# in order to get the string representation of the options - as pyspark does not support calling properties of. # In this example the Kusto Spark connector will determine the optimal path to get data: API for small data sets, Export/Distributed mode for large datasets.
The code resides in a private repository and is integrated in azure synapse. An Introduction to Using Python with Microsoft Azure 4 Figure 2 Once you click OK, you should see the development environment.To open an interactive window, select the Tools menu, select Python Tools, and then select the Interactive menu item. Use the _base function and create a new class with some or all of the fields (columns) defined. After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Resources table). 
Declare a Mapping Class for Azure Data Lake Storage Data. This will let Synapse authenticate to Azure Key Vault using the Synapse. Also, make sure that the Synapse workspace managed service identity (MSI) has Secret Get privileges on your Azure Key Vault. It gives you a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs. Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. We'll walk through a quick demo on Azure Synapse Analytics, an integrated platform for analytics within Microsoft Azure cloud. 
In this video, I share with you about Apache Spark using the Python language, often referred to as PySpark. For dedicated SQL Pool you can use default linked services. Note: If you have created dedicated SQL pool inside Synapse analytics workspace then you don't have to follow below steps to configure Managed identity.
In this blog, we will cover steps to follow to configure Managed indentity when classical SQL DW is created under SQL Server in Azure.
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