Search Results for "processing" - Page 2
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Showing 33 open source projects for "processing"
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Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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Insidias is a next-generation intrusion detection engine, with a focus on speed, efficiency, advanced pattern matching, parallel-processing, modularity, and expandability.
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Provide an LDAP client API for Python, C wrapper module around OpenLDAP 2.x with an object-oriented API, several additional modules for processing LDIF, LDAP URLs and LDAP schema information
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This project provides a fast distributed system for image processing, written in Python. It aim is to be used as service to PHP, Perl and Python application servers.
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csart - Clever-Search-And-Replace-Text Search, find and replace text in named files or recursive in all directories (-r). If choosen, strings are only replaced if another key-string occures in the line (-w)
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Say goodbye to broken revenue funnels and poor customer experiencesLeanData is a Demand Management solution that supports all go-to-market strategies such as account-based sales development, geo-based territories, and more. LeanData features a visual, intuitive workflow native to Salesforce that enables users to view their entire lead flow in one interface. LeanData allows users to access the drag-and-drop feature to route their leads. LeanData also features an algorithms match that uses multiple fields in Salesforce.
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StreamMine is a distributed event processing (streaming) infrastructure. You can create low-latency, fault-tolerant stream processing functionality with any stream-oriented operators that can be implemented in Python.
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A Python-based macro processing tool to extend C-like languages with DSLs. MP reduces the amount of work necessary to extend C by extracting the relevant blocks to process and calling user-supplied Python functions to handle the macro.
