51 dependents
| Package | Description | Downloads/month |
|---|---|---|
| Always know what to expect from your data. | 573K | |
| Great Expectations Airflow operator | 191K | |
| 🧙 Build, run, and manage data pipelines for integrating and transforming data. | 84K | |
| Great Expectations Cloud | 74K | |
| The Context Platform for your Data and AI Stack | 58K | |
| An orchestration platform for the development, production, and observation of da... | 41K | |
| ZZIngestions is a powerful Python package that uses PySpark to transform data in... | 28K | |
| Python - Java/Scala API for the Hopsworks feature store | 15K | |
| Wrapper for Great Expectations to fit the requirements of the Gemeente Amsterdam... | 11K | |
| Great Expectations Plugin for Flytekit | 10K | |
| CDC Data Hub Lifecycle, Analysis and Visualization Accelerator (LAVA) makes buil... | 5K | |
| 3K | ||
| Package for biomedical data model and metadata ingress management | 1K | |
| Data Contract Validator and Pipeline Guardian — generic, open-source | 1K | |
| Combine Kedro data science pipelines with Great Expectations data validations. | 1K | |
| Enrich Developer Kit | 1K | |
| A Python library to check for data quality and automatically generate data tests... | 847 | |
| Expose Great Expectations data-quality checks via MCP | 646 | |
| Prefect integrations for interacting with Great Expectations | 597 | |
| OpenDataDiscovery Action for Great Expectations | 564 | |
| A Python script suite that generates interface files based on the given interfac... | 539 | |
| Run your dbt tests using Great Expectations as the execution engine! | 408 | |
| data operations related code - abstractions | 405 | |
| HDF Data Quality Framework for PySpark DataFrames using Great Expectations | 396 | |
| Inlaw is an orchestration layer on top of the greatexpectations python library. | 341 | |
| A grated application of Great Expectations to create greater Expectations | 326 | |
| A SDK for Sidra data quality validations | 322 | |
| Vision drift monitoring with Great Expectations + Evidently + MLflow (Milestone ... | 314 | |
| this is an example of a simple data pipeline | 293 | |
| A utterly useless package that imports everything for you. Now with top 1000 PyP... | 247 | |
| Used with Great_Expectations to store validation results in an Oracle Database. | 241 | |
| kada-gx-plugin generates validation results in a format for loading into the K P... | 200 | |
| A data loading and transformation engine for data lakehouses | 198 | |
| FabricDataGuard is a Python library that simplifies data quality checks in Micro... | 176 | |
| Utilidades para interactuar con Azure Datalake. | 168 | |
| 153 | ||
| Data Quality for PySpark Pipelines | 137 | |
| this is a template for a python package | 122 | |
| Python utilities used for practicing data science and engineering | 110 | |
| Cooper Pair is a Python library to simplify programmatic access to the Allotrope... | 85 | |
| Simplified Data Validation and Quality Testing with Great Expectations | 82 | |
| A package that simplifies usage of Great Expectations tool for Data Validation. | 80 | |
| Complement library to customize Great Expectations Slack notifications | 80 | |
| 78 | ||
| Dynamic data quality validation for Snowflake using Great Expectations | 78 | |
| A Python module for mimesis and Great Expectations | 65 | |
| Package for biomedical data model and metadata ingress management | 63 | |
| Otomatik analiz, düzeltme ve raporlama için agent tabanlı Python kütüphanesi. | 60 | |
| Mage is a tool for building and deploying data pipelines. | 47 | |
| ETL Functions Packages from mage.ai | 37 |
