Always know what to expect from your data.
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Updated
Apr 27, 2024 - Python
Always know what to expect from your data.
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Compare tables within or across databases
⚡ Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io
re_data - fix data issues before your users & CEO would discover them 😊
Scalable identity resolution, entity resolution, data mastering and deduplication using ML
ML powered analytics engine for outlier detection and root cause analysis.
The premier open source Data Quality solution
Know your data better!Datavines is Next-gen Data Observability Platform, support metadata manage and data quality.
Library for Semi-Automated Data Science
Open Source Data Quality Monitoring.
Find data quality issues and clean your data in a single line of code with a Scikit-Learn compatible Transformer.
Frontend for the osmcha-django REST API
Possibly the fastest DataFrame-agnostic quality check library in town.
Amora Data Build Tool enables analysts and engineers to transform data on the data warehouse (BigQuery) by writing Amora Models that describe the data schema using Python's "PEP484 - Type Hints" and select statements with SQLAlchemy. Amora is able to transform Python code into SQL data transformation jobs that run inside the warehouse.
Make simple storing test results and visualisation of these in a BI dashboard
Datailot-cli is the command line interface for accessing the AI teammate for engineers to ensure best practices in their SQL and dbt projects.
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