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- Option of edge pipeline in your own environment to provide high-end scalability, layered network configurations, and periodic connectivity.
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## Azure Monitor pipeline
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Data collection using the Azure Monitor pipeline is shown in the diagram below. All data is processed through the *cloud pipeline*, which is automatically available in your Azure subscription and requires no configuration. Each collection scenario is defined in a [data collection rule (DCR)](#data-collection-rules). Some environments may choose to implement a local edge pipeline to manage data collection before it's sent to the cloud. See [edge pipeline](#edge-pipeline) for details on this option.
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Data collection using the Azure Monitor pipeline is shown in the diagram below. All data is processed through the *cloud pipeline*, which is automatically available in your Azure subscription and requires no configuration. Each collection scenario is defined in a [data collection rule (DCR)](#data-collection-rules). Some environments may choose to implement a local *edge pipeline* to manage data collection before it's sent to the cloud. See [edge pipeline](#edge-pipeline) for details on this option.
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:::image type="content" source="media/pipeline-overview/pipeline-overview.png" lightbox="media/pipeline-overview/pipeline-overview.png" alt-text="Diagram that shows the data flow for Azure Monitor pipeline." border="false":::
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## Data collection rules
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Data collection rules (DCRs) are sets of instructions supporting data collection using the [Azure Monitor pipeline](./pipeline-overview.md). They provide a consistent and centralized way to define and customize different data collection scenarios. Depending on the scenario, DCRs specify such details as what data should be collected, how to transform that data, and where to send it.
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Data collection rules (DCRs) are sets of instructions supporting data collection using the Azure Monitor pipeline. They provide a consistent and centralized way to define and customize different data collection scenarios. Depending on the scenario, DCRs specify such details as what data should be collected, how to transform that data, and where to send it.
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DCRs are stored in Azure so that you can centrally manage them. Different components of a data collection workflow will access the DCR for particular information that it requires. In some cases, you can use the Azure portal to configure data collection, and Azure Monitor will create and manage the DCR for you. Other scenarios will require you to create your own DCR. You may also choose to customize an existing DCR to meet your required functionality.
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For example, the following diagram illustrates data collection for the [Azure Monitor agent](../agents/azure-monitor-agent-overview.md) running on a virtual machine. In this scenario, the DCR specifies events and performance data to collect, which the agent uses to determine what data to collect from the machine and send to Azure Monitor. Once the data is delivered, the data pipeline runs the transformation specified in the DCR to filter and modify the data and then sends the data to the specified workspace and table. DCRs for other data collection scenarios may contain different information.
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:::image type="content" source="media/data-collection-rule-overview/overview-agent.png" lightbox="media/data-collection-rule-overview/overview-agent.png" alt-text="Diagram that shows basic operation for DCR using Azure Monitor Agent." border="false":::
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## Data collection rule associations
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Some data collection scenarios will use data collection rule associations (DCRAs), which associate a DCR with an object being monitored. A single object can be associated with multiple DCRs, and a single DCR can be associated with multiple objects. This allows you to manage a single DCR for a group of objects.
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For example, the diagram above illustrates data collection for the Azure Monitor agent. When the agent is installed, it connects to Azure Monitor to retrieve any DCRs that are associated with it. You can create an association with to the same DCRs for multiple VMs.
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For example, the following diagram illustrates data collection for the [Azure Monitor agent](../agents/azure-monitor-agent-overview.md) running on a virtual machine. When the agent is installed, it connects to Azure Monitor to retrieve any DCRs that are associated with it. You can create an association with to the same DCRs for multiple VMs. In this scenario, the DCR specifies events and performance data to collect, which the agent uses to determine what data to collect from the machine and send to Azure Monitor. Once the data is delivered, the data pipeline runs the transformation specified in the DCR to filter and modify the data and then sends the data to the specified workspace and table. DCRs for other data collection scenarios may contain different information.
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:::image type="content" source="media/data-collection-rule-overview/overview-agent.png" lightbox="media/data-collection-rule-overview/overview-agent.png" alt-text="Diagram that shows basic operation for DCR using Azure Monitor Agent." border="false":::
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## Transformations
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*Transformations* allow you to modify incoming data before it's stored in Azure Monitor. They are [KQL queries](../logs/log-query-overview.md) defined in the DCR that run in the cloud pipeline. See [Data collection transformations in Azure Monitor](./data-collection-transformations.md) for details on how to create and use transformations.
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