Soda Data Quality provides an AI-powered platform for data observability and quality enforcement across the data lifecycle. It enables engineers and business users to collaborate on data contracts to detect and resolve data incidents before they impact production. The platform offers automated monitoring, anomaly detection, and diagnostics to ensure data reliability at scale.
Funding
$37.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data teams often struggle to maintain data integrity across complex data pipelines, leading to unreliable data products and inconsistent insights. Existing data quality checks are often implemented too late in the pipeline, failing to prevent the propagation of errors downstream. This results in stakeholders questioning the accuracy and trustworthiness of the data.
Solution
Soda provides a GenAI-first platform that enables data teams to embed declarative data quality checks directly into their existing data pipelines, ensuring data integrity at every stage. The platform offers both out-of-the-box observability and declarative testing capabilities, allowing users to monitor data quality health in a way that best suits their needs. By testing data early and often, Soda prevents downstream data quality issues, ensuring that stakeholders can rely on accurate and consistent data products. Soda also offers collaborative data contracts to turn detected anomalies into shared agreements between data producers and consumers.
Target Audience
Soda is designed for data engineers, analytics engineers, data product managers, and data leaders who need to ensure the reliability, accuracy, and consistency of data across their organization.
Features
- Declarative data quality checks that can be embedded into existing data pipelines and CI/CD workflows
- Data observability features to monitor data in production, raise alerts, and debug data and pipelines
- No-code checks that empower business users to contribute to data quality and maintain standards using Soda AI assistants
- Operational data quality features to streamline data processes and route issue alerts to the appropriate data owners
- Integration with tools such as Databricks, dbt, and AWS
- Collaborative Data Contracts for streamlined collaboration across teams
- Automated metric tracking and anomaly detection
- Ability to track and score the health of data across core quality dimensions, test data each and every time it’s transformed, and quarantine bad data