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Great Expectations
Define and validate data quality expectations — catch bad data before it causes incidents.
0Free
Data Quality
Overview
Great Expectations (GX) is an open-source Python library for defining, documenting, and validating data quality expectations on DataFrames, SQL tables, and files — catching data issues before they reach downstream consumers.
Key Features
- Expectation library with 50+ built-in data quality checks
- Data Docs for auto-generated HTML documentation of expectations and results
- Profiler to infer initial expectations from sample data automatically
- Checkpoints for running validation suites in CI/CD or pipeline runs
- Integration with Spark, pandas, Snowflake, BigQuery, and Redshift
- GX Cloud for managed expectation suites and historical validation tracking
Real-World Workflows
Validate incoming data before processing
- 1Define an Expectation Suite for the raw source table (schema, nulls, ranges)
- 2Add a GX Checkpoint step at the start of your Airflow or Dagster DAG
- 3On validation failure, fail the pipeline and alert the data team
- 4Review failure details in auto-generated Data Docs HTML report
Document data contracts between teams
- 1Use GX Profiler to auto-infer expectations from a sample dataset
- 2Refine and add custom expectations for business rules
- 3Publish Data Docs as a static site for data consumers to browse
- 4Run validations on every new data delivery to enforce the contract
Getting Started
# Install Great Expectations pip install great_expectations # Initialize a GX project gx init # Create an Expectation Suite gx suite new # Run a Checkpoint gx checkpoint run my_checkpoint
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