Soda Data Quality

As data pipelines grow in complexity, data testing and data observability have become crucial concepts for maintaining data reliability. Data testing involves validating datasets against predefined expectations to ensure quality and stability, using automated checks for schema, freshness, volume, and business rules within data processing workflows. However, testing alone is insufficient as it only confirms known conditions; this is where data observability comes into play. Data observability continuously monitors data behavior across systems to detect unexpected changes, anomalies, and operational issues that may not have been predefined, thus providing a broader visibility into pipeline behavior and system health. Modern data teams increasingly rely on both methodologies, leveraging data contracts—a single, version-controlled YAML specification co-authored by engineers and business users—to tie them together. This integrated approach allows for precise validation of known data requirements while simultaneously catching unforeseen issues across the data ecosystem, ensuring a more reliable data infrastructure.