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Merge pull request #302888 from EdB-MSFT/lake-updates-1707-1
updated notebook result png
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articles/sentinel/TOC.yml

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- name: Run KQL queries
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href: datalake/kql-queries.md
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displayName: data lake
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- name: Sample data lake queries
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href: datalake/kql-samples.md
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displayName: data lake
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- name: Create KQL jobs
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href: datalake/kql-jobs.md
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displayName: data lake

articles/sentinel/datalake/kql-queries.md

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| take 100
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```
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## Sample KQL queries
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For sample queries, see [KQL sample queries for the data lake](kql-samples.md). You can use these queries as a starting point and modify them to suit your requirements.
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## Query considerations and limitations
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+ Queries are run against a single workspace. Make sure you select the correct workspace before running a query.

articles/sentinel/datalake/kql-samples.md

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articles/sentinel/datalake/notebooks.md

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For sample notebooks that demonstrate how to interact with the Microsoft Sentinel data lake, see [Sample notebooks for Microsoft Sentinel data lake](./notebook-examples.md).
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## Use GitHub Copilot in notebooks
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## Microsoft Sentinel Provider class
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Use GitHub Copilot to help you write code in notebooks. GitHub Copilot provides code suggestions and autocompletion based on the context of your code. To use GitHub Copilot, ensure that you have the [GitHub Copilot extension](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot) installed in Visual Studio Code.
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To connect to the Microsoft Sentinel data lake, use the `SentinelLakeProvider` class.
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Copy code from the [Sample notebooks for Microsoft Sentinel data lake](./notebook-examples.md) and save it in your notebooks folder to provide context for GitHub Copilot. GitHub Copilot will then be able to suggest code completions based on the context of your notebook.
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The following example shows GitHub Copilot generating a code review.
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:::image type="content" source="./media/notebooks/copilot.png" lightbox="./media/notebooks/copilot.png" alt-text="A screenshot showing GitHub Copilot generating a code review.":::
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## Microsoft Sentinel Provider class
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To connect to the Microsoft Sentinel data lake, use the `SentinelLakeProvider` class.
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This class is part of the `access_module.data_loader` module and provides methods to interact with the data lake. To use this class, import it and create an instance of the class using a `spark` session.
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```python

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