![]() For more information about using the Azure Cloud Shell, see the Azure Cloud Shell documentation. Note that you can resize the cloud shell by dragging the separator bar at the top of the pane, or by using the -, ◻, and X icons at the top right of the pane to minimize, maximize, and close the pane. Note: If you have previously created a cloud shell that uses a Bash environment, use the the drop-down menu at the top left of the cloud shell pane to change it to PowerShell. The cloud shell provides a command line interface in a pane at the bottom of the Azure portal, as shown here: Use the button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a PowerShell environment and creating storage if prompted. In this exercise, you’ll use a combination of a PowerShell script and an ARM template to provision an Azure Synapse Analytics workspace. You can use the built-in serverless SQL pool to query files in the data lake. You’ll need an Azure Synapse Analytics workspace with access to data lake storage. Provision an Azure Synapse Analytics workspace You’ll need an Azure subscription in which you have administrative-level access. This lab will take approximately 40 minutes to complete. ![]() Azure Synapse Analytics provides serverless SQL pools that enable you to decouple the SQL query engine from the data storage and run queries against data files in common file formats such as delimited text and Parquet. ![]() As organizations increasingly take advantage of scalable file storage to create data lakes, SQL is often still the preferred choice for querying the data. Most data analysts are proficient in using SQL queries to retrieve, filter, and aggregate data - most commonly in relational databases. ![]() SQL is probably the most used language for working with data in the world. ![]()
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