In the context of the active development of automation and artificial intelligence, the task of effectively collecting,
Cleaning and transforming data becomes critical. Most solutions only close
separate stages of this process, requiring complex integration and support.
SFAP (Seek · Filter · Adapt · Publish) is an open-source project in Python,
which offers a holistic and extensible approach to processing data at all stages of its lifecycle:
from searching for sources to publishing the finished result.
What is SFAP
SFAP is an asynchronous framework built around a clear concept of a data processing pipeline.
Each stage is logically separate and can be independently expanded or replaced.
The project is based on the Chain of Responsibility architectural pattern, which provides:
- pipeline configuration flexibility;
- simple testing of individual stages;
- scalability for high loads;
- clean separation of responsibilities between components.
Main stages of the pipeline
Seek – data search
At this stage, data sources are discovered: web pages, APIs, file storages
or other information flows. SFAP makes it easy to connect new sources without changing
the rest of the system.
Filter – filtering
Filtering is designed to remove noise: irrelevant content, duplicates, technical elements
and low quality data. This is critical for subsequent processing steps.
Adapt – adaptation and processing
The adaptation stage is responsible for data transformation: normalization, structuring,
semantic processing and integration with AI models (including generative ones).
Publish – publication
At the final stage, the data is published in the target format: databases, APIs, files, external services
or content platforms. SFAP does not limit how the result is delivered.
Key features of the project
- Asynchronous architecture based on asyncio
- Modularity and extensibility
- Support for complex processing pipelines
- Ready for integration with AI/LLM solutions
- Suitable for highly loaded systems
Practical use cases
- Aggregation and analysis of news sources
- Preparing datasets for machine learning
- Automated content pipeline
- Cleansing and normalizing large data streams
- Integration of data from heterogeneous sources
Getting started with SFAP
All you need to get started is:
- Clone the project repository;
- Install Python dependencies;
- Define your own pipeline steps;
- Start an asynchronous data processing process.
The project is easily adapted to specific business tasks and can grow with the system,
without turning into a monolith.
Conclusion
SFAP is not just a parser or data collector, but a full-fledged framework for building
modern data-pipeline systems. It is suitable for developers and teams who care about
scalable, architecturally clean, and data-ready.
The project source code is available on GitHub:
https://github.com/demensdeum/SFAP
