
Working in a Jupyter notebook in Azure Data Studio

Jupyter notebooks provide a nice way to combine Markdown-formatted documentation with live code that you can execute directly within the notebook.
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The Git integration is particularly notable in today's DevOps-central software development landscape.
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Maintenance: Tools for managing database maintenance tasks, such as index defragmentation and statistics update.Security: Tools for managing users, roles, and permissions, and for implementing security policies.Backup and Restore: Create database backups and restore them in the event of data loss.Object Explorer: Hierarchical view of all the objects within a SQL Server instance, including databases, tables, views, and stored procedures.Query Editor: Write and execute T-SQL queries and scripts.SQL Server Management Studio has a wide range of capabilities to support Windows-based Microsoft relational database architects, administrators, and developers. SSMS integrates nicely with Azure Data Studio Feature set I particularly like how you can start your work in SSMS and pick it up in a Jupyter notebook in Azure Data Studio that's a nice touch. Newer versions of SSMS also have native integration with Azure Data Studio, which you can see in the following screenshot. Note that you can use SSMS to connect to Azure SQL Database and Azure Synapse SQL pools, as well as local SQL Server databases. For example, being able to work in the Cosmos DB outside the Azure portal would be useful.īy contrast, SSMS is a more fully featured tool that is intended for DBAs and other IT professionals who need to manage and configure SQL Server instances. Personally, I was disappointed that Microsoft chose not to embrace NoSQL databases in Azure Data Studio. Open source software like Azure Data Studio symbolizes the new Microsoft Target audienceĪzure Data Studio is focused on providing a streamlined experience for data professionals and developers working with SQL Server, Azure SQL Database, and Azure Synapse SQL pools.
