Great Expectations vs Soda: Data Quality Tools Compared (2026)

Great Expectations (GX) and Soda both validate data before it reaches your dashboards and downstream models. GX is a Python library -- you write validation logic as code, version it in Git, and run it inside Python pipelines. Soda is a YAML-first tool -- you define checks in SodaCL files that read closer to plain English. The right choice depends on whether your team thinks in Python or in configuration files.

The comparison table

How each tool fits into a data pipeline

Here's where GX and Soda typically sit in a pipeline. Both go between the transform step and the consumption layer -- they're the gate that stops bad data from reaching dashboards.

                       Great Expectations path
                       ──────────────────────
  Source DB ──► ETL/ELT ──► Warehouse ──► GX Checkpoint ──► Dashboard
                                │              │
                                │         Python code
                                │         runs expectations
                                │         generates data docs
                                │
                       Soda path
                       ─────────
  Source DB ──► ETL/ELT ──► Warehouse ──► Soda Scan ──► Dashboard
                                              │
                                         YAML checks
                                         SodaCL definitions
                                         results → Soda Cloud

Both tools query the warehouse directly. GX runs Python against a data context (a connection config). Soda runs its CLI scanner against a data source defined in a YAML config. Neither moves data -- they execute queries in place and report pass/fail.

What does the same check look like in each tool?

The best way to feel the difference is to see the same validation written both ways. Say you want to check that an orders table has no null order_id values, that amount stays between 0 and 100,000, and that the table received fresh data in the last 24 hours.

Great Expectations (Python):

import great_expectations as gx
 
context = gx.get_context()
 
datasource = context.data_sources.add_postgres(
    name="warehouse", connection_string="postgresql://..."
)
asset = datasource.add_table_asset(name="orders", table_name="orders")
batch = asset.add_batch_definition_whole_table("full_table").get_batch()
 
suite = context.add_expectation_suite("orders_quality")
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeBetween(
        column="amount", min_value=0, max_value=100000
    )
)
# Freshness requires a custom expectation or SQL query --
# GX has no built-in freshness check.
 
checkpoint = context.add_or_update_checkpoint(
    name="orders_checkpoint",
    validations=[{"batch": batch, "expectation_suite_name": "orders_quality"}],
)
result = checkpoint.run()

Soda (SodaCL YAML):

# checks/orders.yml
checks for orders:
  - missing_count(order_id) = 0
  - min(amount) >= 0
  - max(amount) <= 100000
  - freshness(updated_at) < 24h

soda scan -d warehouse -c configuration.yml checks/orders.yml

The GX version is roughly 20 lines of Python. The Soda version is 4 lines of YAML plus a CLI command. That gap is the core trade-off: GX gives you full programmatic control; Soda gives you speed and readability.

Which tool has better validation coverage?

GX ships with over 300 built-in expectations. You can validate column types, value ranges, regex patterns, set membership, cross-column relationships, statistical distributions, and table-level row counts. If none of those fit, you write a custom expectation in Python -- a class with a _validate method that returns pass/fail. There's no ceiling on what you can check.

Soda's SodaCL covers the checks most teams actually need: missing values, duplicates, schema changes, freshness, row counts, reference checks (foreign key validity), and custom SQL expressions. The built-in set is smaller, but SodaCL's failed rows and custom SQL checks let you write arbitrary validation logic when the defaults don't cover your case.

In practice, most data quality work falls into a small number of categories -- nulls, ranges, freshness, duplicates, schema drift. Both tools handle these. The difference shows up at the edges: statistical distribution checks, cross-table consistency validations, and conditional expectations are where GX's library depth pulls ahead.

How does each tool handle alerting?

GX does not include alerting. You get a validation result object (pass/fail per expectation) and it's on you to wire that into Slack, PagerDuty, email, or whatever your team uses. Most teams build a thin wrapper that parses the result JSON and posts to a webhook. It works, but it's another thing to maintain.

Soda Cloud includes alerting out of the box. You configure notification channels in the Soda Cloud UI -- Slack, email, PagerDuty, webhooks -- and checks that fail trigger alerts automatically. You can set severity levels, route different checks to different channels, and track incidents over time. If you want alerting without building it yourself, Soda has a real advantage here.

For a deeper look at monitoring strategies at different budget levels, see our guide on monitoring data quality without expensive platforms.

How do they integrate with dbt?

Both tools have dbt integrations, but they work differently.

GX integrates through the dbt-expectations package -- a set of dbt macros that mirror GX expectation names. You write tests in your schema.yml using GX-style names like expect_column_values_to_not_be_null. These run as dbt tests, not as GX checkpoints, so you don't get data docs or the full GX result object. It's more of a naming convention port than a deep integration.

Soda's soda-dbt integration runs Soda checks alongside your dbt tests. You can import dbt test results into Soda Cloud for a unified view of data quality -- dbt tests and SodaCL checks in one dashboard. Soda can also ingest your dbt manifest to understand model lineage. If your team already runs dbt and wants data quality monitoring that sits next to it, Soda's integration is more tightly coupled.

For teams choosing between dbt Core and dbt Cloud, we have a separate breakdown: dbt Core vs dbt Cloud.

What about documentation and data docs?

GX generates static HTML documentation called data docs. Every time you run a checkpoint, GX updates a local HTML site showing each expectation, whether it passed or failed, and when it last ran. You can host these on S3, GCS, or any static file server. For teams that want a self-hosted, version-controlled record of data quality over time, data docs are a genuine differentiator.

Soda's equivalent is the Soda Cloud UI -- a hosted dashboard that shows check results, trends, incidents, and dataset health scores. It's more polished than data docs and includes features like anomaly history, dataset profiling, and check scheduling. But it requires a Soda Cloud subscription.

If you're the type of team that wants everything in Git and self-hosted, GX's data docs fit that philosophy. If you'd rather have a managed UI with less setup, Soda Cloud is the easier path.

What does each tool cost?

Great Expectations is fully open source under Apache 2.0. GX Cloud is in beta and will offer a managed experience, but the core library is free. Your cost is engineering time -- setting up data contexts, writing expectations, building alerting integrations, and maintaining the Python code.

Soda Core (the CLI scanner) is open source. Soda Cloud -- the hosted platform with alerting, anomaly detection, incident tracking, and the UI -- starts at roughly $200/month for small teams and scales with data source count and check volume. The free tier covers basic scanning with limited history.

For a broader view of what's available across the data quality space, see best data quality tools for data teams.

Which one should you pick?

Pick GX if your team writes Python daily, you need validation logic that goes beyond simple threshold checks, and you want everything version-controlled and self-hosted. GX's depth is real -- 300+ expectations, custom expectations as Python classes, and data docs that live wherever you host static files. The trade-off is setup time and maintenance burden.

Pick Soda if you want to be running checks by end of day, your team prefers YAML and SQL over Python, or you need built-in alerting and anomaly detection without wiring it yourself. Soda Cloud adds real operational value -- incident tracking, anomaly baselines, and notification routing -- at the cost of a monthly subscription.

FAQ

Can I use Great Expectations and Soda together?

Yes, and some teams do. A common pattern is GX for detailed validation logic in Python pipelines (where you need programmatic control) and Soda for quick YAML checks on warehouse tables with Soda Cloud providing the monitoring UI. There's overlap, but the tools don't conflict -- they query the same data sources independently.

Which tool is easier to set up for a first-time user?

Soda. You install soda-core, write a YAML configuration pointing at your database, write a few SodaCL checks, and run soda scan. A working setup takes under 30 minutes. GX requires understanding data contexts, data sources, expectation suites, and checkpoints before you can run your first validation. The v1.0 release simplified this, but it's still a steeper ramp.

Do either tool support data contracts?

Both are moving in this direction. GX expectations can serve as a form of data contract -- you define what valid data looks like and fail the pipeline if it doesn't match. Soda has explicit data contract support in SodaCL, letting you define contracts as YAML and enforce them in CI. Soda's framing is more contract-native; GX's is more validation-native, but they achieve similar outcomes.

How do these tools compare to Monte Carlo or Anomalo?

Monte Carlo and Anomalo are data observability platforms -- they monitor your entire warehouse automatically with ML-based anomaly detection and lineage tracking. GX and Soda are data testing tools -- you write explicit checks for specific tables. The observability platforms are passive monitors; GX and Soda are active gates. Most teams that outgrow GX or Soda add a platform like Monte Carlo alongside them, not instead of them.

Can I run these tools in CI/CD?

Both. GX checkpoints can run in GitHub Actions, GitLab CI, or any Python-capable CI environment -- fail the build if expectations don't pass. Soda scans work the same way -- run soda scan as a CI step and fail on check violations. Both tools return non-zero exit codes on failure, so standard CI integration is straightforward. For teams building data pipelines alongside application code, both fit into existing CI workflows. See our best tools for data engineering teams for how these fit into the broader stack, and best open-source ETL tools for the ingestion layer that feeds them.

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