Note: After downloading Azure Data Studio, say Yes to enabling preview features so that you can use extensions.
Azure Data Studio is a multi-database, cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. To learn more, visit our GitHub.
The key highlights to cover this month include:
- Announcing Redgate SQL Prompt extension
- Announcing the new machine learning extension
- Added new Python dependencies wizard
- Added support for parameterization for Always Encrypted
- Improvements to the notebook markdown toolbar
- Bug fixes
For a list of complete updates, refer to the Azure Data Studio release notes.
Announcing Redgate SQL Prompt extension
Redgate has recently launched a preview of SQL Prompt for Azure Data Studio, so you can now use their powerful formatting tool to write and format SQL in Azure Data Studio.
The extension lets you manage formatting styles directly within Azure Data Studio, so you can create and edit your styles without leaving the IDE. If you’re already a SQL Prompt user, you can import your existing formatting styles from SQL Server Management Studio.
SQL Prompt for Azure Data Studio includes an extensive collection of code snippets to write your SQL code quickly and efficiently, and existing snippets can also be imported from SQL Server Management Studio.
Announcing the new machine learning extension
The machine learning extension is now available to download from Azure Data Studio Marketplace. To obtain the extension, search for machine learning in the extension viewlet in Azure Data Studio, and click install.
After installation, this extension will be accessible from the server dashboard of your SQL Server or Azure SQL Edge preview instances.
This extension enables you to:
- Manage Python and R packages with SQL Server machine learning services with Azure Data Studio.
- Use ONNX model to make predictions in Azure SQL Edge.
- View ONNX models in an Azure SQL Edge database.
- Import ONNX models from a file or Azure Machine Learning into Azure SQL Edge database.
- Create a notebook to run experiments.
Here’s a quick gif to make predictions against Boston dataset and using an ONNX model. Both the dataset and the model in this example are stored in the database in Azure SQL Edge.
To learn more about this extension, see the machine learning extension for Azure Data Studio.
Added new Python dependencies wizard
Managing Python dependencies can be challenging. In addition, we want users to know what Python packages are installed when supporting a new kernel. In the May release, we have added a new Python dependencies wizard to help manage the Python packages when supporting a new notebook kernel.
The wizard will pop up when you change to a kernel that is not SQL, such as Python. From the wizard, you can choose a fresh installation of Python or an existing Python install on your machine. Finally, you can select all kernels to install on your machine, or you can pick and choose which kernels you want to download at this time.
This wizard should help you understand what packages are being installed on your machine.
Added support for parameterization for Always Encrypted
Parameterization for Always Encrypted is a feature that allows you to run queries that insert data into encrypted columns or filter by encrypted columns. When parameterization for Always Encrypted is enabled, Azure Data Studio automatically converts Transact-SQL variables into query parameters (instances of SqlParameter Class). This allows the underlying Microsoft .NET data provider for SQL Server that Azure Data Studio uses to transparently detect data targeting encrypted columns, and to encrypt such data before sending it to the database.
For more information, see query columns using Always Encrypted with Azure Data Studio.
Improvements to the markdown toolbar
The markdown toolbar for Azure Data Studio notebooks has received a lot of positive feedback. This month, we added underline support to the toolbar. We will add more toolbar actions based on community feedback, so make sure to submit a GitHub issue if you have a request.
If you would like to help make Azure Data Studio a great product, share any feedback or report issues through our Issues page. Our engineering team is regularly going through the untriaged issues and assigning issues into different monthly milestones so that you know we are working on it. Your votes on issues help us prioritize.
A full list of bug fixes for the May release can be found here.
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