Great Expectations

Great Expectations is an open-source framework for validating data quality and testing whether datasets meet predefined rules.

It allows teams to define expectations for data, such as required columns, accepted value ranges, uniqueness rules, or missing-value limits, and then automatically check whether incoming data satisfies those conditions.

Great Expectations is commonly used in ETL pipelines, analytics workflows, data warehouses, and machine learning systems.

Also known as: Data quality framework, data validation framework.

How Does Great Expectations Work?

Great Expectations evaluates datasets against rules called Expectations.

A typical workflow includes:

1. Connect Great Expectations to a data source.
2. Define data quality expectations.
3. Group those expectations into a validation suite.
4. Run validation against the dataset.
5. Record which expectations passed or failed.
6. Trigger alerts or downstream actions when validation fails.

This makes data quality checks repeatable and easier to integrate into automated pipelines.

What Is an Expectation?

An Expectation is a rule that describes what valid data should look like.

Examples may include:

A column must exist.
Values must not be null.
Values must be unique.
Numbers must fall within a defined range.
Strings must match an expected pattern.

For example, a product dataset may require:

product_id must be unique
price must be greater than 0
currency must not be null

Each rule can be tested automatically.

Expectation Suites

An Expectation Suite is a collection of related data quality rules.

For example, a product dataset may use a suite containing checks such as:

product_id exists
product_id is unique
product_name is not null
price is greater than 0
currency uses an approved value

Grouping rules into suites makes it easier to apply consistent validation across repeated pipeline runs.

Checkpoints

A Checkpoint coordinates validation tasks.

It can define:

• Which data should be validated
• Which expectation suite should be used
• What should happen after validation
• Where validation results should be stored

A checkpoint can be included in scheduled or automated data workflows.

Great Expectations vs. Data Cleaning

Great Expectations primarily validates whether data meets defined quality rules.

Data cleaning modifies or corrects problematic records.

In simple terms:

• Great Expectations: Detects whether data violates defined rules.
• Data Cleaning: Fixes, removes, or transforms problematic data.

The two processes are often used together.

For example:

Validate Data
     ↓
Identify Problems
     ↓
Clean Invalid Records
     ↓
Validate Again

Great Expectations vs. Data Observability

Data observability is a broader practice focused on monitoring the health and reliability of data systems.

Great Expectations focuses more directly on rule-based validation.

In simple terms:

• Great Expectations: Tests whether datasets meet explicit conditions.
• Data Observability: Monitors broader changes, failures, lineage, freshness, and system behavior.

A data platform may use both approaches together.

Great Expectations vs. Unit Testing

Software unit tests verify whether application code behaves as expected.

Great Expectations applies a similar idea to datasets.

In simple terms:

• Unit Test: Tests software behavior.
• Data Expectation: Tests data behavior and quality.

For example, a software test may verify that a function returns the correct result, while a data expectation verifies that a column contains only valid values.

Common Data Quality Checks

Great Expectations can be used to validate many aspects of data quality, including:

• Column existence
• Null values
• Unique values
• Data types
• Numeric ranges
• String patterns
• Accepted categories
• Table size
• Value distributions

These checks can help detect pipeline problems before incorrect data reaches downstream systems.

Advantages of Great Expectations

• Automated data validation: Quality checks can run automatically.
• Reusable rules: Expectations can be applied repeatedly to new datasets.
• Pipeline integration: Validation can be added to ETL and orchestration workflows.
• Clear failure reporting: Teams can identify which rules failed.
• Improved reliability: Invalid data can be detected before reaching analytics or machine learning systems.
• Documentation support: Expectations also describe what valid data is supposed to look like.

Limitations of Great Expectations

• Configuration effort: Teams must define meaningful validation rules.
• Maintenance requirements: Expectations need updates when schemas or business rules change.
• Rule-based focus: It may not automatically detect every unexpected data problem.
• Pipeline complexity: Large validation suites can add operational overhead.
• Performance cost: Running extensive checks on very large datasets can require additional resources.

Great Expectations and ETL Pipelines

Great Expectations is commonly added to ETL workflows to verify data at important stages.

A pipeline may look like this:

Extract
   ↓
Validate Raw Data
   ↓
Transform
   ↓
Validate Transformed Data
   ↓
Load

This helps prevent invalid data from moving further through the pipeline.

For example, a pipeline may stop before loading if required fields are missing or price values fall outside acceptable ranges.

Great Expectations and Web Data

Web data can be inconsistent because websites may change layouts, field formats, or content structures.

Great Expectations can help detect these changes after data collection.

A web data workflow may validate rules such as:

Product ID must exist
Product name must not be empty
Price must be numeric
Currency must use an approved code
Collected timestamp must exist

If a website changes and the scraper begins returning empty prices, the validation step can detect the problem before the bad data reaches downstream systems.

Example

Suppose a product data pipeline produces the following records:

Product ID | Name              | Price | Currency
1001       | Wireless Mouse    | 29.99 | USD
1002       | Keyboard          | 49.99 | USD
1003       | Monitor           |       | USD

The validation rules require:

Product ID must be unique.
Name must not be null.
Price must not be null.
Price must be greater than 0.
Currency must be USD.

The third record fails because the price is missing.

Great Expectations can report the failed rule so the pipeline can stop, alert an operator, or send the record for further processing.

Key Takeaway

Great Expectations is an open-source data quality framework that validates datasets against predefined rules called Expectations.

It helps teams detect missing, invalid, duplicated, or inconsistent data before it reaches downstream analytics, machine learning, or storage systems. By integrating validation into ETL and web data pipelines, Great Expectations can make data workflows more reliable and easier to monitor.