dbt vs Great Expectations vs Soda: Which Data Quality Tool to Choose

Are you constantly battling data quality issues that lead to inaccurate KPIs? Do you find yourself struggling with manual, inefficient checks on large, complex datasets, and looking for a way to automate the cleanup process? If you're nodding in agreement, you're not alone.

The frustration is real. As one data engineer put it, "You probably shouldn't use Great Expectations if you want to get something done, it can be needlessly complex and time-consuming to setup." Yet somehow, you need to ensure your data is trustworthy without spending all your time on manual validations.

Data quality is a continuous, demanding process that cannot be handled manually at scale. This is where automated data quality tools come in, streamlining and automating critical activities like profiling, cleansing, and monitoring.

In this comprehensive comparison, we'll examine three leading open-source contenders:

• dbt: The transformation powerhouse with built-in testing
• Great Expectations (GX): The comprehensive validation framework
• Soda: The modern, user-friendly monitoring and observability tool

By the end of this article, you'll have a clear framework to decide which tool aligns best with your team's needs, existing stack, and data quality challenges.

Foundations: What is Data Quality and Why Does It Matter?

Before diving into the tools, let's establish what we mean by "data quality" and why it's worth investing in dedicated solutions.

Key Metrics to Evaluate Data Quality

Data quality can be measured across several dimensions:

• Timeliness: Data is ready when you need it
• Completeness: The amount of usable data is sufficient
• Accuracy: Data is reliable against a source of truth
• Validity: Data conforms to business rule formats
• Consistency: Data is comparable across different datasets

Benefits of High-Quality Data

• Increased Trust & Enhanced Decision-Making: Reliable data enables data-driven decisions and better business outcomes
• Internal Consistency: Standardizes data across departments to avoid discrepancies
• Cost Efficiency: Reduces time and money spent on manual data cleansing

With these foundations established, let's dive into our three contenders.

Deep Dive: dbt for Data Quality

What It Is

dbt (data build tool) isn't primarily a data quality tool, but rather a transformation framework with powerful, integrated testing capabilities. It's best for ensuring data accuracy during transformations, making it a favorite for analytics engineers who live in the dbt ecosystem.

Key Features & Test Types

dbt offers several testing approaches:

1. Generic Tests: Built-in tests that come with dbt Core:
   • unique: Ensures all values in a column are unique
   • not_null: Ensures a column contains no null values
   • accepted_values: Checks if column values are within a specified list
   • relationships: Validates referential integrity between two tables
2. Singular Tests: Custom tests for a specific model, written as a SQL query that should return zero rows on success
3. Custom Generic Tests: Extend dbt's capabilities by importing packages like dbt-expectations, which adds functionality inspired by Great Expectations

How It Works: Implementing dbt Data Quality Checks

Here's a step-by-step approach to implementing data quality checks in dbt:

1. Define Metrics: Identify key metrics like completeness and accuracy
2. Identify Data for Testing: Choose the tables/views to evaluate
3. Define Testing Criteria: Use YAML and SQL to specify checks
4. Set Up dbt Project: Configure your schema.yml file to include the tests
5. Run Tests: Execute dbt test manually or on a schedule (e.g., in a CI/CD pipeline)

Pros & Cons

Pros:

• Seamless integration with transformation workflows
• SQL-based, which most data teams already know
• Massive, highly active community
• Tests defined alongside models for better maintainability

Cons:

• Limited to dbt ecosystem
• Basic reporting (mostly pass/fail logs)
• Primary focus is transformation, not comprehensive data quality

Deep Dive: Great Expectations (GX) for Comprehensive Validation

What It Is

Great Expectations, released in 2017, is a dedicated, open-source data validation and profiling framework. It's designed for in-depth validation of data from multiple sources, not just within transformation workflows.

Key Features

• Expectations: A declarative language for describing assertions about your data, the core of GX
• Automated Data Profiling: Can scan data to automatically generate a suite of expectations
• Data Docs: Automatically generated, human-readable documentation and data quality reports from test results
• Validation: Can be integrated into pipelines (e.g., Airflow) to validate data at critical points
• ExpectAI: A new feature that auto-generates tests to reduce manual effort

Pros & Cons

Pros:

• Comprehensive validation capabilities
• Rich, auto-generated documentation
• Powerful profiling and schema validation
• Strong Python integration

Cons:

• Steep learning curve
• As one user noted, it "can be needlessly complex and time-consuming to setup"
• Over-engineered for simpler use cases
• Requires strong Python skills

Deep Dive: Soda for Data Observability

What It Is

Soda Core (released 2022) is an open-source command-line tool that uses a user-friendly language to turn user-defined checks into SQL queries. It focuses on monitoring and observability, with an emphasis on ease of use.

Key Features

Soda Checks Language (SodaCL): This YAML-based, domain-specific language is designed for data quality and is remarkably readable:

# Example SodaCL validations
checks:
  - missing_count(YEAR) = 0
  - missing_percent(TOTALEMISSIONS) < 5
  - invalid_count(YEAR) = 0:
      valid length: 4

Other Core Features:

• Metrics Observability: Claims to detect anomalies "70% faster and more accurately than Facebook Prophet-based systems"
• Pipeline Testing: Test data early in CI/CD workflows to prevent bad data from being merged
• Collaborative Contracts: Enable data producers and consumers to create shared agreements on data quality

Pros & Cons

Pros:

• Simple, declarative language (SodaCL) with low barrier to entry
• Strong focus on anomaly detection and monitoring
• Collaborative features for data contracts
• Modern architecture and design

Cons:

• Smaller community than dbt and GX
• As one user noted, there's a "lack of community discussion and support around Soda"
• Fewer integrations with other tools
• Relatively new compared to alternatives

Head-to-Head Comparison: A Feature-by-Feature Breakdown

Feature	dbt	Great Expectations (GX)	Soda
Primary Goal	Testing within data transformation	Deep validation, profiling, and documentation	Monitoring, anomaly detection, and observability
Ease of Use	Easy for built-in tests; moderate with packages	Steep learning curve; can be complex	Easy to moderate; user-friendly SodaCL
Test Language	YAML + SQL	Python, JSON, YAML	YAML (SodaCL)
Key Strength	Seamless integration with dbt transformation workflows	Extensive library of "Expectations" and auto-generated "Data Docs"	Simple, declarative language and focus on anomaly detection
Reporting	Basic pass/fail/warn logs; requires other tools for rich UI	Rich, auto-generated HTML reports (Data Docs)	Cloud-based observability dashboard and alerts
Community	Massive and highly active	Large and established open-source community	Growing, but smaller than dbt and GX

The Decision Framework: Which Tool is Right for You?

Choose dbt if...

• You are an "analytics engineer" and live inside dbt Cloud or dbt Core
• Your primary need is to validate assumptions and ensure data integrity during transformation
• You want tests tightly coupled with your models and defined in the same repository
• You prefer SQL-based testing and have a team already familiar with dbt

Choose Great Expectations if...

• You need a comprehensive, standalone data quality framework to validate data from multiple sources
• Detailed, shareable data quality reports (Data Docs) are a critical requirement for your stakeholders
• Your team has strong Python skills and is willing to invest time in mastering a powerful tool
• You need deep profiling capabilities and a high degree of customization

Choose Soda if...

• Your top priority is ease of use and a declarative language that can be adopted by a wider range of roles
• You need strong capabilities for continuous monitoring, alerting, and anomaly detection
• You want to establish "data contracts" between producers and consumers
• You prefer a modern tool with a clean, focused approach to data quality

Building a Culture of Data Trust

Remember that choosing a data quality tool is just one part of the equation. The best tool is one that fits your team's workflow, technical skills, and specific data quality challenges. The ultimate goal is not just to implement a tool, but to foster a culture where data quality is a shared responsibility.

dbt is ideal for integrated transformation testing, Great Expectations excels at deep, standalone validation, and Soda offers user-friendly monitoring and observability. Each has its place in the modern data stack, and many teams even use a combination of these tools to address different aspects of their data quality strategy.

By implementing the right tool(s) for your specific needs, you'll be well on your way to building trust in your data and enabling better business outcomes through reliable, high-quality information.

What's your experience with these tools? Have you found one that works particularly well for your use case? Share your thoughts and experiences in the comments below.

Frequently Asked Questions

What is the main difference between dbt, Great Expectations, and Soda?

The primary difference lies in their core focus. dbt excels at data quality checks integrated within data transformation workflows, Great Expectations provides a comprehensive framework for deep validation and documentation across various data sources, and Soda specializes in user-friendly data monitoring, observability, and anomaly detection.

When should I choose dbt for data quality?

You should choose dbt for data quality when your primary goal is to ensure data integrity during the transformation process. If your team already uses dbt for transformations, its built-in testing is the most seamless and efficient way to validate models, check for nulls, and maintain referential integrity directly within your existing workflows.

Is Great Expectations too complex for a small team?

Great Expectations can have a steep learning curve, which might be challenging for a small team with limited resources. Its comprehensive nature and reliance on Python can feel complex for simple use cases. For teams seeking a quicker setup, dbt (if already in use) or Soda's declarative language (SodaCL) might offer a more accessible starting point.

How do Soda and Great Expectations compare for data monitoring?

Both tools can be used for data monitoring, but they approach it differently. Great Expectations focuses on validating data against predefined "Expectations" at specific points in a pipeline, generating detailed reports. Soda is built more for continuous observability, using its simple SodaCL to run checks on a schedule and providing powerful anomaly detection features to automatically flag unexpected changes in your data over time.

Can dbt, Great Expectations, and Soda be used together?

Yes, many teams use these tools together to cover different aspects of data quality. A common pattern is to use dbt for tests during transformation, Great Expectations for rigorous validation of raw data at ingestion or critical data assets, and Soda for continuous monitoring and alerting on production data warehouses.

Which data quality tool is best for beginners?

For beginners already familiar with SQL and working within the dbt ecosystem, dbt's native testing is the easiest to start with. For a standalone tool, Soda is often considered more beginner-friendly due to its simple, declarative SodaCL language, which has a lower barrier to entry than the extensive Python-based configuration of Great Expectations.