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1. To have an abstraction layer of query engine
   The current spark implementation limited Griffin's adoption, since a company could use other query engine in company level. It's too heavy to set up a spark environment to run Griffin.
   
   
2. To support a common data quality workflow: measure-monitor-alert
   In most of real scenarios, measuring is not the goal, but measure-monitor-alert workflow.
     user should be able to define an use case, including:
       1. to measure a dq metrics
       2. to evaluate the monitoring dq trigger
       3. to define the alerting action
       4. to integrate the 3-steps into a single job/UoW (by scheduler)
   
   

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