08.02 · Walkthrough
Great Expectations Validation
Create a Great Expectations suite that validates schema, null rates, ranges, categorical values, and row counts.
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Great Expectations turns data contracts into executable validation rules for tables entering a pipeline. A suite can check expected columns and types, acceptable null rates, numeric bounds, allowed category values, and row counts, so bad loads or contract drift are caught before downstream transformations, dashboards, or models consume the data.
What this lesson answers
- how to validate schema with Great Expectations
- how to check null rates in data pipelines
- how to test row counts before downstream jobs
Notes
Great Expectations is a data validation framework that lets you turn assumptions about a dataset into executable checks. In a data engineering workflow, these checks act like tests for data: they verify that incoming tables have the expected shape, types, completeness, and business constraints before downstream jobs depend on them. A validation suite is the collection of expectations for a dataset, such as which columns must exist, which columns may not be null, what ranges numeric values should fall within, which values are allowed for categorical fields, and how many rows should be present.
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Common questions
- What is a Great Expectations validation suite?
- A validation suite is a versioned set of expectations for a dataset. It describes what valid data looks like: required columns, data types, completeness rules, accepted ranges, allowed categorical values, and expected volume. Running the suite produces pass or fail results that can be used to block, alert, or investigate pipeline changes.
- Where should data validation run in a pipeline?
- Validation should run as close as practical to ingestion or to the transformation that changes the contract. That placement catches broken feeds, schema drift, empty loads, and unexpected values before dependent jobs consume them. The result should be visible to the team responsible for deciding whether to fix data or update expectations.
- Should null checks require every value to be present?
- Not always. Some fields are genuinely required and should reject missing values. Others are optional or only populated for certain records, so a threshold is more useful than an absolute rule. Null-rate expectations let you encode that distinction and detect meaningful changes without treating every missing value as a failure.
Short definition: what is Great Expectations Validation?