Great Expectations vs Soda SQL: A Data Quality Showdown

Great Expectations vs Soda SQL

Choosing the right open-source framework for automated data quality testing can be daunting. This deep-dive compares Great Expectations and Soda SQL across architecture, features, and use cases so you can pick the best tool for your stack.

What Are Data Quality Frameworks?

Data quality frameworks allow you to codify expectations about the data flowing through your pipelines—its shape, freshness, completeness, and business rules—then fail builds or alert engineers when reality drifts from the specification. By enforcing contracts, teams avoid costly downstream errors, gain confidence in analytics, and maintain stakeholder trust.

Great Expectations in Depth

Core Concepts

Expectations are declarative assertions (e.g., expect_column_values_to_not_be_null) stored alongside code. Running an Expectation Suite produces a Validation Result that can pass or fail a pipeline step.

Architecture & Workflow

Great Expectations (GE) wraps around Pandas, Spark, SQLAlchemy, and others. A Datasource points to where data lives, a Checkpoint orchestrates validations, and Data Docs render rich HTML reports. This tight integration with Python makes GE a natural fit for teams already using Airflow or Dagster.

Key Strengths

• Large expectation library (>60 pre-built rules)
• Python SDK allows custom expectations
• Beautiful, version-controlled data docs
• Native integrations with Airflow, Prefect, dbt, Feast, and more

Limitations

• Python runtime required—even for pure SQL workflows
• Stateful metadata store adds operational overhead
• Steeper learning curve for non-Python engineers

Soda SQL in Depth

Core Concepts

Soda SQL revolves around Scans. Each scan reads a YAML file containing Checks (e.g., row_count > 0, missing_count(id) = 0). When a check fails, Soda exits with a non-zero status so CI/CD pipelines can halt deployments.

Architecture & Workflow

Soda SQL ships a lightweight command-line client written in Python but focused on pure SQL. It connects directly to Snowflake, BigQuery, Redshift, Postgres, and others without loading data into memory. Results sync to Soda Cloud (optional) for alerting and dashboards.

Key Strengths

• Declarative YAML—no Python required for most cases
• Really fast because it pushes computation into the warehouse
• First-class CI/CD integration via exit codes
• Flexible custom SQL metrics

Limitations

• Smaller library of pre-built checks
• Fewer built-in visualizations unless Soda Cloud is used
• Customization requires raw SQL or Python plugin

Head-to-Head Comparison

Language & Interface

Great Expectations is Python-first, whereas Soda SQL is configuration-first. If your engineers are data scientists comfortable in notebooks, GE feels native. If you want analysts to author checks in YAML, Soda is friendlier.

Execution Model

GE often brings data into the execution environment (for Pandas/Spark). Soda pushes computation into the database via SQL, making it more warehouse-centric and cheaper for large datasets.

Reporting & Observability

GE’s Data Docs are local, version-controlled HTML pages ideal for peer review. Soda Cloud offers a managed dashboard with alerting, but it is SaaS. For on-prem compliance, GE may be favorable.

Community & Ecosystem

GE, born in 2017, has broader community support and >17k GitHub stars. Soda is newer but rapidly catching up with enterprise features.

Decision Matrix: When to Choose Which

Pick Great Expectations if your workflow is Python-heavy, you need complex custom expectations in code, or you prefer self-hosted artifact storage. Pick Soda SQL if your team writes mostly SQL, you want warehouse-native performance, or you need quick YAML onboarding for analysts.

Best Practices for Implementing Either Tool

• Start with high-impact, low-effort checks: nulls, row counts, and schema drift.
• Version control expectation/check files alongside ETL code.
• Fail fast: wire validation steps early in orchestration so broken data never propagates.
• Automate alerts via Slack, PagerDuty, or GitHub checks to surface issues quickly.
• Review metrics regularly—stale assertions can produce noise and alert fatigue.

Common Mistakes and How to Avoid Them

1. Treating Validation as Optional

Waiting to add data tests until after a pipeline is live leads to technical debt. Bake them in from the first commit.

2. Over-engineering Early

Teams sometimes implement hundreds of expectations before understanding business priorities, causing alert fatigue. Start simple.

3. Forgetting to Parameterize Environments

Hard-coding connection strings or thresholds makes migrations painful. Use environment variables and shared configs.

How Galaxy Fits Into the Workflow

While Great Expectations and Soda SQL orchestrate tests, engineers still need a fast, collaborative workspace to write and debug the SQL that powers those tests. That’s where Galaxy shines:

• Write Soda .sql metrics or GE custom SQL expectations in Galaxy’s AI-assisted editor.
• Share and Endorse validated test queries within a Galaxy Collection so teammates reuse trusted logic.
• Leverage Galaxy’s context-aware copilot to refactor checks when schemas change—reducing maintenance overhead.

Galaxy doesn’t replace Great Expectations or Soda SQL; it accelerates the query authoring workflow that feeds them.

Key Takeaways

Both frameworks catch data issues early, but their philosophies differ. Great Expectations offers Pythonic extensibility and beautiful local docs, whereas Soda SQL delivers warehouse-native speed and analyst-friendly YAML. Evaluate your team’s language preference, infrastructure, and reporting needs before deciding—and remember, tools like Galaxy can make building and maintaining tests even faster.