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Normalization typically means rescales the values into a range of [0,1]. Standardization typically means rescales data to have a mean of 0 and a standard deviation of 1 (unit variance).A link which explains better is - https://towardsdatascience.com...

https://docs.microsoft.com/en-us/azure/databricks/scenarios/frequently-asked-questions-databricks#solution-1

By default, each streaming query generates a unique group ID for reading data ( ensuring it's own  its own consumer group ) . In scenarios where you'd want to specify it (authz etc ) , it is not recommended to have two streaming applications specify ...

As indicated there are ways to manage the amount of data being sampled for inferring schema. However as a best practice for production workloads its always best to define the schema explicitly for consistency, repeatability and robustness of the pipe...

Yes Convert to Delta allows for converting a parquet table into Delta format in place by adding a transaction log, infering the schema and also collecting stats to improve query performance - https://docs.databricks.com/spark/latest/spark-sql/languag...

Databricks SQL also integrates with several popular BI tools over JDBC/ODBC which you can use as a mechanism to embed visualizations into a webpage

See this docs article for details on setting up a Delta Presto integration - https://docs.databricks.com/delta/presto-integration.html

You can use libraries such as Seaborn, Bokeh, Matplotlib, Plotly for visualization inside of Python notebooks. See https://docs.databricks.com/notebooks/visualizations/index.html#visualizations-in-pythonAlso, Databricks has its own built-in visualiza...