How Great Expectations Works
Great Expectations is a data validation framework designed to help teams define, test, and document expectations about their data. At its core, it provides a structured way to create unit tests for data quality, referred to as Expectations. The framework is built on the principle of collaboration, offering a common language for teams to articulate data quality requirements in an intuitive way.
The framework operates by validating data against these predefined Expectations. When a dataset is processed, Great Expectations evaluates whether the data meets the specified criteria. Results are automatically documented, generating reports that detail which Expectations passed or failed. This documentation serves as a living record of data quality, preserving institutional knowledge and ensuring transparency across teams.
It supports connectors for data warehouses, Spark, and pandas, enabling validation across diverse environments. The framework is designed to be extensible, allowing users to define custom Expectations or integrate with additional data sources as needed.
Users initialize this context by importing the library and creating an instance, which then manages data sources, validation suites, and results. The Data Context also facilitates the generation of documentation, often referred to as Data Docs, which provides a visual and interactive interface for reviewing validation outcomes.
The core library handles the validation logic, while additional modules provide support for specific integrations, such as Spark or SQL databases.
The framework is open-source and adheres to the Apache 2.0 license, making it freely accessible for use, modification, and distribution. It is maintained by a community-driven approach, with contributions welcomed through pull requests, issues, and discussions on platforms like GitHub and Discourse. This collaborative model ensures continuous improvement and adaptation to evolving data quality challenges.
The installation process is straightforward, involving a single pip command to install the core library. Once installed, users can begin defining Expectations and integrating them into their data pipelines.
The framework prioritizes support through structured channels, including GitHub issues, Discourse forums, and Slack communities. This ensures that users can seek assistance, report bugs, or propose new features in an organized manner. The community also maintains a code of conduct to foster a respectful and inclusive environment for collaboration.
Key Features and Capabilities
Great Expectations introduces the concept of Expectations as the foundation of its validation framework. These are declarative tests that define rules for data quality, such as ensuring that a column contains only numeric values, that a set of values adheres to a specific regex pattern, or that a column’s values fall within a defined range.
One of the standout features of Great Expectations is its ability to generate automated documentation for validation results.
Great Expectations supports a wide range of data sources and integrations, making it versatile for different use cases. The framework includes native connectors for popular data warehouses, such as Snowflake, BigQuery, and Redshift, as well as support for Spark and pandas. This compatibility ensures that users can validate data regardless of where it resides or how it is processed. Additionally, Great Expectations provides experimental support for newer Python versions, such as 3.14, through environment variables, allowing early adopters to test compatibility.
For example, in release 1.23.1, Great Expectations introduced improvements to how regex patterns are evaluated in Spark, ensuring consistency with pandas and SQL behaviors. This release also fixed a long-standing bug where validators built on the same datasource incorrectly shared batch results, now correctly isolating each validator’s data.
For instance, the SQL execution engine is now cached across calls, reducing overhead and improving efficiency. Additionally, the framework handles configuration files with explicit UTF-8 encoding, ensuring compatibility across different locales and preventing issues with non-ASCII characters.
The framework also supports custom actions and integrations, allowing users to extend its functionality beyond out-of-the-box capabilities. While Great Expectations provides general guidance for creating custom actions, it does not offer troubleshooting for domain-specific logic.
Great Expectations is designed to work with modern orchestration tools, with official support for Apache Airflow version 2.9.0 and later. While Airflow is the primary orchestrator supported, the framework’s Python-based architecture ensures compatibility with other orchestration platforms that execute Python code.
The framework’s compatibility reference outlines supported versions of Python, data sources, and dependencies. As of version 1.23.2, Great Expectations supports Python 3.10 through 3.13, with experimental support for 3.14. It also maintains compatibility with a range of databases, including Oracle, PostgreSQL, and SQL Server, while providing tested integrations for tools like Trino and Databricks. This broad support ensures that users can deploy Great Expectations in diverse technical environments.
Use Cases
By defining Expectations for schema consistency, data types, and value ranges, teams can quickly identify and address issues before they propagate through the pipeline.
By validating input data against predefined criteria, teams can ensure that models are trained on clean, consistent, and representative data. This is particularly important for avoiding biases or inaccuracies that could arise from poor-quality data.
The automated documentation feature further simplifies the process of reporting and verifying data integrity for external reviews.
The framework’s support for Spark and SQL databases also makes it suitable for large-scale data processing workflows, where validation must scale alongside data volume.
The framework’s extensibility also allows for custom validations that align with specific governance policies or business rules.
The framework’s support for Airflow and other orchestration tools enables teams to embed data validation directly into their existing workflows. This integration ensures that validation is not treated as an afterthought but is instead a core part of the data pipeline. By automating validation checks, teams can catch issues early and respond more quickly to data quality incidents, improving overall operational efficiency.