Great Expectations and Soda SQL are open-source frameworks that let teams write automated tests—called expectations or checks—against their data so issues are caught long before they hit production dashboards.
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.
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.
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.
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.
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.
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.
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.
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.
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.
GE, born in 2017, has broader community support and >17k GitHub stars. Soda is newer but rapidly catching up with enterprise features.
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.
Waiting to add data tests until after a pipeline is live leads to technical debt. Bake them in from the first commit.
Teams sometimes implement hundreds of expectations before understanding business priorities, causing alert fatigue. Start simple.
Hard-coding connection strings or thresholds makes migrations painful. Use environment variables and shared configs.
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:
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metrics or GE custom SQL expectations in Galaxy’s AI-assisted editor.Galaxy doesn’t replace Great Expectations or Soda SQL; it accelerates the query authoring workflow that feeds them.
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.
Selecting the right data quality framework determines how efficiently engineers can detect data issues, how seamlessly tests fit into CI/CD pipelines, and how quickly teams can trust their analytics. An informed choice prevents costly data incidents and accelerates development velocity.
No. Great Expectations and Soda SQL excel in different contexts. GE shines in Python ecosystems needing advanced customization, while Soda wins for warehouse-centric, YAML-driven teams.
Yes, but it requires translating expectations into checks (or the reverse) and re-wiring your orchestration. Start with core tests, validate results match, then phase out the old framework.
Galaxy is a modern SQL editor. You can draft Soda metrics or GE custom SQL expectations inside Galaxy, share them via Collections, and rely on its AI copilot to refactor tests as schemas evolve.
No. Soda SQL is fully open-source and can run locally or in CI/CD without Soda Cloud. The cloud service adds dashboards and alerting but is optional.