![]() Azure Data StudioĪzure Data Studio is basically a database tool that is cross-platform routinely used by data engineers and professionals for both on-premises and cloud services throughout the operating system spectrum from Windows to macOS and Linux. You can also get introduced to Azure Data Studio from the previous article, Azure Data Studio and learn how to install it. You can learn it from the previous articles, How To Create, Query And Delete Database In Azure SQL and Create Single Serverless Database. First of all, we need to create a Azure SQL Database and a SQL Server. In following articles, we'll learn to create tables, query data, view the data and then delete the table. Please feel free to submit your suggestions and bugs on GitHub.In this article, we’ll learn to connect and access the Azure SQL Database through the Azure Data Studio. This preview release is the beginning of a strategic journey to richer end-to-end DevOps with Data in Azure Data Studio. Create diagnostic settings to send platform logs and metrics to different destinations.Azure Monitor Logs Extension for Azure Data Studio (preview). ![]() Other related extensions: Kusto (KQL) extension which works for Azure Data Explorer How to get started Users can also add these files as part of their CI/CD pipelines in GitHub or Azure DevOps. Version control the queries and notebooks directly with git in Azure Data Studio.This is illustrated in the previous examples. Improved DevOps troubleshooting experience with Azure Monitor Logs notebooks.Reproducible analysis and diagnosis with notebooks.Users can also use Sand Dance extension to further enhance their data exploration and analysis. Users now have access to their SQL data sources in the same place as their Log Analytics workspaces. Efficiency in data exploration and data analysis.Here are four key benefits of using Azure Monitor Logs extension in Azure Data Studio. A good question would be to understand why error 208 happens a lot. In this example, we see a timeline with two-day intervals that maps error count by error number. | evaluate pivot(ErrorNumber, sum(event_count)) | summarize event_count=count() by EventTime = bin(TimeGenerated, 2d), ErrorNumber | extend ErrorNumber = toint(error_number_d) Copy the following query and paste it to Query editor in Azure Data Studio. Right-click and choose New Notebook from the menu. In this example, a good question to ask might be, why are these users executing a lot of drops during this time? Example Two: What type of errors happen?Ĭonnect to your Log Analytics workspace that captures the Azure SQL error events. | project TimeGenerated, Category, OperationName, server_principal_name_s, statement_s | where statement_s contains "DROP TABLE" | where action_name_s = "BATCH COMPLETED" ![]() Right-click and choose New Query from the menu. Example One: Who dropped a table in my database?Ĭonnect to your Log Analytics workspace that captures the Azure SQL audit events. Now, let’s dive into these examples where users can leverage Log Analytics workspace data to better understand an Azure SQL database. While the examples in this section are specific to Azure SQL scenarios, Azure Monitor Logs can also capture events from other Azure resources, such as Azure Functions or Azure Web Apps. This native Azure Monitor Logs extension allows Azure service customers to also author notebooks with Log Analytics kernel, all equipped with Intellisense. Data professionals who are using Azure SQL, Azure PostgreSQL, or Azure Data Explorer in Azure Data Studio can access the data in the Log Analytics workspace for diagnosis or auditing in that same development environment. ![]() ![]() By installing native Azure Monitor Logs extension in Azure Data Studio, users can connect, browse, and query against Log Analytics workspace. The extension is supported in Azure Data Studio August 2021 release, v1.32.0.Īdministrators can enable platform logging and metrics to one of their Azure services such as Azure SQL and set the destination to Log Analytics workspace. The Azure Monitor Logs extension in Azure Data Studio is now available in preview. ![]()
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