Tool Review: Soda Core vs. Great Expectations

Declarative simplicity or programmatic power? How to pick your data quality foundation.

Hope you’re having a relaxed Sunday. If you’re planning the week ahead, data quality is probably on the list. Choosing the right tool to enforce that quality is critical, and the debate often comes down to two open source contenders: Soda Core and Great Expectations.

Both are Python-based and designed to answer the same question: Can I trust this dataset? However, their philosophies and approaches to providing an answer are fundamentally different.

Let’s break them down.

The Philosophical Divide: Programmatic vs. Declarative

At their core, the difference between these tools is a difference in worldview.

Great Expectations (GX) is the engineer’s choice. It takes a programmatic, code-first approach. While early versions relied heavily on complex YAML configurations, the modern framework (especially post-v1.0) is built around the concept of “Expectations” defined directly in Python. You create these testable assertions, often in a Jupyter notebook, to build a rich, detailed test suite for your data. It’s like building a custom quality control rig from the ground up. You have immense power and flexibility, but you also have to assemble the components yourself.

Soda Core takes a more declarative, SQL-friendly approach. Its primary interface is a YAML file where you define “checks” using a straightforward, custom language. Instead of writing Python code, you’re writing lines like:

checks for dim_customers:
- row_count > 0
- missing_count(email) = 0
- duplicate_count(customer_id) = 0

It’s designed for clarity and speed, asking, “What do you need to be true?” rather than, “How do you want to test it?”

Head-to-Head: Where It Matters

1. Getting Started & Learning Curve

• Soda Core: The lower barrier to entry. If you can write a SQL WHERE clause, you can start writing Soda checks in minutes. The YAML syntax is intuitive, making it easy for data analysts and engineers alike to contribute to data quality.

• Great Expectations: A steeper initial climb. You need to be comfortable with Python and its data stack (Pandas, Spark). The initial setup, understanding Data Contexts, Batch Requests, and Expectation Suites, is more involved. The payoff is ultimate flexibility, but the onboarding requires a bigger time investment.

2. Flexibility & Power

• Great Expectations: The clear winner for complexity. Need to validate that the statistical distribution of a column hasn’t shifted? Or that a text field follows a specific regex pattern across multiple datasets? GX’s programmatic nature makes this possible. You can leverage the full power of Python to create highly specific Expectations.

• Soda Core: Powerful, but primarily within a declarative framework. It covers the vast majority of standard data quality checks (freshness, volume, validity, etc.) with its simple syntax. While it favors simplicity, it is not restrictive. For highly complex or custom validation logic, Soda allows you to define custom SQL checks and even Python User Defined Functions (UDFs) to push the boundaries of its framework, making the flexibility gap with Great Expectations narrower than it initially seems.

3. Integration and Data Sources

Both tools support a wide array of data sources (Snowflake, BigQuery, Redshift, Postgres, etc.). The integration experience, however, differs, and one crucial element for the modern data stack is dbt.

• Soda Core: Feels lightweight. You configure your data source in the YAML file, and you’re off. It’s designed to be run as a CLI tool, fitting neatly into existing CI/CD pipelines or orchestration tools like Airflow or integrated directly via the soda-dbt package.

• Great Expectations: More structured. It creates a dedicated project directory with a defined configuration. This structure is a strength for complex, organization-wide deployments. GX also has a strong presence in the dbt ecosystem, often used via the dedicated dbt-expectations package, allowing data teams to run GX checks as native dbt tests.

4. The It Factor: Data Docs

One of the most valuable features both tools offer is automatically generated Data Docs. These are HTML pages that document your data quality rules and test results.

• Great Expectations: Its Data Docs are a standout feature and serve as a centralized, clearly-defined contract for your data. It’s a fantastic tool for building trust with data consumers.

• Soda Core: Also generates clean and functional Data Docs, effectively communicating pass/fail status for your checks.

Final Verdict: Which One Should You Choose?

The choice isn’t about which tool is better, but which one is better for your team. I know that sounds like a cop-out, but it’s the truth.

Choose Soda Core if:

• You need to get a data quality framework up and running yesterday.

• Your team is more comfortable with SQL and YAML than with Python.

• Your checks are primarily around freshness, volume, completeness, and basic validity.

• You value simplicity and a low-friction developer experience.

Choose Great Expectations if:

• You have complex validation logic that requires the full expressiveness of Python.

• Your team is composed of strong Python developers who are comfortable with the data ecosystem.

• You need to build a comprehensive, bespoke data contract for critical datasets.

• You’re planning a deep, organization-wide integration and are willing to invest the setup time.

The First Step on a Longer Journey

Choosing between Soda Core and Great Expectations is a critical first step. Implementing either one will bring a new level of confidence to your data platform. You’ll stop bad data from reaching your stakeholders and finally have a formalized process for ensuring data reliability.

Yes, from a strategic standpoint, validating data is only the first step. Even the most sophisticated data quality tool only solves one part of the problem. You can have perfect, 100% valid data, but if you’re accessing it using inefficient, poorly designed queries, you’re still hemorrhaging money. The tool ensures the data is correct. It doesn’t ensure you’re using it wisely.

In Wednesday’s Data Letter, we’re going to tackle a problem that often flies under the radar, one that can have a direct and massive impact on your bottom line. We’ll move beyond data quality and into a different kind of audit. One that looks at the hidden costs running in your warehouse every single day.

Until then, may your data be valid and your pipelines run smoothly.

Hodman Murad