Bring data quality together. Expand your coverage.

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 SLA
Book a demo

As your data grows, checks get harder to manage.

  1. Checks are spread across tools, making coverage hard to see.

  2. Maintaining existing 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

Shared checks. Clear ownership. More coverage.

Bring each dataset’s checks into a shared data contract. Reuse rules across datasets, add business context and see where quality needs attention.

  1. Manage checks together. Keep rules in shared contracts and reuse standards across datasets.

    Data Contracts · Data Standards

  2. Cover more data. Use AI to draft checks and detect metric and record-level anomalies.

    Soda Autopilot · Data observability

  3. Let business teams contribute. Write checks in code, the UI or plain language.

    Soda AI · Contract collaboration

How 2K and HelloFresh manage data quality.

“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 checks for missing values, duplicate records, stale data and values that break business rules. It reports failures so teams can investigate and track quality over time. Soda combines these checks with anomaly detection. Engineers and business users define requirements together in data contracts: shared sets of checks for each dataset.

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 create checks in Soda’s UI or describe a rule in plain language with Soda AI. Engineers can work in code. Both contribute to the same dataset contract, so business teams can add their knowledge without handing every request to an engineer.

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

Yes. Start with one dataset and the checks you already run. Soda supports custom SQL checks, so you can bring existing business logic into a data contract and compare results before retiring older checks. Some SQL may need adapting for the data source or check type. You can expand to more datasets when you are ready.

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

Add Soda when you need to manage quality across sources, reuse rules across datasets or let business teams contribute checks. Native checks can remain part of your setup. Soda gives your teams a shared way to manage requirements, monitor quality and investigate issues across your data 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

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
  • Soda runs inside your own network
  1. 1You
  2. 2Your company
  3. 3Your time