Great Expectations (Data Quality) Cheat Sheet

Define, validate, and document data quality expectations for pipelines using Great Expectations suites, checkpoints, and data docs.

2 PagesIntermediateJan 20, 2026

Create an Expectation Suite

Define reusable data quality rules against a pandas or SQL data source.

python

import great_expectations as gxcontext = gx.get_context()source = context.sources.add_pandas("orders_source")asset = source.add_dataframe_asset(name="orders", dataframe=df)validator = context.get_validator(    batch_request=asset.build_batch_request(),    expectation_suite_name="orders_suite",)validator.expect_column_values_to_not_be_null("order_id")validator.expect_column_values_to_be_between("amount", min_value=0, max_value=100000)validator.expect_column_values_to_be_in_set("status", ["pending", "completed", "cancelled"])validator.save_expectation_suite(discard_failed_expectations=False)

Run a Checkpoint

Bundle a batch and a suite into a checkpoint and execute validation as a pipeline step.

python

checkpoint = context.add_or_update_checkpoint(    name="orders_checkpoint",    validations=[{        "batch_request": asset.build_batch_request(),        "expectation_suite_name": "orders_suite",    }],)result = checkpoint.run()print(result.success)  # False if any expectation failedif not result.success:    raise ValueError("Data quality check failed — halting pipeline")

Validate a SQL Table

Point Great Expectations at a warehouse table via a SQLAlchemy connection string.

python

source = context.sources.add_sql(    name="warehouse", connection_string="postgresql://user:pass@host/db")asset = source.add_table_asset(name="orders_tbl", table_name="orders")validator = context.get_validator(    batch_request=asset.build_batch_request(),    expectation_suite_name="orders_suite",)validator.expect_table_row_count_to_be_between(min_value=1)

Frequently Used Expectations

A starting set of expectation methods covering the majority of data quality checks.

expect_column_values_to_not_be_null- flags unexpected nulls in a required column
expect_column_values_to_be_unique- enforces primary-key-like uniqueness
expect_column_values_to_be_between- range check for numeric columns
expect_column_values_to_match_regex- format validation, e.g. emails or phone numbers
expect_table_row_count_to_be_between- guards against empty or unexpectedly small loads
expect_column_pair_values_A_to_be_greater_than_B- cross-column consistency checks

Pro Tip

Wire checkpoint.run() into your orchestrator (Airflow, Dagster) as a hard gate before the load step — catching a schema drift or null spike before it reaches the warehouse is far cheaper than debugging a downstream dashboard.

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