Soda vs Great Expectations: Complete Comparison for 2025
Two leading open-source data quality tools. Soda offers YAML simplicity with SodaCL. Great Expectations provides Python power and flexibility. Here's how to choose.
Choose Soda if you want:
- ✓Simple YAML-based configuration (SodaCL)
- ✓Faster time to first check (minutes, not hours)
- ✓No Python requirement for basic usage
- ✓Native dbt integration that feels natural
- ✓ML-powered anomaly detection (Soda Cloud)
Choose Great Expectations if you want:
- ✓Python-native approach with full flexibility
- ✓Extensive expectation library (150+ built-in)
- ✓Auto-generated Data Docs (HTML documentation)
- ✓Large, established community and ecosystem
- ✓Deep Spark and big data support
Feature-by-Feature Comparison
| Feature | Soda | Great Expectations |
|---|---|---|
| Primary Approach | YAML-based checks (SodaCL) | Python-native expectations |
| Best For | Data engineers wanting simple YAML config | Python-heavy teams wanting flexibility |
| Open Source | Yes (Soda Core) | Yes (Great Expectations OSS) |
| Pricing | Free core + paid Soda Cloud | Free OSS + paid GX Cloud |
| Configuration | YAML files (SodaCL) | Python code + JSON/YAML |
| Learning Curve | Medium (learn SodaCL syntax) | Higher (Python + GX concepts) |
| Documentation | Good, improving | Extensive, can be overwhelming |
| dbt Integration | Yes (native support) | Yes (via packages) |
| Anomaly Detection | Soda Cloud only (ML-powered) | Limited (rule-based) |
| Data Profiling | Yes | Yes (comprehensive) |
| CI/CD Integration | Yes | Yes |
| Alerting | Soda Cloud (Slack, email, etc.) | GX Cloud or custom |
| Data Docs | Basic reporting | Yes (auto-generated HTML) |
| Community Size | Growing | Large, established |
| Snowflake Support | Yes | Yes |
| Spark Support | Yes | Yes (strong) |
Deep Dive: Key Differences
Configuration Philosophy
Soda uses SodaCL (Soda Checks Language), a YAML-based DSL designed specifically for data quality. It's declarative and readable—you describe what you want to check, not how to check it. Non-programmers can write and understand Soda checks.
Soda check example:
checks for orders:
- row_count > 0
- freshness(created_at) < 1d
- missing_percent(customer_id) < 5%
- duplicate_count(order_id) = 0
- values in (status) must be in ['pending', 'shipped', 'delivered']Great Expectations is Python-native. You define expectations using Python code, giving you the full power of the language. This is more flexible but requires Python knowledge and understanding of GX concepts.
Great Expectations example:
import great_expectations as gx
context = gx.get_context()
validator = context.get_validator(...)
validator.expect_table_row_count_to_be_between(min_value=1)
validator.expect_column_values_to_not_be_null("customer_id")
validator.expect_column_values_to_be_unique("order_id")
validator.expect_column_values_to_be_in_set(
"status", ["pending", "shipped", "delivered"]
)Learning Curve
Soda has a gentler learning curve. If you can read YAML and understand basic data concepts, you can write Soda checks within an hour. The SodaCL documentation is straightforward, and the syntax is intuitive.
Great Expectations has a steeper curve. You need to understand:
- Python programming basics
- GX object model (Data Context, Data Sources, Expectations, Checkpoints)
- How to configure datasources and batch requests
- The expectation suite workflow
However, once you master GX, you have more power and flexibility than Soda offers.
dbt Integration
Both tools integrate with dbt, but the experience differs:
Soda feels more native in dbt workflows. The YAML-based configuration fits dbt's philosophy, and you can run Soda checks as part of your dbt pipeline using the soda-core-dbt package. Checks live alongside your dbt models.
Great Expectations also integrates with dbt but requires more setup. You'll typically run GX in a separate step or use the dbt-great-expectations package. It works well but feels more like a separate tool than an integrated part of dbt.
See our Great Expectations vs dbt Tests article for a deeper comparison.
Cloud Offerings
Soda Cloud adds ML-powered anomaly detection, a collaborative UI, alerting integrations (Slack, email, PagerDuty), and historical trending. It's the path to enterprise features without building infrastructure.
GX Cloud (formerly GX Labs) is newer but growing. It provides a hosted version with collaboration features, though the open-source version is more mature and full-featured compared to Soda Core.
When Each Tool Shines
Soda excels when:
- • Team prefers YAML over Python
- • You need fast time-to-value
- • dbt is central to your stack
- • Non-engineers need to write checks
- • You want ML-powered detection (Cloud)
Great Expectations excels when:
- • Team is Python-native
- • You need maximum customization
- • Auto-generated Data Docs are valuable
- • You're working with Spark at scale
- • You want a mature, battle-tested tool
Frequently Asked Questions
What is the difference between Soda and Great Expectations?
Soda uses a YAML-based configuration language (SodaCL) designed for simplicity, while Great Expectations is Python-native with more flexibility but higher complexity. Soda is easier to get started with; Great Expectations offers more power for Python-heavy teams. Both are open source with paid cloud offerings.
Which is easier to learn: Soda or Great Expectations?
Soda is generally easier to learn. Its SodaCL language is purpose-built for data quality checks and doesn't require Python knowledge. Great Expectations has a steeper learning curve—you need to understand Python, the GX object model, expectations, checkpoints, and data contexts. However, GX offers more flexibility once mastered.
Can I use Soda and Great Expectations for free?
Yes, both have free open-source versions. Soda Core is free and includes the core SodaCL check functionality. Great Expectations OSS is free and very full-featured. Both companies offer paid cloud products (Soda Cloud, GX Cloud) that add collaboration, alerting, and ML-powered features.
Which tool has better dbt integration?
Both integrate well with dbt, but Soda has a slight edge for simplicity. Soda checks can be embedded directly in your dbt project using soda-core-dbt. Great Expectations also works with dbt but requires more configuration. If you're already comfortable with Python, GX's dbt integration is powerful.
Should I use Soda or Great Expectations for data validation?
Choose Soda if you want simpler configuration, faster setup, and your team prefers YAML over Python. Choose Great Expectations if your team is Python-native, you need extensive customization, or you want the mature ecosystem and comprehensive Data Docs feature.
Want Something Even Simpler?
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