Soda Core

Open-source data quality framework; SodaCL plus Soda Cloud.

Reviewed by 7wData

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Publisher review

Soda Core is an open-source framework for embedding data quality checks into modern data pipelines using SodaCL, a YAML-based declarative syntax. The tool targets data engineers who want to version data quality definitions in Git and enforce them through CI/CD or orchestration platforms like Airflow, Dagster, and Prefect. The architecture separates concerns cleanly: Soda Core runs standalone or embedded in pipelines; Soda Cloud, the commercial tier, adds centralized monitoring, alerting, and collaborative data contracts—avoiding vendor lock-in.

Unlike behavior-based observability platforms such as Monte Carlo, Soda is rule-based by design. You define explicit standards: freshness windows, null counts, row counts, statistical distributions, and custom SQL checks. That explicitness appeals to teams with clear data quality standards but can feel limiting for teams seeking automated discovery of unknown issues. The open-source tier has no anomaly detection, no field-level lineage, and no root-cause analysis across systems.

Soda was founded in 2018 in Brussels by Maarten Masschelein and Tom Baeyens. The company raised €12.9M in July 2024 and operates offices in Brussels and Chicago. It's a rare data quality vendor with genuine European origins.

The practical appeal is strong: SodaCL's SQL-friendly syntax has a much lower barrier to entry than Great Expectations' programmatic Python approach. The CLI is smooth, integration with dbt/Airflow/Dagster requires minimal boilerplate, and checks live in version control like any other infrastructure code. But you own the burden of defining standards. For mid-market teams, pricing becomes a consideration: the free tier handles basic pipeline testing, but the Team plan ($750/month) quickly rises with usage-based SPU costs. As of May 2026, the framework is actively maintained with 2.4k GitHub stars, 141 releases, and a healthy backlog of community connectors.

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How it works

  1. SodaCL declarative syntax

    YAML-based language for defining data quality checks, versioned in Git and enforced automatically in pipelines.

  2. 50+ built-in quality checks

    Freshness, volume, nullness, completeness, validity, distributions, schema validation, and reconciliation rules.

  3. Custom SQL and Python UDFs

    Write arbitrary SQL WHERE clauses or Python functions for validation logic beyond built-in checks.

  4. Multi-source connector support

    Runs checks on Snowflake, BigQuery, Databricks, DuckDB, PostgreSQL, MySQL, and 18+ other data sources.

  5. Pipeline orchestration integration

    Native support for Airflow, Dagster, Prefect, and dbt as embedded checks or standalone CLI invocation.

  6. Soda Cloud monitoring and alerting

    Centralized dashboard, anomaly detection, Slack/email alerts, and data contract collaboration (paid tier).

  7. Data contract validation

    Enforce both schema and data quality guarantees, supporting modern data contract patterns.

Strengths and trade-offs

Strengths

  • Low learning curve with SQL/YAML syntax; shallow ramp compared to programmatic alternatives.
  • Checks live in Git and integrate into CI/CD pipelines; no separate UI needed for basic usage.
  • Clean split between free open-source and paid cloud tiers; genuine optionality to avoid lock-in.

Trade-offs

  • Rule-based only; no automated anomaly detection or behavior learning in open-source tier; you define standards manually.
  • Free tier lacks UI, dashboards, alerting, and centralized governance; teams must build custom reporting.
  • No field-level lineage, impact tracing, or root-cause analysis across systems; limited to single-dataset context.

Pricing context

Soda Core is free and open-source under Apache 2.0. Soda Cloud starts at $0/month (free tier: up to 3 datasets, pipeline testing, metrics observability, alerting integrations). Team Plan runs $750/month plus usage-based Soda Processing Units (SPUs) that vary with data volume; many mid-market teams experience overages beyond base cost.

Enterprise Tier has custom pricing and includes collaborative data contracts, AI-powered quality features, RBAC, SSO, and private deployment options. Annual billing and volume discounts are available.

Getting started with Soda Core

  1. Install Soda Core

    Install the open-source framework with pip. Add a connector for your database: pip install 'soda-core[snowflake]' for Snowflake or 'soda-core[bigquery]' for BigQuery. Soda supports Databricks, PostgreSQL, MySQL, and 18+ others. Verify with soda --version. Your environment is ready to define checks.

  2. Set up database connection

    Create a configuration file with your database credentials: host, port, username, password. Store secrets in environment variables. Point Soda to your data source: Snowflake, BigQuery, Databricks, PostgreSQL, or another supported system. Test the connection to confirm Soda can access your data.

  3. Define data quality checks

    Write checks in SodaCL, a YAML declarative syntax. Choose from 50+ built-in checks: freshness windows, null counts, row thresholds, distributions, validity rules, or custom SQL. Version your check file in Git alongside code. Each check explicitly states your data standards.

  4. Run your first data scan

    Execute Soda Core from the command line to run checks against your data. Review the results in terminal output to see which checks passed or failed. This baseline run confirms your configuration and check logic work correctly before moving to production.

  5. Wire checks into your pipeline

    Embed Soda Core into Airflow, Dagster, Prefect, or dbt so checks run automatically as part of your workflow. Integration requires minimal code. Optionally upgrade to Soda Cloud (paid) for centralized dashboards, alerting, and scheduling. Data quality is now continuous.

Frequently Asked Questions

What is Soda Core?

Soda Core is an open-source framework for embedding data quality checks into data pipelines using YAML-based syntax. It lets data engineers define standards like freshness, nullness, and row counts, version them in Git, and enforce them through CI/CD pipelines or orchestration platforms like Airflow and Dagster.

What is SodaCL?

SodaCL is a YAML-based language for defining data quality checks in Soda Core. It offers low barrier to entry versus programmatic approaches, allowing engineers to express freshness windows, null counts, schema validation, and custom SQL checks using simple syntax that lives in Git alongside infrastructure code.

How do you integrate Soda Core with Airflow?

Soda Core integrates with Airflow through minimal boilerplate setup. You can embed checks directly in DAGs as tasks or invoke the CLI standalone. Checks are defined in YAML using SodaCL, version controlled in Git, and enforced automatically when pipelines run. Native support means no custom operators are needed.

How much does Soda Core cost?

Soda Core is free and open-source under Apache 2.0 license. Soda Cloud, the commercial tier, starts at $0/month for up to three datasets, then $750/month for Team Plan plus usage-based Soda Processing Units (SPUs). Enterprise tier offers custom pricing with data contracts, RBAC, SSO, and AI-powered quality features.

How does Soda Core differ from Monte Carlo?

Soda Core is rule-based; you define explicit data quality standards. Monte Carlo uses behavior-based observability with automated anomaly detection. Soda lacks automated discovery in open-source tier but offers lower learning curve with SQL/YAML syntax. Choose Soda for explicit standards and developer experience; choose Monte Carlo for automated anomaly learning.

Can I use Soda Core without paying?

Yes, Soda Core is completely free and open-source under Apache 2.0. You can define and enforce unlimited checks using SodaCL syntax with no cost. Soda Cloud's free tier covers basic testing for three datasets. Centralized monitoring, alerting, and data contract collaboration require the paid Team Plan or Enterprise tier.

Alternatives in this category

Integrations

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How Soda Core compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Soda Core

Pricing
Soda Core is free and open-source under Apache 2.0. Soda Cloud starts at $0/month (free tier: up to 3 datasets, pipeline testing, metrics observability, alerting integrations). Team Plan runs $750/month plus usage-based Soda Processing Units (SPUs) that vary with data volume; many mid-market teams experience overages beyond base cost. Enterprise Tier has custom pricing and includes collaborative data contracts, AI-powered quality features, RBAC, SSO, and private deployment options. Annual billing and volume discounts are available.
Target
Soda Core is an open-source framework for embedding data quality checks into modern data pipelines using SodaCL, a YAML-based declarative syntax.
Deployment
self-hosted
Strength
Low learning curve with SQL/YAML syntax; shallow ramp compared to programmatic alternatives.
Watch for
Rule-based only; no automated anomaly detection or behavior learning in open-source tier; you define standards manually.

Great Expectations

Pricing
GX Core open-source, free. GX Cloud Developer tier free (3 users). Team and Enterprise require contact sales.
Target
Data engineers wanting Python-native, code-first validation integrated into pipelines via programmatic Expectations API.
Deployment
Open-source (GX Core) or SaaS (GX Cloud).
Strength
Python Expectations API handles custom validation logic that declarative YAML-based frameworks cannot express natively.
Watch for
Complex initial configuration and Python dependency are top user complaints on G2 and TrustRadius.

Monte Carlo

Pricing
All plans require contact sales. Vendr data shows typical mid-market annual contracts between $30,000 and $80,000.
Target
Data engineering and analytics teams wanting automated table monitoring without writing explicit quality rules.
Deployment
SaaS.
Strength
ML-driven anomaly detection identifies quality issues automatically without requiring users to define thresholds or rules.
Watch for
No published pricing. Minimum annual contracts reported at $30,000. Metadata history accumulates on Monte Carlo servers, making switching costly.

Datafold

Pricing
Free tier available. Cloud from $799/month billed annually. Enterprise: custom.
Target
Teams running large-scale database migrations needing cross-database row-level reconciliation and validation.
Deployment
SaaS. On-prem for Enterprise.
Strength
Column-level data diff across databases identifies row-level discrepancies between source and target tables during migrations.
Watch for
Substantially repositioned as an AI migration platform in 2024. Open-source data-diff package sunset May 2024. Data quality monitoring is no longer the core offering.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. github.com — Project status, active maintenance (v4.10.1 May 2026), 2.4k stars, 141 releases, 50+ built-in checks, multi-source connector support
  2. www.eu-startups.com — Founding year (2018), headquarters (Brussels), founding team (Maarten Masschelein, Tom Baeyens), recent funding (€12.9M July 2024)
  3. soda.io — Company location and offices (Brussels and Chicago), current positioning as AI-native data quality platform
  4. www.modern-datatools.com — Pricing tiers (Free $0, Team $750/month + SPUs, Enterprise custom), SPU usage-based model details
  5. www.siffletdata.com — Strengths (pipeline integration, YAML syntax, developer experience), weaknesses (manual rule definition, limited observability, basic anomaly detection)
  6. medium.com — Practical limitations (learning curve, limited free-tier monitoring, complementary nature of framework), strengths (accessibility, flexibility, comprehensive checks)
  7. www.thedataletter.com — Comparative strengths vs Great Expectations (SQL/YAML ease vs programmatic power), use-case alignment, deployment trade-offs