Great Expectations

Great Expectations is an open-source data validation framework that helps teams ensure data quality by defining, documenting, and testing expectations about datasets. It allows users to create assertions about data characteristics such as column types, value ranges, null percentages, and statistical distributions, then automatically validates data against these expectations throughout the pipeline. Great Expectations provides comprehensive documentation, data profiling capabilities, and integration with popular data tools, making it easier to catch data issues early and maintain trust in data-driven systems.

Also known as: GX, data validation framework, data testing platform, expectation suite

Comparisons

• Great Expectations vs. Data Quality: Data quality is the overall concept of reliable, accurate data, while Great Expectations provides specific tools and frameworks to measure and enforce quality standards.
• Great Expectations vs. Data Cleaning: Data cleaning fixes existing issues in datasets, whereas Great Expectations proactively validates data to catch quality problems before they propagate downstream.
• Great Expectations vs. Unit Testing: Traditional unit tests validate code behavior, while Great Expectations validates data characteristics and quality throughout pipeline execution.

Pros

• Proactive quality control: Catches data issues early in pipelines before they impact downstream analysis, reporting, or machine learning models.
• Comprehensive documentation: Automatically generates data documentation and quality reports that help teams understand dataset characteristics and trends.
• Pipeline integration: Works seamlessly with orchestration tools like Apache Airflow and Dagster to validate data at each pipeline stage.
• Flexible validation: Supports custom expectations and statistical tests that can adapt to specific business requirements and data patterns.

Cons

• Initial setup complexity: Defining comprehensive expectation suites requires significant upfront investment and domain knowledge about expected data characteristics.
• Performance overhead: Running extensive validation checks can slow down data pipelines, especially with large datasets or complex statistical tests.
• Maintenance burden: Expectations need regular updates as data sources, business requirements, and data characteristics evolve over time.

Example

A company collecting product data using web scraper APIs implements Great Expectations to validate scraped data quality. They define expectations that product prices must be positive numbers, product names cannot be null, and availability status must match specific values. When their scraping process encounters unexpected data formats or missing fields due to website changes, Great Expectations automatically flags these data quality issues and prevents corrupted data from reaching their analytics and pricing algorithms.