Soda vs Great Expectations

Data contracts are becoming the backbone of modern data architectures. As organisations shift from ad-hoc pipelines to product-oriented data ecosystems, they need guarantees: that data will arrive on time, in the right shape, and with the expected semantics. This is where tools like Soda and Great Expectations (GE) enter the picture.

Both solve a similar problem—testing and validating data—but their approaches reflect two worlds: cloud-native SaaS versus Python-first open source. Understanding their strengths helps teams choose the right tool to enforce their data contracts.

What Are Data Contracts?

A data contract is an agreement between producers and consumers about the quality, structure, and availability of data. Just like APIs, contracts define:

• Schema (the shape of the data)
• Semantics (what the fields mean)
• SLAs/SLOs (timeliness, freshness, completeness)
• Expectations (valid ranges, uniqueness, referential integrity)

Contracts prevent “silent breakage” in data platforms by making expectations explicit and testable. The challenge: operationalising them at scale.

Great Expectations: The Open Source Standard

Launched in 2017, Great Expectations quickly became the default for data quality checks in Python-based pipelines. Great Expectations is the heavyweight champion – feature-rich, highly customizable, but with a steeper learning curve.

• Key Features:
  • Declarative expectations (e.g., “column A must be non-null”)
  • Data Docs (human-readable validation reports)
  • Integration with Airflow, dbt, Spark, Snowflake, Databricks
  • Strong developer ecosystem and community
• Strengths:
  • Python-native, flexible, highly customisable
  • Proven for local, on-prem, or hybrid setups
  • Transparency—users can see and extend the full codebase
• Weaknesses:
  • Can feel heavy with boilerplate YAML/config
  • Maintenance overhead (scaling, orchestration, storage of results)
  • Less SaaS-like monitoring—teams need to build ops around it

GE works well for engineering-led data contracts, where development teams want fine-grained control and are already comfortable in Python.

validator.expect_column_values_to_be_between(
    column="age", 
    min_value=0, 
    max_value=120
)
validator.expect_column_values_to_match_regex(
    column="email",
    regex=r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"
)

validator.expect_column_values_to_be_between(
    column="age", 
    min_value=0, 
    max_value=120
)
validator.expect_column_values_to_match_regex(
    column="email",
    regex=r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"
)

Soda: Cloud-Native Observability

Soda takes a different tack: a cloud-first, SaaS platform for data observability and quality monitoring. Soda is the lightweight contender – simpler, faster to implement, with a focus on SQL-first testing.

• Key Features:
  • Soda Cloud (centralised dashboards, alerting, collaboration)
  • Soda Checks (SQL- or YAML-defined quality rules)
  • Integrations with Snowflake, BigQuery, Databricks, Redshift
  • Incident management, alerting, and Slack/MS Teams workflows
• Strengths:
  • SaaS model—fast setup, central visibility across teams
  • Collaboration features (shared dashboards, contract enforcement)
  • Focused on data contracts as a service, linking directly to producers/consumers
• Weaknesses:
  • Vendor lock-in and subscription costs
  • Less developer freedom than writing raw GE expectations
  • Relies on Soda’s ecosystem for extensibility

Soda fits best where business-facing data contracts are needed—where data teams want non-technical stakeholders (data product owners, analysts) to see and manage contract health in real time.

checks for orders:
  - row_count > 1000
  - missing_count(customer_id) = 0
  - avg(order_amount) between 10 and 5000
  - duplicate_count(order_id) = 0
  - freshness(order_date) < 1d

checks for orders:
  - row_count > 1000
  - missing_count(customer_id) = 0
  - avg(order_amount) between 10 and 5000
  - duplicate_count(order_id) = 0
  - freshness(order_date) < 1d

Head-to-Head: Soda vs Great Expectations

Great Expectations is like a Swiss Army knife – powerful but complex. Soda is the chef’s knife – it does one thing exceptionally well. For most modern data teams working in cloud warehouses with SQL-heavy workflows, Soda’s simplicity wins. But if you need the full power and flexibility of Python-based validation, Great Expectations remains unmatched.

Neither tool alone “solves” data contracts. Both illustrate the trend:

• Contracts as Code (GE): Developers embed quality checks directly into pipelines.
• Contracts as Service (Soda): Teams manage observability and SLAs centrally in the cloud.

Whether you pick Soda or Great Expectations, the message is the same: without data contracts, modern data platforms will crumble under broken assumptions.