other streaming frameworks (especially: temporal joins, iterative graph algorithms, machine learning routines).\n\nIf you are curious, here are [some benchmarks to play with](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway-benchmarks).\n\n\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway-benchmarks\u002Fraw\u002Fmain\u002Fimages\u002Fbm-wordcount-lineplot.png\" width=\"1326\" alt=\"WordCount Graph\"\u002F>\n\n## Documentation and Support\u003Ca id=\"resources\">\u003C\u002Fa>\n\nThe entire documentation of Pathway is available at [pathway.com\u002Fdevelopers\u002F](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Fuser-guide\u002Fintroduction\u002Fwelcome), including the [API Docs](https:\u002F\u002Fpathway.com\u002Fdevelopers\u002Fapi-docs\u002Fpathway).\n\nIf you have any question, don't hesitate to [open an issue on GitHub](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Fissues), join us on [Discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fpathway), or send us an email at [contact@pathway.com](mailto:contact@pathway.com).\n\n\n\n## 🤝 Featured Collaborations & Integrations\n\nWe build cutting-edge data processing pipelines and co-promote solutions that push the boundaries of what's possible with Python and streaming data.\nMeet the people helping us shape the future of data engineering.\n\n\u003Cdiv align=\"center\">\n\n| Project | Description |\n| ------------ | ----------- |\n| [Databento](https:\u002F\u002Fdatabento.com\u002Fblog\u002Foption-greeks) | A simpler, faster way to get market data. |\n| [LangChain](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fintegrations\u002Fvectorstores\u002Fpathway) | LangChain is the platform for agent engineering. |\n| [LlamaIndex](https:\u002F\u002Fdevelopers.llamaindex.ai\u002Fpython\u002Fexamples\u002Fretrievers\u002Fpathway_retriever\u002F) | The developer-trusted framework for building context-aware AI agents. |\n| [MinIO](https:\u002F\u002Fwww.min.io\u002F) | MinIO is a high-performance, S3 compatible object store, open sourced under GNU AGPLv3 license. |\n| [PaddleOCR](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddleOCR) | PaddleOCR is an industry-leading, production-ready OCR and document AI engine, offering end-to-end solutions from text extraction to intelligent document understanding. |\n| [Redpanda](https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Freplace-kafka-redpanda-data-analysis-streaming) | Build, operate, and govern streaming and AI applications without the complexity of Kafka. |\n\u003C\u002Fdiv>\n\n\n## License\u003Ca id=\"license\">\u003C\u002Fa>\n\nPathway is distributed on a [BSL 1.1 License](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fpathway\u002Fblob\u002Fmain\u002FLICENSE.txt) which allows for unlimited non-commercial use, as well as use of the Pathway package [for most commercial purposes](https:\u002F\u002Fpathway.com\u002Flicense\u002F), free of charge. Code in this repository automatically converts to Open Source (Apache 2.0 License) after 4 years. Some [public repos](https:\u002F\u002Fgithub.com\u002Fpathwaycom) which are complementary to this one (examples, libraries, connectors, etc.) are licensed as Open Source, under the MIT license.\n\n\n## Contribution guidelines\u003Ca id=\"contribution-guidelines\">\u003C\u002Fa>\n\nIf you develop a library or connector which you would like to integrate with this repo, we suggest releasing it first as a separate repo on a MIT\u002FApache 2.0 license. \n\nFor all concerns regarding core Pathway functionalities, Issues are encouraged. For further information, don't hesitate to engage with Pathway's [Discord community](https:\u002F\u002Fdiscord.gg\u002Fpathway).\n","Pathway 是一个用于流处理、实时分析、LLM 管道和 RAG 的 Python ETL 框架。其核心功能包括易用的 Python API，能够无缝集成多种 Python 机器学习库，并且代码既适用于开发环境也适合生产环境，支持批处理和流数据处理。Pathway 适用于需要高效处理实时数据流、进行复杂数据分析以及构建机器学习流水线的场景，尤其在物联网分析、时间序列分析等领域表现出色。",2,"2026-06-11 02:36:28","top_all"]