Semantic Model Refresh Not Reflecting Latest Lakehouse Data using data pipeline

Thank you v-lgarikapat 

I'm currently exploring an alternative solution and debating whether to implement that or revert to the PySpark script, which simply triggers the semantic refresh. I initially used that script but later transitioned to the semantic refresh activity, thinking it would be a more robust approach.

However, my semantic model refresh is currently taking over an hour to process 44 million rows. If I end up triggering it twice, I'm not sure this will be a feasible solution from a performance standpoint.

Thanks again, Krishna