In the context of the active development of automation and artificial intelligence, the task of effectively collecting,
\nCleaning and transforming data becomes critical. Most solutions only close
\nseparate stages of this process, requiring complex integration and support.<\/p>\n
SFAP (Seek \u00b7 Filter \u00b7 Adapt \u00b7 Publish) is an open-source project in Python,
\nwhich offers a holistic and extensible approach to processing data at all stages of its lifecycle:
\nfrom searching for sources to publishing the finished result.<\/p>\n
SFAP is an asynchronous framework built around a clear concept of a data processing pipeline.
\nEach stage is logically separate and can be independently expanded or replaced.<\/p>\n
The project is based on the Chain of Responsibility<\/em> architectural pattern, which provides:<\/p>\n At this stage, data sources are discovered: web pages, APIs, file storages Filtering is designed to remove noise: irrelevant content, duplicates, technical elements The adaptation stage is responsible for data transformation: normalization, structuring, At the final stage, the data is published in the target format: databases, APIs, files, external services All you need to get started is:<\/p>\n The project is easily adapted to specific business tasks and can grow with the system, SFAP is not just a parser or data collector, but a full-fledged framework for building 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 \u00b7 Filter \u00b7 Adapt \u00b7 Publish) is an open-source project in Python, which offers a holisticContinue reading “SFAP: a modular framework for modern data acquisition and processing”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[61],"tags":[],"class_list":["post-4283","post","type-post","status-publish","format-standard","hentry","category-techie","entry"],"translation":{"provider":"WPGlobus","version":"3.0.2","language":"en","enabled_languages":["en","ru","zh","de","fr","ja","pt"],"languages":{"en":{"title":true,"content":true,"excerpt":false},"ru":{"title":true,"content":true,"excerpt":false},"zh":{"title":true,"content":true,"excerpt":false},"de":{"title":true,"content":true,"excerpt":false},"fr":{"title":true,"content":true,"excerpt":false},"ja":{"title":true,"content":true,"excerpt":false},"pt":{"title":true,"content":true,"excerpt":false}}},"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/posts\/4283","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/comments?post=4283"}],"version-history":[{"count":4,"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/posts\/4283\/revisions"}],"predecessor-version":[{"id":4287,"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/posts\/4283\/revisions\/4287"}],"wp:attachment":[{"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/media?parent=4283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/categories?post=4283"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demensdeum.com\/blog\/wp-json\/wp\/v2\/tags?post=4283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Main stages of the pipeline<\/h2>\n
Seek – data search<\/h3>\n
\nor other information flows. SFAP makes it easy to connect new sources without changing
\nthe rest of the system.<\/p>\nFilter – filtering<\/h3>\n
\nand low quality data. This is critical for subsequent processing steps.<\/p>\nAdapt – adaptation and processing<\/h3>\n
\nsemantic processing and integration with AI models (including generative ones).<\/p>\nPublish – publication<\/h3>\n
\nor content platforms. SFAP does not limit how the result is delivered.<\/p>\nKey features of the project<\/h2>\n
\n
Practical use cases<\/h2>\n
\n
Getting started with SFAP<\/h2>\n
\n
\nwithout turning into a monolith.<\/p>\nConclusion<\/h2>\n
\nmodern data-pipeline systems. It is suitable for developers and teams who care about
\nscalable, architecturally clean, and data-ready.
\nThe project source code is available on GitHub:
\nhttps:\/\/github.com\/demensdeum\/SFAP<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"