Data quality checks scatter. Soda gives them one home.

Soda is a data quality and observability platform. Engineers and business users define checks together in shared data contracts.

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

They use Soda to:

  • Catch schema breaks in CI/CD, before a change merges
  • Get alerts in Slack within minutes, not days
  • Hold a 95% data quality SLA, and hit it
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Data Platform Teams search for data quality tools because their checks are no longer keeping up.

  1. Every team checks data its own way, in its own tool.

  2. Setting the checks up was easy. Keeping them running became the job.

  3. The people who know the rule can't add it without an engineer.

  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

Not one more tool. One place for every rule.

Each dataset gets one data contract. Every check for it lives there, whichever source the data sits in and whoever wrote the rule.

  • Checks scattered across teams and tools
  • Upkeep grows with every new table
  • Rules wait in an engineer's queue
  • Data contracts · Dashboards

    Every check for a dataset lives in one contract, across every source. One view shows what is covered and what failed.

  • Check suites · Data standards

    Define a rule once and reuse it across datasets. The next table is one more contract, not one more script.

  • Soda AI

    Whoever knows the rule writes it in plain English. Engineers review it before it runs.

  1. Checks scattered across teams and tools
    Data contracts · Dashboards

    Every check for a dataset lives in one contract, across every source. One view shows what is covered and what failed.

  2. Upkeep grows with every new table
    Check suites · Data standards

    Define a rule once and reuse it across datasets. The next table is one more contract, not one more script.

  3. Rules wait in an engineer's queue
    Soda AI

    Whoever knows the rule writes it in plain English. Engineers review it before it runs.

Teams that put every check in one place.

“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

  • A business analyst or data analyst can write and provision checks themselves through self-service.
    Gu Xie
    Group 1001

    Gu XieHead of Data Engineering

  • 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 tests whether data meets requirements such as freshness, completeness, uniqueness and valid values. It reports failures so teams can investigate issues and track quality over time. Soda groups these requirements in executable data contracts, giving engineers and business users a shared place to define and manage checks.

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

Yes. Soda combines checks for known business requirements with metric-level and record-level anomaly detection. Data quality checks validate explicit rules, while data observability identifies unexpected changes. Record-level detection analyzes patterns across columns, rows and segments to flag unusual records, helping teams investigate issues that aggregate metrics can miss.

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

Yes. Business users and data stewards can define requirements through Soda’s UI or in plain language with AI. Engineers can contribute through code. Their work becomes part of the same dataset contract, so the people who understand the data can help define its quality requirements while teams review and manage checks together.

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

Yes. Start with a critical dataset and the requirements your existing checks enforce. Soda supports custom SQL checks, so you can express existing business logic in a contract and compare results before retiring older checks. Adapting each check depends on its SQL dialect and implementation; you do not need to move every dataset at once.

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

Consider an additional tool when your requirements span multiple systems, your teams need a shared way to manage checks, or business users need to contribute. Soda brings checks into dataset contracts alongside monitoring and reconciliation. Evaluate it against the coverage, collaboration and visibility you need beyond the checks already running in your platform.

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

Yes. Soda provides metric-level and record-level reconciliation checks. Record-level checks use keys to match rows between source and target, then compare values to find differences. Teams can inspect results from both datasets before switching systems. Reconciliation checks live in data contracts alongside the quality checks you continue running after the migration.

Read about data reconciliation
Can Soda run in our own environment and keep failed rows private?

Yes. With a self-hosted Soda Runner, checks execute in your environment. A Diagnostics Warehouse keeps failed-row data in your warehouse for investigation. Soda Cloud receives metadata, aggregated check results and monitoring metrics. Your team can review these data flows against its access and security requirements.

Review Soda’s data flows

Put your first dataset under contract.

Bring the checks you already run. Add the business rules your team needs, then extend coverage to more datasets.

Book a demo
  1. Start hereChoose a datasetStart with data your business relies on.
  2. DefineBring existing checks into one data contract.
  3. ReviewHave engineers and business users review the requirements.
  4. ExpandApply shared standards to more datasets and sources.