Event Hubs Capture is the easiest way to automatically deliver streamed data in Event Hubs to an Azure Blob storage or Azure Data Lake store. You can subsequently process and deliver the data to any other storage destinations of your choice, such as SQL Data Warehouse or Cosmos DB.
You to capture data from your event hub into a SQL data warehouse by using an Azure function triggered by an event grid.
Example:

First, you create an event hub with the Capture feature enabled and set an Azure blob storage as the destination. Data generated by WindTurbineGenerator is streamed into the event hub and is automatically captured into Azure Storage as Avro files.
Next, you create an Azure Event Grid subscription with the Event Hubs namespace as its source and the Azure Function endpoint as its destination.
Whenever a new Avro file is delivered to the Azure Storage blob by the Event Hubs Capture feature, Event Grid notifies the Azure Function with the blob URI. The Function then migrates data from the blob to a SQL data warehouse.
References:
https://docs.microsoft.com/en-us/azure/event-hubs/store-captured-data-data-warehouse

Explanation

Step 1: Create an Azure Machine Learning Studio workspace
Step 2: Create a notebook
You can manage notebooks using the UI, the CLI, and by invoking the Workspace API.
To create a notebook
* Click the Workspace button Workspace Icon or the Home button Home Icon in the sidebar. Do one of the following:
Next to any folder, click the Menu Dropdown on the right side of the text and select Create > Notebook.
Create Notebook
In the Workspace or a user folder, click Down Caret and select Create > Notebook.
2. In the Create Notebook dialog, enter a name and select the notebook's primary language.
3. If there are running clusters, the Cluster drop-down displays. Select the cluster to attach the notebook to.
4. Click Create.
Step 3: Create a new experiment
Create a new experiment by clicking +NEW at the bottom of the Machine Learning Studio window. Select EXPERIMENT > Blank Experiment.
References:
https://docs.azuredatabricks.net/user-guide/notebooks/notebook-manage.html
https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-run-cloud-notebook

Explanation

Box 1: Export Data
The Export data to Hive option in the Export Data module in Azure Machine Learning Studio. This option is useful when you are working with very large datasets, and want to save your machine learning experiment data to a Hadoop cluster or HDInsight distributed storage.
Box 2: Apache Hive
Apache Hive is a data warehouse system for Apache Hadoop. Hive enables data summarization, querying, and analysis of data. Hive queries are written in HiveQL, which is a query language similar to SQL.
Box 3: Azure Data Lake
Default storage for the HDFS file system of HDInsight clusters can be associated with either an Azure Storage account or an Azure Data Lake Storage.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-to-hive-query
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/hdinsight-use-hive

Explanation
References:
https://docs.microsoft.com/en-us/azure/data-factory/introduction

Explanation
Instead add an SSH key to the node, and then you create an SSH connection.
References:
https://docs.microsoft.com/en-us/azure/aks/ssh