Great Expectations: The Complete Guide to Ensuring Data Quality in Modern Data Pipelines

In a world where decisions are increasingly data-driven, one bad dataset can derail an entire analytics effort or machine learning model. We often focus on building pipelines but neglect to ensure that what flows through them --our data-- is actually trustworthy.

That’s where Great Expectations (GX) steps in.

Great Expectations is an open-source framework for validating, documenting, and profiling data to ensure consistency and quality across your data systems.

This guide will walk you through everything you need to know about Great Expectations -- from fundamental concepts to hands-on examples, all the way to production-grade integrations.

Great Expectations (GX) brings testing discipline to data engineering.

Just as developers write unit tests to validate code behavior, GX lets you define expectations about your data -- rules that describe what “good data” should look like.

When those rules are violated, GX immediately flags or blocks the data before it causes downstream damage.

Here are the foundational pieces that make GX so powerful.

1. Expectations

An Expectation is a declarative rule about your data.

For example:

PYTHON
expect_column_values_to_not_be_null("customer_id")
expect_column_values_to_be_between("age", 18, 60)
expect_column_values_to_match_regex("email", r"[^@]+@[^@]+\.[^@]+")

GX includes over 100 built-in Expectations, covering:

• Schema validation
• Numeric range checks
• Regex patterns
• Uniqueness and null detection
• Custom logic through Python functions

2. Expectation Suites

A collection of related expectations, grouped logically into a suite.

For example:

JSON
{
  "expectations": [
    {"expect_column_values_to_not_be_null": {"column": "customer_id"}},
    {"expect_column_values_to_be_between": {"column": "age", "min_value": 18, "max_value": 60}}
  ]
}

Suites act as data quality contracts, version-controlled just like code.

3. Checkpoints

A Checkpoint runs an Expectation Suite against a dataset.

You can trigger Checkpoints:

• On a schedule (via Airflow or Dagster)
• On data arrival (via AWS Lambda or S3 events)
• In CI/CD (to validate data during deployment)

Example checkpoint configuration:

YAML
name: customer_data_checkpoint
expectation_suite_name: customer_suite
validations:
  - batch_request:
      datasource_name: customer_db
      data_asset_name: customers

4. Data Docs

Data Docs are auto-generated HTML reports that visualize your validation results beautifully. They include:

• Which expectations passed or failed
• Validation timestamps
• Links to datasets and checkpoints

You can host Data Docs internally or share them across teams — perfect for collaboration between data engineers, analysts, and business users.

Let’s go step by step.

Step 1: Install Great Expectations and pandas

BASH
pip install great_expectations
pip install pandas

Step 2: Import the great_expectations library and pandas.

PYTHON
import great_expectations as gx
import pandas as pd

Step 3: Download and read the sample data into a Pandas DataFrame.

PYTHON
df = pd.read_csv("file_Name.csv")

Step 4: Create a Data Context.

PYTHON
context = gx.get_context()

Step 5: Connect to data and create a Batch.

PYTHON
data_source = context.data_sources.add_pandas("pandas")
data_asset = data_source.add_dataframe_asset(name="pd dataframe asset")
batch_definition = data_asset.add_batch_definition_whole_dataframe("batch definition")
batch = batch_definition.get_batch(batch_parameters={"dataframe": df})

Step 6: Create an Expectation.

PYTHON
expectation = gx.expectations.ExpectColumnValuesToBeBetween(
    column="column_name", min_value=1, max_value=6, severity="warning"
)

step 7: Run code

PYTHON
validation_result = batch.validate(expectation)
print(validation_result)

GX is built for flexibility. You can integrate it with nearly any modern data stack.

1. Automated Data Quality Assurance: Detect nulls, duplicates, invalid formats, and schema drifts automatically.
2. Data Documentation as a Byproduct: Beautiful Data Docs give you transparency across teams — analysts, engineers, and business stakeholders.
3. Flexible and Scalable: Supports Pandas, Spark, SQL, Snowflake, BigQuery, Redshift, and more.
4. Customizable: Write custom expectations, use plugins, and integrate deeply into your pipelines.
5. Prevents Downstream Failures: Catches data issues early — before they affect your analytics or ML models.

1. Start small: Validate key columns first (IDs, timestamps, critical fields).
2. Automate validations: Schedule checkpoints in your orchestration layer.
3. Version control suites: Store Expectation Suites in Git.
4. Generate docs automatically: Make Data Docs part of your CI build output.
5. Monitor continuously: Use alerts or dashboards to track data health trends.

Data validation isn’t a one-time task. It’s a culture — a commitment to data trust. Great Expectations automates this culture, helping organizations move from reactive firefighting to proactive assurance.

With built-in integrations, human-readable documentation, and community-driven growth, GX has become the industry standard for data quality management.

“If you don’t test your data, you’re just guessing.”

Great Expectations empowers you to define what “good data” means — and enforce it at every step of your pipeline. As your organization grows, GX scales with you, ensuring your insights remain accurate, consistent, and credible.

• Official Great Expectations Docs
• GitHub Repository
• Community Slack
• DataToInsight Guides