Data quality checks scatter. Soda gives them one home.

2K Games went from zero to 1,945 checks across 984 datasets.

They use Soda to:

  • Monitor petabytes of player data at launch
  • Keep studio and marketing data reliable
  • Meet a 95% data quality service level
Book a demo

Your data grows. Your checks grow apart.

  1. Checks span tools. Coverage is hard to see.

  2. Maintaining checks leaves less time to cover new data.

  3. Business teams know the rules. Engineers have to write them.

  4. Something still breaks, and you hear about it from the business first.

    A table breaks, a dashboard user notices, and only then does the platform team hear about it. Time before the platform team hears Tablebreaks Dashboard usernotices Platformteam hears

One contract per dataset. Engineers and business teams write it.

  1. Manage checks together. Reuse standards across datasets.

    Data Contracts · Data Standards

  2. Cover more data. Draft checks with AI. Detect anomalies in your metrics.

    Soda Autopilot · Data observability

  3. Let business teams contribute. Write checks in code, without code, or in plain language.

    Soda AI · Contract collaboration

Choose a data quality tool by who writes your checks.

From the checks you write yourself to the tools you buy: what each kind does well, and where it stops.

Kind of toolBest whenWatch forWith Soda
Tests written in codeTests in your transformation tool, Great Expectations, Pandera, Deequ
Best whenOne engineering team owns the data and already knows the rules.
Watch forEngineers only. A business user has nowhere to propose a check.
With SodaEngineers work in Git. Business users propose checks in a no-code editor for the owner to review.
Checks built into your data platformDatabricks expectations and DQX, Snowflake, AWS Glue Data Quality
Best whenYour data lives on one platform and you want checks switched on where it sits.
Watch forChecks cover data on that platform. Data on other sources needs its own.
With SodaOne way to write checks on Databricks, Snowflake and other sources, with results in one place.
Data observability and monitoringMonte Carlo, Anomalo
Best whenYou want to spot changes in volume, freshness or values without writing rules first.
Watch forBuilt to alert after the data lands, with no contract object for teams to agree on.
With SodaSoda does both: monitors flag what changed, contract checks catch broken rules before data ships.
Governance suitesCollibra, Informatica, Ataccama
Best whenA governance program needs policies, a glossary and data owners in one place.
Watch forBuilt for governance teams. Heavy for engineers who keep their work in Git.
With SodaKeep your catalog. Check results show up in Collibra, Atlan, Alation and Purview.
End-to-end data qualityAutomatically find, understand and fixSoda
Contracts define good data before it ships and monitors watch it once it lands. When a check fails, the owner is notified, the failing rows can land in your own warehouse, and the pipeline can stop.

Based on each tool's public documentation, October 2026.

Build or buy? Your SQL goes a long way.

Switch on what's true for your team. If none of it is, keep what you have.

Keep your checks.

A handful of tables, one engineer who knows every rule, no plans to grow: your SQL is the better pick.

Time for Soda. Bring checks together.

Recommended for your team

  • Data contracts Shared checks per dataset
  • Data standards Apply checks to matching datasets
  • No-code editor Propose rules for owner review
  • Custom SQL checks Reuse your existing checks
  • Metric monitors Flag unusual changes without rules
orders_currency_check.sqlorders.contract.yml · Soda
dataset: dwh/analytics/sales/orderschecks:  - metric:      query: |SELECT COUNT(*) FROM sales.ordersWHERE currency NOT IN ('EUR','USD')      threshold:        must_be_less_than: 1

Your datasets need an owner. At 2K, all 984 have one.

“We went from detection to prevention. Most data quality tooling will tell you something broke after it broke. Our goal is to catch it before that reaches the dashboards and datasets in the first place.”
Sid Srivastava, Director of Data Governance
Sid Srivastava
  • Investing in data quality is key for cross-functional teams to make accurate, complete decisions.
    Mario Konschake

    Mario KonschakeDirector, Data Platform

  • Data contracts brought transparency and also bigger cooperation between different teams.
    Renata Hlavová
    make

    Renata HlavováData Engineer

  • We're catching issues we never would've noticed before.
    Zach Phillips
    Appfire

    Zach PhillipsSenior Product Analyst

  • Working with Soda has improved the productivity of my team. Previously we would do a lot of back-and-forth Q&A: “is this file up to date? working? are you sure”.
    Patrick Callinan
    CarTrawler

    Patrick CallinanDirector of Insights and Data Science

Also on Soda

What to look for in a data quality tool.

Coverage, collaboration and how it fits your stack.

What does a data quality tool do?

A data quality tool checks for missing values, duplicates, stale data and broken business rules, then reports failures. Soda groups a dataset’s checks in a shared contract.

Explore data contracts
Does Soda combine data quality and data observability?

Yes. Soda combines business rule checks with anomaly detection on your metrics, catching unexpected changes in volume or freshness.

Explore data observability
Can business users create data quality checks without writing code?

Yes. Business users and data stewards use Soda’s interface or plain language with AI. Engineers write in code. Both contribute to the same contract.

See how teams author checks
Can we adopt Soda gradually and keep our existing SQL checks?

Yes. Start with one dataset. Bring existing SQL checks in as custom SQL checks. Some may need adapting. Compare results before retiring older checks.

Explore custom SQL checks
When should we add a data quality tool to Snowflake or Databricks?

Add Soda for checks across sources, reusable rules or business teams writing checks. Keep existing controls running as you introduce Soda.

Explore shared data contracts
Can Soda compare source and target data during a migration?

Yes. Compare source and target metrics or match records by key to find value differences. Review results before switching. Keep reconciliation and ongoing quality checks in the same contract.

Read about data reconciliation
What does a data quality tool cost?

Vendors price per table, per check, per user or by compute. Soda’s Team plan is $750 per month. Enterprise has custom pricing and adds no-code checks, private deployment and single sign-on.

Ask us for a quote
Are there open-source data quality tools?

Yes. Great Expectations is open source. Your team builds and operates the surrounding platform. A shared platform helps when more teams or business users need to write checks.

See how Soda runs checks in code
Can Soda run in our own environment and keep failed rows private?

Yes. A self-hosted Soda Runner runs checks in your environment. With a Diagnostics Warehouse enabled, failed rows from row-level checks stay in your warehouse. Soda Cloud receives metadata, check results and monitoring metrics. Every plan offers a Soda-hosted Runner.

Review Soda’s data flows

Introduction meeting · 30 minutes · Zoom

Put your checks
where
put theirs.

Thirty minutes with the Soda team to learn how your checks work today and how Soda can help you and your team.

  • Two short steps, then pick a time
  • Checks can run inside your own network
  1. 1You
  2. 2Your company
  3. 3Your time