Outils de validation de schémas de données
For un outil pour vérifier la qualité de mes données, the strongest matches are great-expectations/great_expectations (Great Expectations is a dedicated data quality framework that), unionai-oss/pandera (Pandera is a Python library that validates both schema) and tensorflow/data-validation (TensorFlow Data Validation is a library purpose-built for validating). Each is ranked by relevance to your query, popularity and recent activity.
Frameworks et bibliothèques automatisés pour vérifier l'intégrité de la structure des données et appliquer des standards de qualité sur les jeux de données.
Outils de validation de schémas de données
Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
- great-expectations/great_expectations
great-expectations/great_expectations
11,558Voir sur GitHubGreat Expectations is a data quality testing framework and observability platform designed to monitor the reliability of data pipelines. It provides a structured environment for defining, documenting, and automating data quality assertions, allowing teams to validate datasets against expected structure and content before they move through downstream processes. The project distinguishes itself through a declarative domain-specific language that stores quality rules as version-controlled configuration files. It utilizes an execution engine abstraction to translate these high-level assertions in
Great Expectations is a dedicated data quality framework that validates both schema structure and data content through declarative expectations, offers profiling and statistics, and integrates with Pandas/Spark, making it a comprehensive fit for data pipeline validation.
PythonData Quality Reports - unionai-oss/pandera
unionai-oss/pandera
4,382Voir sur GitHubPandera is a data pipeline validation framework and statistical type validation tool. It functions as a library for defining and enforcing schemas on datasets to ensure data quality and consistency, specifically providing validation capabilities for Pandas dataframes. The project includes a schema inference tool that automates setup by analyzing existing dataset samples to generate validation schemas. It also serves as a synthetic data generator, creating artificial datasets based on predefined schemas to verify data-producing functions. The framework covers data engineering quality assuranc
Pandera is a Python library that validates both schema structure and data quality on Pandas DataFrames through user-defined schemas and statistical hypothesis testing, making it a direct fit for data pipeline validation; it covers schema validation and customizable rules but does not integrate with Spark or include explicit anomaly detection or automated documentation generation, which keeps it a solid match rather than a flagship.
PythonCustom Validation RulesData Schema Validation - tensorflow/data-validation
tensorflow/data-validation
Library for exploring and validating machine learning data
TensorFlow Data Validation is a library purpose-built for validating ML data, providing schema inference and validation, statistics computation, and anomaly detection, which directly matches the need for a framework that checks schema structure and data quality rules in data pipelines.
PythonData ValidationData Validation and Anomaly DetectionData Visualization
