Search Results for "data quality"
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Showing 1144 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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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 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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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. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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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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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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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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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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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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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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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Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. ... -
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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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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
...Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others). -
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ydata-profiling
Create HTML profiling reports from pandas DataFrame objects
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. -
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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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FastQC
A quality control analysis tool for high throughput sequencing data
FastQC is a quality control analysis tool designed to spot potential problems in high throughput sequencing datasets. Its goal is to provide a simple way by which to check the quality of raw sequence data coming from high throughput sequencing pipelines. It does this by running a modular set of analyses on one or more raw sequence files in fastq or bam format. -
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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... -
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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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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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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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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. ...
