There are 21 repositories under data-profiling topic.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Always know what to expect from your data.
OpenMetadata is a unified metadata platform for data discovery, data observability, and data governance powered by a central metadata repository, in-depth column level lineage, and seamless team collaboration.
Visualize and compare datasets, target values and associations, with one line of code.
:zap: Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.
Automatically find issues in image datasets and practice data-centric computer vision.
Know your data better!Datavines is Next-gen Data Observability Platform, support metadata manage and data quality.
Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
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.
Databricks framework to validate Data Quality of pySpark DataFrames
Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.
🚕 A spreadsheet-like data preparation web app that works over Optimus (Pandas, Dask, cuDF, Dask-cuDF, Spark and Vaex)
Installer for DataKitchen's Open Source Data Observability Products. Data breaks. Servers break. Your toolchain breaks. Ensure your team is the first to know and the first to solve with visibility across and down your data estate. Save time with simple, fast data quality test generation and execution. Trust your data, tools, and systems end to end.
Papers about training data quality management for ML models.
Swiple enables you to easily observe, understand, validate and improve the quality of your data
a set of scripts to pull meta data and data profiling metrics from relational database systems
Metadata and data identification tool and Python library. Identifies PII, common identifiers, language specific identifiers. Fully customizable and flexible rules
Open-source metadata collector based on ODD Specification
Dataset search engine, discovering data from a variety of sources, profiling it, and allowing advanced queries on the index
Client interface to Cleanlab Studio
Metadata/data identification Java library. Identifies Semantic Type information (e.g. Gender, Age, Color, Country,...). Extensive country/language support. Extensible via user-defined plugins. Comprehensive Profiling support.
A project for exploring how Great Expectations can be used to ensure data quality and validate batches within a data pipeline defined in Airflow.
🔍Your Data Quality Detector / Gain insight into your data and get it ready for use before you start working with it 💡📊🛠💎
Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
A Node.js tool to examine the correctness of Open Data Metadata and build custom dataset profiles
Data cleaning tool.
DISTOD algorithm: Distributed discovery of bidirectional order dependencies