A great experience that combines ML-Runtimes - MLFlow and Spark. The ability to use Python, and SQL seamlessly in one platform. Since databricks notebooks can be saved as python scripts in the background it is amazing to have both notebook and script...
Too many customizations are needed to achieve the right mix of parameterization for optimal performance. On the other hand, snowflake provides lots of features out of the box without the developer worrying about these things.
I am using DBT more than 1 year. Since the first day I started using it I like it's many things: 1) Simplicity: It is very simple. We just need to write SELECT quries to achive the whole design. So after the development, 2) Version Control: DBT used to...
we can not able to load the data from source , we can only able to use data present in dataware houses, new users may face difficulties while learning, support also not that good from community
A great experience that combines ML-Runtimes - MLFlow and Spark. The ability to use Python, and SQL seamlessly in one platform. Since databricks notebooks can be saved as python scripts in the background it is amazing to have both notebook and script...
I am using DBT more than 1 year. Since the first day I started using it I like it's many things: 1) Simplicity: It is very simple. We just need to write SELECT quries to achive the whole design. So after the development, 2) Version Control: DBT used to...
Too many customizations are needed to achieve the right mix of parameterization for optimal performance. On the other hand, snowflake provides lots of features out of the box without the developer worrying about these things.
we can not able to load the data from source , we can only able to use data present in dataware houses, new users may face difficulties while learning, support also not that good from community