Search Results for "data quality"
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Showing 1136 open source projects for "data quality"
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DQO Data Quality Operations Center
Data Quality Operations Center
DQO is an DataOps friendly data quality monitoring tool with customizable data quality checks and data quality dashboards. DQO comes with around 100 predefined data quality checks which helps you monitor the quality of your data. Table and column-level checks which allows writing your own SQL queries. Daily and monthly date partition testing. Data segmentation by up to 9 different data streams. ... -
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Synthetic Data Generator
SDG is a specialized framework
Synthetic Data Generator is an open-source framework designed to generate high-quality synthetic tabular datasets that replicate the statistical characteristics of real data while avoiding privacy risks. The platform enables developers and data scientists to create artificial datasets that preserve important relationships between variables without containing sensitive personal information. -
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FFmpeg Quality Metrics
Calculate quality metrics with FFmpeg (SSIM, PSNR, VMAF, VIF)
FFmpeg Quality Metrics is a Python-based tool that evaluates video quality by calculating objective metrics using FFmpeg. It supports widely used metrics such as PSNR, SSIM, VIF, MSAD, and VMAF, enabling detailed comparison between reference and distorted video files. The tool outputs both per-frame data and aggregated statistics like averages and standard deviation, making it useful for research, encoding optimization, and benchmarking. -
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Synthetic Data Kit
Tool for generating high quality Synthetic datasets
Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. -
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Data Contract CLI
Enforce Data Contracts
Data Contract CLI is an open-source command-line tool and Python library for creating, validating, testing, importing, exporting, and enforcing data contracts. It uses YAML-based contract files to define the structure, meaning, quality rules, service levels, and connection details for a data product. The tool can connect to real data sources and check whether the actual dataset matches the schema, constraints, and quality expectations described in the contract. ... -
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Data-Juicer
Data processing for and with foundation models
Data-Juicer is an open-source data processing and augmentation framework designed to enhance the quality and diversity of datasets for machine learning tasks. It includes a modular pipeline for scalable data transformation. -
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! ... -
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Qualitis
Qualitis is a one-stop data quality management platform
Qualitis is a data quality management platform that supports quality verification, notification, and management for various datasource. It is used to solve various data quality problems caused by data processing. Based on Spring Boot, Qualitis submits quality model task to Linkis platform. It provides functions such as data quality model construction, data quality model execution, data quality verification, reports of data quality generation and so on. ... -
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Book2_Beauty-of-Data-Visualization
Machine Learning, Criticism and Correction
...By combining theory with hands-on plotting exercises, the book helps readers build both analytical and presentation skills. Overall, it is intended as a foundational guide for anyone seeking to produce professional-quality data visualizations. -
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DataQualityDashboard
A tool to help improve data quality standards in data science
The quality checks were organized according to the Kahn Framework1 which uses a system of categories and contexts that represent strategies for assessing data quality. Using this framework, the Data Quality Dashboard takes a systematic-based approach to running data quality checks. Instead of writing thousands of individual checks, we use “data quality check types”. -
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CSV Lint
CSV Lint plug-in for Notepad++ for syntax highlighting
CSV Lint plug-in for Notepad++ for syntax highlighting, csv validation, automatic column and datatype detecting fixed width datasets, change datetime format, decimal separator, sort data, count unique values, convert to xml, json, sql etc. A plugin for data cleaning and working with messy data files. Use CSV Lint for metadata discovery, technical data validation, and reformatting on tabular data files. It is not meant to be a replacement for spreadsheet programs like Excel or SPSS, but rather it's a quality control tool to examine, verify or polish up a dataset before further processing. -
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ODD Platform
First open-source data discovery and observability platform
...Know the impact of each code change with automatic testing. Enjoy lineage and alerts powered with data quality information. -
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lakeFS
lakeFS - Git-like capabilities for your object storage
...Easily Collaborate on production data with your team. Automate data quality checks within data pipelines. -
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent... -
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Mumble
Mumble is an open-source, low-latency, high quality voice chat
Mumble is an open-source, low-latency, high-quality voice chat software. There are two modules in Mumble; the client (mumble) and the server (murmur). The client works on Windows, Linux, FreeBSD, OpenBSD, and macOS, while the server should work on anything Qt can be installed on. Low-latency and high-quality voice-chat program written on top of Qt and Opus. Administrators appreciate Mumble for being able to self-host and have control over data security and privacy. ... -
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FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. -
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CleanVision
Automatically find issues in image datasets
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset! The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. ... -
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Deequ
Deequ is a library built on top of Apache Spark
...It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments. -
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Easy3D
Efficient library for processing 3D data
Easy3D is a lightweight, easy-to-use, and efficient library for processing and rendering 3D data, implemented in C++ with Python bindings. It is designed for tasks such as 3D modeling, geometry processing, and rendering, emphasizing simplicity and efficiency. Easy3D serves as a valuable tool for research, education, and the development of sophisticated 3D applications, providing a solid foundation for handling 3D data. -
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Gretel Synthetics
Synthetic data generators for structured and unstructured text
Unlock unlimited possibilities with synthetic data. Share, create, and augment data with cutting-edge generative AI. Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports.... -
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AG Grid
The best JavaScript Data Table for building enterprise applications
The performance, feature set and quality of AG Grid have not been seen before in a JavaScript data grid. Many features in AG Grid are unique to AG Grid, and simply put AG Grid into a class of its own, without compromising on quality or performance. Over 600,000 monthly downloads of AG Grid Community and over 50% of the Fortune 500 using AG Grid Enterprise. AG Grid has become the JavaScript Datagrid of choice for Enterprise JavaScript developers. -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. ... -
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pointblank
Data quality assessment and metadata reporting for data frames
With the pointblank package it’s really easy to methodically validate your data whether in the form of data frames or as database tables. On top of the validation toolset, the package gives you the means to provide and keep up-to-date with the information that defines your tables. For table validation, the agent object works with a large collection of simple (yet powerful!) validation functions. We can enable much more sophisticated validation checks by using custom expressions, segmenting... -
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you...
