{"repo":"Desbordante/desbordante-core","free":true,"listed":false,"github":"https://github.com/Desbordante/desbordante-core","clone":"git clone https://github.com/Desbordante/desbordante-core.git","description":"Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.","language":"C++","stars":495,"topics":["data-analytics","data-cleaning","data-cleansing","data-engineering","data-exploration","data-mining","data-profiling","data-science","data-wrangling","data-preprocessing"],"license":"AGPL-3.0","category":"data-pipelines","readme_excerpt":"General Desbordante is a high-performance data profiler that is capable of discovering and validating many different patterns in data using various algorithms. The Discovery task is designed to identify all instances of a specified pattern type of a given dataset. The Validation task is different: it is designed to check whether a specified pattern instance is present in a given dataset. This task not only returns True or False, but it also explains why the instance does not hold (e.g. it can list table rows with conflicting values). For some patterns Desbordante supports a dynamic task variant. The distinguishing feature of dynamic algorithms compared to classic (static) algorithms is that after a result is obtained, the table can be changed and a dynamic algorithm will update the result based just on those changes instead of processing the whole table again. As a result, they can be up to several orders of magnitude faster than classic (static) ones in some situations. The currently supported data patterns are: Exact functional dependencies (discovery and validation) Approximate functional dependencies, with - $g 1$ metric — classic AFDs (discovery and validation) - $\\mu+$ metric (discovery) - $\\tau$ metric (discovery) - $pdep$ metric (discovery) - $\\rho$ metric (discovery) Probabilistic functional dependencies, with PerTuple and PerValue metrics (discovery and validation) Classic soft functional dependencies (with correlations), with $\\rho$ metric (discovery and validation","default_branch":null,"files":null,"tree":[],"storefront":"/r/Desbordante","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Desbordante/desbordante-core/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}