makinage
pathway
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makinage | pathway | |
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4 | 3 | |
38 | 1,720 | |
- | 17.5% | |
2.7 | 9.6 | |
about 2 years ago | 3 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
makinage
pathway
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
We understood how streaming databases differ from traditional databases, stream processing engines, conventional analytics databases, or OLAP databases. Now let’s focus on when and why we can use stream data processing frameworks for Python as an alternative to streaming databases. Python is the go-to language for data science and machine learning. There are some stream-processing libraries and frameworks in Python such as Bytewax, Quix, GlassFlow, Pathway. They have been developed to cope with the challenges Python Engineers face with Apache Kafka or Flink since they do not natively support Python.
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How to build a custom GPT enabled full-stack app for real-time data
Maintain a data snapshot to observe variations in sales prices over time, as Pathway provides a built-in feature to compute differences between two alterations.
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Show HN: LLM App – build a realtime LLM app in 30 lines, with no vector database
Pathway (https://github.com/pathwaycom/pathway) is a data processing framework we are developing that unifies stream and batch processing of large datasets. It lets developers concentrate on writing the data processing logic, without worrying about tracking changes to data and updating the results. The same code can then be run on batch data (e.g. during testing) or on real-time data streams (i.e. online query processing)
In the LLM app, Pathway allows concentrating on prompt building and querying the LLM APIs as if the corpus of documents were static, while all updates to it are handled by the framework itself.
What are some alternatives?
river - 🌊 Online machine learning in Python
llm-app - LLM App templates for RAG, knowledge mining, and stream analytics. Ready to run with Docker,⚡in sync with your data sources.
Faust - Python Stream Processing
SimpleCommand - A simple, standardized way to build and use Service Objects (aka Commands) in Ruby
kafka-streams-in-action - Source code for the Kafka Streams in Action Book
Light Service - Series of Actions with an emphasis on simplicity.
kafkaml-anomaly-detection - Project for real-time anomaly detection using Kafka and python
Interactor - Interactor provides a common interface for performing complex user interactions.
tractor - A distributed, structured concurrent runtime for Python (and friends)
pypath - Python module for prior knowledge integration. Builds databases of signaling pathways, enzyme-substrate interactions, complexes, annotations and intercellular communication roles.
kafkatop - Monitor kafka consumer lags and event rates