Soda

Detect and Prevent Data Issues at Lakehouse Scale

Soda brings AI-powered data observability, collaborative data contracts, and pipeline testing natively to your Databricks environment so teams can detect, resolve, and prevent data quality issues at scale.

High-performing data teams fueled by Soda on Databricks:

Why teams choose Soda on Databricks

  • The fastest, and most accurate anomaly detection: Detect anomalies up to 70% faster and more accurately on Databricks than any Facebook Prophet-based system.
  • Turn anomalies into data contracts: Convert recurring anomalies into collaborative, enforceable data contracts that keep quality high release after release.
  • Test data early: Embed pipeline testing in Databricks notebooks and CI/CD so bad data never reaches production.
  • See exactly what broke: Go from symptoms to record-level resolution with rich context, giving teams pinpoint visibility into exactly which rows fail data quality rules.
  • Nothing leaves your lakehouse: Manage observability, testing, and contracts with Soda while data stays inside Databricks.

From detection to prevention. Start right, shift left.

Start right. Monitor at lakehouse scale.
  • High-speed anomaly detection purpose-built for Databricks helps you monitor metrics across all your tables and data products.
  • With built-in backfilling, Soda instantly analyzes historical metadata and metric trends, so teams gain immediate visibility into past data quality metrics and can uncover anomalies that might have previously gone unnoticed.
  • Soda's smart alerting system provides the highest anomaly accuracy paired with explanations to avoid alert fatigue. Resolve detected anomalies, then prevent issues from happening again. otebooks and CI/CD so bad data never reaches production.
Shift left. Prevent critical issues.
  • Catch and fix data quality issues before they impact production by embedding Soda checks directly into your Databricks notebooks and CI/CD workflows.
  • Define checks directly as code or through an intuitive no-code UI, making data quality accessible for all, from engineering to operations.
  • Enforce checks automatically as data contracts and ensure every dataset meets agreed-upon standards between data producers and consumers—reducing costly rework and protecting downstream analytics, dashboards, and AI models.
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Built for the Lakehouse

  • Runs natively in Databricks: Execute quality checks right where your data lives and tap directly into Unity Catalog for seamless governance and lineage.
  • Integrates with Unity catalog: Connect Soda with your Databricks workspace, run checks from notebooks or jobs, and write the results back to Unity.
  • Keeps everyone in the loop: Send alerts to Slack, Teams, Jira, ServiceNow, or the tools your teams already use, and work together to resolve issues fast.
  • Security first: Only quality rules and metadata leave Databricks, your raw data stays safely in your environment.

FAQ

  • Does data leave Databricks when using Soda?
    ‍
    No. You can manage observability, testing, and contracts without data leaving your Databricks environment.
  • How is Soda different from generic observability tools?
    ‍
    Soda is built for both detection and prevention—combining anomaly detection, pipeline testing, and collaborative data contracts in one platform.
  • How accurate/fast is the anomaly detection?
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    Soda’s algorithms detect anomalies up to 70% faster and more accurately than Prophet-based systems.
  • Can I author checks in notebooks and automate them?
    ‍
    Yes. Use Soda in Databricks notebooks, then automate in jobs/CI; results flow to Soda Cloud for collaboration and auditability.
  • Do you support Unity Catalog?
    ‍
    Absolutely. For example, you can run data ingestion checks after data is ingested into the Unity catalog.
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See Soda on Databricks in Action

Experience how AI-powered data observability, testing, and contracts work natively inside your Databricks Lakehouse. Watch anomalies surface in minutes, turn them into guardrails, and safeguard every downstream dashboard, model, and report.
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