...

Griffin is a generic framework to enable user to measure and monitor the data quality in an easy and extensive manner.

...

Griffin

...

What are the pain points we are facing in the current edition of Griffin from the architectural perspective?

...

0.7.0 problems

...

  - Incomplete and Inflexible Data Quality Definition: The current definition of data quality lacks completeness and flexibility. A comprehensive data quality rule should encompass recording metrics, anomaly detection, and actionable steps such as alerting.

 - Rigid Triggering Mechanism: The triggering mechanism for measures exhibits rigidity. Integration with the scheduler in enterprise production environments needs to be seamless and deeply integrated.

 - Over Reliance on Internal Data Comparison: The measure implementation overly depends on its own data comparison methods, neglecting the optimization capabilities inherent in the engine. There's a need to leverage the engine's optimization features more effectively. We need to focus on data quality benchmarks, rather than optimization queries.

 - Configurability of Gateway: To enhance flexibility, the gateway between Apache Griffin and the engine should be configurable. This ensures compatibility with popular gateways such as Trino, Kyuubi, etc.

 - Lack of Default Alert Channels: Currently, there is a deficit in default alert channels. Providing default channels such as Slack, WeChat, etc., is essential to ensure timely communication of alerts.

 - Absence of Anomaly Detection Module: An anomaly detection module is conspicuously absent. Presently, our thresholds are statically configured, indicating a need for dynamic anomaly detection capabilities.

Next generation Griffin architecture considerations

...