great-expectations

Overview

GX Core is the modern Python library for programmatic data validation workflows. The shape is: DataSource → DataAsset → BatchDefinition → ExpectationSuite → ValidationDefinition → Checkpoint (gx-overview). This skill covers authoring expectations, running them via a ValidationDefinition or Checkpoint, parsing the JSON result, and gating CI on it.

When to use

• The repo imports great_expectations (Python).
• The user asks about ExpectColumn*, ExpectationSuite, Checkpoint, Data Docs, or gx.get_context().
• A pipeline needs row-level / column-level assertions on Pandas, SQL, or Spark data with a programmatic interface (rather than a YAML-only config like Soda).
• A CI workflow needs to gate a run on a validation result and surface failures in Data Docs or Slack.

Authoring expectations

The four key objects to compose (gx-overview):

1. DataSource - represents a connection to a data store (Pandas / SQL / Spark / files).
2. DataAsset - a collection of records inside a DataSource (e.g. a table, a directory of partitioned files).
3. BatchDefinition - slices a DataAsset into validatable batches (whole table, partition, dataframe).
4. ExpectationSuite - a named collection of Expectation objects that describe what the data should look like.

Expectations themselves come from the gxe namespace (create-an-expectation):

import great_expectations as gx
from great_expectations import expectations as gxe

context = gx.get_context()

suite = context.suites.add(gx.ExpectationSuite(name="orders_suite"))

# Column-level expectations
suite.add_expectation(gxe.ExpectColumnValuesToNotBeNull(column="order_id"))
suite.add_expectation(gxe.ExpectColumnValuesToBeUnique(column="order_id"))
suite.add_expectation(
    gxe.ExpectColumnValuesToBeBetween(
        column="discount_percent", min_value=0, max_value=100
    )
)
suite.add_expectation(
    gxe.ExpectColumnValuesToBeInSet(
        column="status",
        value_set=["placed", "shipped", "completed", "returned"],
    )
)

# Table-level expectations
suite.add_expectation(gxe.ExpectTableRowCountToBeBetween(min_value=1, max_value=10_000_000))

The full expectation gallery (column-, table-, multi-column-, and custom expectations) is browsable at greatexpectations.io/expectations.

Running

Option A - ValidationDefinition (single-suite, single-batch)

A ValidationDefinition binds one BatchDefinition to one ExpectationSuite. Calling .run() validates and returns a JSON-shaped result whose results list reports each expectation's outcome (run-validation-definition):

validation_definition = context.validation_definitions.get("orders_validation")

# batch_parameters maps to the underlying BatchDefinition's keys
result = validation_definition.run(batch_parameters={"year": "2026"})
print(result.success)       # bool — True only if every expectation passed

batch_parameters keys depend on how the BatchDefinition was authored: {"dataframe": df} for a Pandas runtime asset, {"year": "...", "month": "..."} for partitioned data, etc. (run-validation-definition).

Option B - Checkpoint (multi-suite + actions)

A Checkpoint runs one or more ValidationDefinitions and triggers Actions on the result. Actions live in great_expectations.checkpoint; built-ins include UpdateDataDocsAction (regenerates the Data Docs static site) and SlackNotificationAction (alerts on failure) - all action class names end with *Action (checkpoint-actions):

import great_expectations as gx
from great_expectations.checkpoint import (
    SlackNotificationAction,
    UpdateDataDocsAction,
)

context = gx.get_context()
validation_definitions = [context.validation_definitions.get("orders_validation")]

action_list = [
    SlackNotificationAction(
        name="alert_on_failure",
        slack_token="${VALIDATION_SLACK_WEBHOOK}",
        slack_channel="${VALIDATION_SLACK_CHANNEL}",
        notify_on="failure",
        show_failed_expectations=True,
    ),
    UpdateDataDocsAction(name="refresh_data_docs"),
]

checkpoint = gx.Checkpoint(
    name="orders_checkpoint",
    validation_definitions=validation_definitions,
    actions=action_list,
    result_format={"result_format": "COMPLETE"},
)

context.checkpoints.add(checkpoint)
checkpoint.run()

result_format controls how much detail the Validation Result carries. Documented values include SUMMARY (default) and COMPLETE - use COMPLETE when downstream tooling needs the failing rows / unexpected-values list (checkpoint-actions).

Parsing the result

validation_definition.run() (and the per-validation entries on a Checkpoint result) returns a JSON-shaped object with at least (run-validation-definition):

Triage script:

result = validation_definition.run()
if not result.success:
    for r in result.results:
        if not r.success:
            # r.expectation_config has the expectation type / kwargs
            # r.result has the unexpected_count / unexpected_percent
            print(r.expectation_config.type, r.result)

When result_format: COMPLETE, each r.result block additionally carries unexpected_index_list (Pandas) or unexpected_value_counts, which lets the gate report the offending rows by id rather than just a count.

CI integration

The minimal pattern is: gx.get_context() from a repo-checked-in GX project, run a Checkpoint, exit non-zero on not result.success. Use UpdateDataDocsAction so the rendered HTML report is uploaded as a build artifact for human triage.

# scripts/run_gx_gate.py
import sys
import great_expectations as gx

context = gx.get_context()
checkpoint = context.checkpoints.get("orders_checkpoint")
result = checkpoint.run()

if not result.success:
    sys.exit(1)

# .github/workflows/data-quality.yml (excerpt)
- name: Run GX checkpoint
  run: python scripts/run_gx_gate.py

- name: Upload Data Docs
  if: always()
  uses: actions/upload-artifact@v4
  with:
    name: gx-data-docs
    path: gx/uncommitted/data_docs/local_site/

if: always() is required so the Data Docs upload survives a failing checkpoint - that's exactly when you need them for triage.

References

• gx-overview - GX Core concept model and 4-step workflow.
• create-an-expectation - gxe namespace and expectation instantiation patterns.
• run-validation-definition - .run(batch_parameters=...) and the result object shape.
• checkpoint-actions - Checkpoint composition and built-in Actions.