Databricks

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...

If you're question is about using the Databricks API from within a databricks notebook, then the answer is yes of course, you can definitely orchestrate anything and invoke the REST API from a python notebook using the `requests` library already bake...

Here is the support matrix for import/export operations for databricks-syncAlso checkout https://github.com/databrickslabs/migrate

When using delta tables in databricks, you have the advantage of delta cache which accelerates data reads by creating copies of remote files in nodes’ local storage using a fast intermediate data format. At the beginning of each query delta tables au...

Start an endpointRun a queryGo to Query HistoryClick Details, Go to the Environment tabSearch sparkVersion.