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This preview release is the beginning of a strategic journey to bring rich native Kusto (KQL) experiences in Azure Data Studio.

Using KQL magic in Azure Data Studio notebooks.

Connecting to Azure Data Explorer cluster in Azure Data Studio, and writing KQL queries.

This also enables users to add these files as part of their CI/CD pipelines in GitHub or Azure DevOps. Now, users can take advantage of adding their KQL files and KQL notebook files to their Git repositories. Enriching your DevOps flow with KQL filesĪzure Data Studio supports a Git source control manager (SCM). For example, diagnosis steps and pattern or anomaly detections may be expressed as notebooks with Kusto kernel, and mitigation notebooks in PowerShell or other kernels.

Microsoft azure storage explorer download for mac how to#

These runbooks or playbooks, detailing how to troubleshoot apps via telemetry data and how to mitigate, can be stored as notebooks with different kernel types, organized as a Jupyter Book. Improved DevOps troubleshooting experience with KQL notebooksĮngineers working on apps with telemetry connected to Azure Data Explorer can easily create a troubleshooting runbook or playbook in Azure Data Studio with Kusto kernel.

When writing KQL queries in code cells, users can also be more productive with the IntelliSense support in Notebooks.īelow is an example of pattern detection in Storm Events data using autocluster plugin in Kusto notebook in Azure Data Studio accessing data from Azure Data Explorer databases:ģ. Notebooks provide the benefits of being able to capture code, results and context on the analysis. By supporting KQL natively with IntelliSense, users can benefit from optimized experience for fast and rich functionalities on a large amount of real-time streaming datasets in Azure Data Explorer.įor more interactive data exploration, users can visualize the resultset from the KQL query in SandDance.Ĭombined with the Kusto kernel addition to Notebook in Azure Data Studio, it makes it easy to create reproducible analyses in notebooks.

Users working with heterogeneous data sources can now do data exploration and data analysis from SQL and Big Data Clusters to Azure Data Explorer without breaking their flow. Efficiency in data exploration and data analysis Here are four key benefits of using Kusto (KQL) extension in Azure Data Studio: 1. Users can now connect and browse their Azure Data Explorer clusters and databases, write and run KQL, as well as author notebooks with Kusto kernel, all equipped with IntelliSense.īy enabling native Kusto (KQL) experiences in Azure Data Studio, users such as data engineers, data scientists, or data analysts can now quickly discover insights as well as identify trends and anomalies against a massive amount of data stored in Azure Data Explorer. This native Kusto (KQL) support brings another modern data experience to Azure Data Studio, a cross-platform client – for Windows, macOS, and Linux. The Kusto (KQL) extension in Azure Data Studio is now available in preview.