Soda
Enables data teams to proactively detect, resolve, and prevent data quality issues through automated monitoring and testing, ensuring reliable data for analytics and operations.
Last updated Feb 8, 2026
- Industry
- Data Infrastructure/Data Quality (NOT AdTech)
- Business Model
- SaaS
- Target Market
- Mid-Market to Enterprise
- Employee Count
- 51-200
- Funding
- $71M
- API Available
- Yes
Market Position
Leading data quality and observability platform for modern data stacks
Overview
Soda is NOT an AdTech company. It is a data quality and observability platform that serves data engineering and analytics teams across various industries. The company provides tools to monitor, test, and ensure data reliability throughout the data lifecycle, helping organizations detect and resolve data quality issues before they impact business decisions. Soda integrates with modern data infrastructure including cloud data warehouses, data lakes, and data pipelines. While Soda could be used by AdTech companies to maintain data quality for their advertising metrics and analytics, it is not positioned within the AdTech ecosystem. The platform is industry-agnostic and serves any organization that relies on data quality for analytics, operations, and decision-making. Soda competes in the data observability and data quality management space alongside companies like Monte Carlo Data, Bigeye, and Great Expectations.
Products & Features
Soda Cloud
Cloud-based data quality monitoring and collaboration platform
Soda Core
Open-source data quality testing framework
Soda Checks
Data quality test definitions using Soda Checks Language (SodaCL)
Data Contracts
Define and enforce data quality agreements between data producers and consumers
Key Features
Automated data quality monitoringAnomaly detectionData profiling and discoveryCustom data quality checksIncident management and alertingData lineage integrationCollaboration tools for data teamsSQL-based testing
Use Cases
Data pipeline monitoringData warehouse quality assuranceAnalytics data validationData migration testingRegulatory compliance data checksML data quality monitoring
Customer Segments
Data engineering teamsAnalytics teamsData platform teamsEnterprise data organizationsTechnology companies