If you're building a modern data stack, you've likely encountered both Great Expectations and dbt tests. Both tools promise to improve data quality, both let you write assertions about your data, and both can stop bad data from propagating through your pipelines. So which one should you use?

The short answer: probably both, but in different ways. The longer answer requires understanding what each tool was actually designed to do.

The Core Philosophy Difference

Before diving into features, it's crucial to understand that Great Expectations and dbt tests emerged from different philosophies about where data quality checks belong in your architecture.

dbt tests are fundamentally about transformation validation. They're designed to verify that your SQL transformations produce the expected outputs. Think of them as unit tests for your analytics code. They run as part of your transformation workflow, typically in your data warehouse, and they're tightly coupled to your dbt models.

Great Expectations takes a broader view. It's a standalone data validation framework designed to work across your entire data pipeline—from raw ingestion through to final consumption. It's more like a comprehensive quality assurance system that can validate data at any stage, regardless of what tools you're using for transformation.

When to Use dbt Tests

dbt tests shine in specific scenarios that align with the transformation layer of your data pipeline:

1. Validating Transformation Logic

If you're writing SQL transformations in dbt, the native tests are perfect for validating that your logic works as intended. Need to ensure your user_id is never null after a join? That your revenue calculations are always positive? That your date spine has no gaps? dbt tests handle these elegantly.

-- models/schema.yml
version: 2

models:
  - name: fct_orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: order_total
        tests:
          - positive_value

2. Simple, Fast Checks During Development

dbt tests are incredibly low-friction. They're defined in YAML alongside your models, require no additional infrastructure, and run in the same environment as your transformations. During development, you can run dbt test to get immediate feedback on whether your models meet basic quality standards.

3. Column-Level Validation in Your Warehouse

Because dbt tests compile to SQL and run in your warehouse, they're ideal for checks that leverage your warehouse's compute power. Testing relationships between tables, checking for referential integrity, or validating complex business rules across millions of rows—all of these work well with dbt's native execution model.

4. When You Need Tight Git-Based Governance

Since dbt tests live in your version-controlled dbt project, they inherit all the benefits of your git workflow: code review, CI/CD integration, and clear change history. For teams where analysts own data quality rules and need to propose changes through pull requests, this is invaluable.

When to Use Great Expectations

Great Expectations becomes the better choice when you need capabilities beyond transformation validation:

1. Validating Raw Data at Ingestion

Great Expectations excels at validating data as it enters your systems—before any transformations occur. If you're ingesting data from third-party APIs, partner data feeds, or legacy systems, you want to catch problems immediately, not after you've spent compute resources transforming bad data.

import great_expectations as gx

context = gx.get_context()
batch = context.get_batch(batch_request)

expectations = [
    batch.expect_column_values_to_not_be_null("user_id"),
    batch.expect_column_values_to_be_between("age", 0, 120),
    batch.expect_column_values_to_match_regex("email", r"^[\w\.-]+@[\w\.-]+\.\w+$")
]

results = batch.validate()

2. Complex Statistical Validation

Great Expectations provides a rich library of statistical expectations that go far beyond what you'd want to implement in SQL. You can validate distributions, detect anomalies, check for data drift, and perform sophisticated pattern matching. These capabilities are essential for ML pipelines and advanced analytics.

3. Multi-Platform Data Validation

If your data lives in multiple systems—some in Snowflake, some in S3, some in Postgres—Great Expectations provides a unified interface for validation across all of them. You write expectations once and can apply them regardless of where the data sits.

4. Rich Documentation and Data Profiling

Great Expectations automatically generates comprehensive data documentation, including visual data profiling, validation results over time, and human-readable expectation suites. This is particularly valuable for data governance, stakeholder communication, and regulatory compliance.

5. Advanced Alerting and Observability

Great Expectations integrates with observability platforms and can send detailed alerts with context about what failed and why. The validation results are structured data that you can route to Slack, PagerDuty, or your monitoring stack with rich metadata about the failure.

The Power of Using Both

Here's where it gets interesting: the most mature data teams use both tools, strategically placed at different points in their pipeline.

A common pattern looks like this:

This layered approach provides defense in depth. Source data problems get caught early, transformation bugs get caught during development, and critical outputs get extra validation before going to production.

Practical Decision Framework

Still not sure which to choose? Ask yourself these questions:

Are you validating data before or after dbt runs? If before (or without dbt at all), you need Great Expectations. If during dbt transformation, dbt tests are the natural choice.

How complex are your validation rules? Simple column-level checks work great in dbt. Statistical distributions, complex patterns, or ML-specific validations need Great Expectations.

Who owns the validation rules? If analytics engineers managing dbt own them, keep them in dbt. If data engineers or platform teams own them separately, Great Expectations provides better separation of concerns.

What's your tolerance for setup complexity? dbt tests require almost no setup—they're built in. Great Expectations requires more initial configuration but provides more power and flexibility.

Common Pitfalls to Avoid

Don't try to make dbt tests do everything. I've seen teams write incredibly complex custom dbt tests that would be much simpler as Great Expectations. If you're writing Python macros to implement statistical checks in dbt, you're probably using the wrong tool.

Conversely, don't use Great Expectations for simple transformation validation. If you're already running dbt and you just want to check that a column is unique and not null, adding Great Expectations is overkill.

And please, don't implement validation logic in both tools for the same data at the same pipeline stage. That's just maintenance burden with no real benefit.

The Bottom Line

Great Expectations and dbt tests aren't competitors—they're complementary tools that solve different problems. dbt tests are perfect for validating your transformation logic with minimal friction. Great Expectations is the right choice for comprehensive data validation across your entire pipeline, especially at ingestion and for complex quality checks.

For most teams building production data pipelines, the answer isn't choosing one or the other. It's understanding where each tool adds value and implementing them strategically at the right points in your data flow. Start with dbt tests if you're already using dbt, then add Great Expectations when you need validation capabilities that dbt can't easily provide.

The goal isn't to use every tool available—it's to build reliable data systems that catch problems early and give your stakeholders confidence in the data they're using to make decisions.