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Thanks for your response. Can business user without the help of any script modify any rows in the table after loading it onetime from CSV fiels?

Here are the supported data types for the Feature Store:https://docs.databricks.com/applications/machine-learning/feature-store/feature-tables.html#supported-data-typesAs you can see, image is not between them, but you could use BinaryType.

Check out Part 2 of this Data Ingestion webinar to find out how to easily ingest semi-structured data at scale into your Delta Lake, including how to use Databricks Auto Loader to ingest JSON data into Delta Lake.

Not yet, but stay-tuned it's being cooked in the kitchen

I can find a link which can help https://docs.databricks.com/dev-tools/databricks-connect.html

Yes.Please refer to our docshttps://docs.databricks.com/applications/machine-learning/manage-model-lifecycle/multiple-workspaces.html

At the moment, it's really just xgboost, and sklearn implemenations like random forests, logistic regression, and linear regression as applicable. More possibilities are coming.

It depends on what you mean, but if you're just trying to (say) tokenize and process data with spacy in parallel, then that's trivial. Write a 'pandas UDF' function that expresses how you want to transform data using spacy, in terms of a pandas DataF...

You can use explain to see what type of physical and logical plans are getting created . This is the best way to see difference , but as mentioned in the question , it should give the same physical plan

The following resources provide more detail on this:Databricks model registry example notebook: https://docs.databricks.com/_static/notebooks/mlflow/mlflow-model-registry-example.htmlDatabricks model lifecycle - https://docs.databricks.com/applicatio...