Great Expectations is an open-source Python library that checks whether your data matches the rules you define \u2014 like “this column has no nulls” or “values fall between 0 and 100.” You install it with pip install great_expectations, point it at a dataset, write expectations describing what “good” data looks like, and run validations that flag rows or columns that break those rules before they reach a report or model.<\/p>\n\n\n\n
Great Expectations is a data validation framework that lets you write explicit, testable rules about what your data should look like \u2014 then checks real data against those rules automatically.<\/p>\n\n\n\n
When we added Great Expectations to a client’s weekly reporting pipeline in late 2025, a vendor feed had quietly started sending revenue figures as strings instead of floats. The validation suite caught it on the very first run, before it ever reached the dashboard.<\/p>\n\n\n\n
Pro Tip:<\/strong> Think of expectations as unit tests, but for data instead of code. The same instinct that makes you write assert result == 5 applies here \u2014 you’re just asserting things about a dataset.<\/p>\n\n\n\n Bad data is expensive precisely because it’s quiet. A null where there shouldn’t be one, or a date format that silently changes, often doesn’t throw an error \u2014 it just produces wrong numbers downstream, and nobody notices until a report looks off.<\/p>\n\n\n\n Great Expectations matters because it turns these silent failures into loud, early ones:<\/p>\n\n\n\n Warning:<\/strong> Great Expectations checks data quality \u2014 it doesn’t fix bad data automatically. You still need a plan for what happens when a validation fails, whether that’s stopping the pipeline or routing bad rows elsewhere.<\/p>\n\n\n\n Data Point:<\/strong> Many data teams adopt Great Expectations specifically because Data Docs gives analysts and stakeholders a readable report, not just a terminal pass\/fail message. [HUMAN EDITOR: replace with a sourced adoption statistic if available, or remove this line]<\/p>\n\n\n\n Great Expectations runs on Python 3.9 or later, and it’s a heavier install than most validation libraries because it ships with reporting tools.<\/p>\n\n\n\n Best Practice:<\/strong> Install Great Expectations in its own virtual environment. Its dependency list is large, and mixing it into a project with conflicting package versions is a common source of install errors.<\/p>\n\n\n\n A “Data Context” is the project-level configuration that tells Great Expectations where your data lives and where to store validation results.<\/p>\n\n\n\n import great_expectations as gx<\/p>\n\n\n\n context = gx.get_context()<\/p>\n\n\n\n # Connect to a pandas DataFrame as a data source<\/p>\n\n\n\n import pandas as pd<\/p>\n\n\n\n df = pd.read_csv(“orders.csv”)<\/p>\n\n\n\n data_source = context.data_sources.add_pandas(“orders_source”)<\/p>\n\n\n\n data_asset = data_source.add_dataframe_asset(name=”orders”)<\/p>\n\n\n\n This creates a lightweight, file-based project context \u2014 enough to start validating without setting up a database or cloud storage.<\/p>\n\n\n\n The context stores three things: where your data comes from, what expectations you’ve defined, and the history of past validation runs. This is what lets Great Expectations show you trends over time, not just a single pass\/fail snapshot.<\/p>\n\n\n\n Expectations are the actual rules. Here’s a small, realistic set for an orders dataset.<\/p>\n\n\n\n batch_request = data_asset.build_batch_request(dataframe=df)<\/p>\n\n\n\n validator = context.get_validator(batch_request=batch_request)<\/p>\n\n\n\n # Define expectations<\/p>\n\n\n\n validator.expect_column_values_to_not_be_null(“order_id”)<\/p>\n\n\n\n validator.expect_column_values_to_be_between(“order_total”, min_value=0, max_value=100000)<\/p>\n\n\n\n validator.expect_column_values_to_be_in_set(“status”, [“pending”, “shipped”, “delivered”, “cancelled”])<\/p>\n\n\n\n # Save and run<\/p>\n\n\n\n validator.save_expectation_suite(discard_failed_expectations=False)<\/p>\n\n\n\n results = validator.validate()<\/p>\n\n\n\n print(results.success)<\/p>\n\n\n\n If results.success comes back False, Great Expectations tells you exactly which expectation failed and on which rows \u2014 not just that “something” was wrong.<\/p>\n\n\n\n [Suggested visual: Add a screenshot here showing a Data Docs validation results page with a failed expectation highlighted.]<\/p>\n\n\n\n Data Docs is the HTML report Great Expectations generates after each run, and it’s built to be read without writing more code.<\/p>\n\n\n\n This report is often the single artifact that convinces a non-engineering stakeholder that data quality is actually being monitored, rather than assumed.<\/p>\n\n\n\n A validation failure is only useful if something happens because of it. Most teams wire validation into their pipeline so a failure stops the process or triggers an alert.<\/p>\n\n\n\n results = validator.validate()<\/p>\n\n\n\n if not results.success:<\/p>\n\n\n\n raise ValueError(“Data validation failed \u2014 check Data Docs for details”)<\/p>\n\n\n\n Best Practice:<\/strong> Don’t aim for 100% strict validation on day one. Start with the two or three rules that would actually break something downstream \u2014 like a null primary key \u2014 and add more expectations as you learn where your data tends to break.<\/p>\n\n\n\nWhy Does Data Validation Matter?<\/h2>\n\n\n\n
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Great Expectations vs. Other Validation Tools<\/h2>\n\n\n\n
Feature<\/strong><\/th> Great Expectations<\/strong><\/th> Pandera<\/strong><\/th> dbt tests<\/strong><\/th><\/tr><\/thead> Best for<\/td> Standalone Python validation across many data sources<\/td> Schema validation tied closely to pandas\/DataFrames<\/td> SQL-based validation inside a dbt transformation pipeline<\/td><\/tr> Reporting<\/td> Built-in Data Docs (HTML reports)<\/td> No built-in reporting UI<\/td> Test results shown in dbt run logs<\/td><\/tr> Learning curve<\/td> Moderate \u2014 more setup, more configuration options<\/td> Low \u2014 feels like Python type hints<\/td> Low if you already use dbt<\/td><\/tr> Works outside Python pipelines<\/td> Yes, via SQL backends<\/td> Limited<\/td> Yes, but only within dbt projects<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n How to Install Great Expectations<\/h2>\n\n\n\n
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How to Set Up Your First Data Context<\/h2>\n\n\n\n
What the Data Context Actually Tracks<\/h3>\n\n\n\n
How to Write and Run Expectations<\/h2>\n\n\n\n
How to Read Data Docs Reports<\/h2>\n\n\n\n
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How to Handle Failed Validations in a Pipeline<\/h2>\n\n\n\n
Key Takeaways<\/h2>\n\n\n\n
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