Reverse Dependencies of great-expectations

The following projects have a declared dependency on great-expectations:

• acryl-datahub — A CLI to work with DataHub metadata
• airflow-provider-great-expectations — An Apache Airflow provider for Great Expectations
• airflow-provider-great-expectations-cta — An Apache Airflow provider for Great Expectations
• aqueduct-sdk — Python SDK for Aqueduct
• argilla-plugins — 🔌 Open-source plugins for with practical features for Argilla using listeners.
• ascend-great-expectations-gcs — A wrapper around Great Expectations for building validation components in Ascend.io platform
• autorad — Radiomics-related modules for extraction and experimenting
• cdpdev-datahub — A CLI to work with DataHub metadata
• centraal-dataframework — Utilidades para interactuar con Azure Datalake.
• cooper-pair — A small library that provides programmatic access to Superconductive's GraphQL API.
• ctodd-python-lib-data-science — Python utilities used for practicing data science and engineering
• dagster-ge — Package for GE-specific Dagster framework op and resource components.
• data-kalite — Data Quality for PySpark Pipelines
• dbcat — Tokern Data Catalog
• dolbaram — no summary
• dq-suite-amsterdam — Wrapper for Great Expectations to fit the requirements of the Gemeente Amsterdam.
• dq-tool — no summary
• easy-expectations — A package that simplifies usage of Great Expectations tool for Data Validation.
• easy-ge — A package that simplifies usage of Great Expectations tool for Data Validation.
• Feast — Python SDK for Feast
• feast-spark — Spark extensions for Feast
• feastmo — Python SDK for Feast
• flytekitplugins-great-expectations — Great Expectations Plugin for Flytekit
• gdp-time-series — no summary
• ge-custom-slack-renderer — Complement library to customize Great Expectations Slack notifications
• grater-expectations — A grated application of Great Expectations to create greater Expectations
• great-expectations-cloud — Great Expectations Cloud
• great-expectations-ethical-ai-expectations — A collection of Expectations to validate for degradation, bias, and related Ethical Data concerns with Great Expectations.
• great-expectations-experimental — Always know what to expect from your data.
• great-expectations-geospatial-expectations — A collection of Expectations to validate Geospatial data with Great Expectations.
• great-expectations-semantic-types-expectations — A collection of Expectations to validate Semantically Typed Data with Great Expectations.
• great-expectations-time-series-expectations — Expectations for detecting trends, seasonality, outliers, etc. in time series data.
• great-expectations-zipcode-expectations — A collection of Expectations to validate zipcode data with Great Expectations.
• GXKent — An awesome package for GXKent.
• idg-metadata-client — Ingestion Framework for OpenMetadata
• kada-gx-plugin — kada-gx-plugin generates validation results in a format for loading into the K Platform.
• kedro-expectations — Combine Kedro data science pipelines with Great Expectations data validations.
• kedro-great — Kedro Great makes integrating Great Expectations with Kedro easy!
• lakehouse-engine — A Spark framework serving as the engine for several lakehouse algorithms and data flows.
• mimesis-and-ge — A Python module for mimesis and Great Expectations
• mk-feature-store — Python SDK for Feast
• nuna-sql-tools — Nuna Sql Tools contains utilities to create and manipulate schemas and sql statements.
• odd-great-expectations — OpenDataDiscovery Action for Great Expectations
• openlineage-airflow — OpenLineage integration with Airflow
• openlineage-integration-common — OpenLineage common python library for integrations
• openmetadata-ingestion — Ingestion Framework for OpenMetadata
• piicatcher — Find PII data in databases
• pingpong-datahub — A CLI to work with DataHub metadata
• prefect-great-expectations — Prefect Collection containing integrations for interacting with Great Expectations
• pyretailscience — Retail Data Science Tools
• schematicpy — Package for biomedical data model and metadata ingress management
• sidradataquality — A SDK for Sidra data quality validations
• stacks-data — A suite of utilities to support data engineering workloads within an Ensono Stacks data platform.
• store-validations-oracle — Used with Great_Expectations to store validation results in an Oracle Database.
• streamduo — streamduo.com API
• structured-profiling — A Python library to check for data quality and automatically generate data tests.
• thisbeatest — A CLI to work with DataHub metadata
• tidal-algorithmic-mixes — common transformers used by the tidal personalization team.
• tidal-per-transformers — common transformers used by the tidal personalization team.