data-observability

Data quality monitoring catches bad data like nulls, duplicates, and schema drift before they break pipelines. You can establish validation suites using Great Expectations, Soda checks, or custom SQL to enforce quality rules and prevent downstream analytics failures.

The best way to detect data anomalies is by implementing baseline profiling and continuous checks. This monitors source schema changes, volume spikes, and distribution shifts, allowing you to catch anomalous distributions before they impact downstream analytics.

Yes, data observability supports SLA-based tiered alerting. You can define specific thresholds and operational guardrails so that warnings and critical failures are handled differently, ensuring appropriate responses to various data quality issues.

Yes, you can use Great Expectations and Soda checks together for continuous data validation. The approach supports generating monitoring logic and selecting appropriate tools for quality versus freshness use cases, providing integration guidance for your orchestrator.

To create a Great Expectations suite, design the validation logic including quality rules, baseline metrics, and integration points for alerting. This establishes infrastructure-level data validation to enforce quality rules and monitor your critical datasets continuously.