This blog teaches you how to monitor and troubleshoot data pipelines using Azure Data Factory tools and features such as the monitoring dashboard, alerts, and run details.
1. Introduction
Azure Data Factory is a cloud-based data integration service that allows you to create, manage, and orchestrate data pipelines. Data pipelines are workflows that move and transform data from various sources to various destinations. Data pipelines can be used for various purposes, such as data ingestion, data transformation, data analysis, and data visualization.
However, creating data pipelines is not enough. You also need to monitor and troubleshoot them to ensure they are running smoothly and efficiently. Monitoring and troubleshooting data pipelines can help you identify and resolve issues, optimize performance, and improve reliability.
In this blog, you will learn how to monitor and troubleshoot data pipelines using Azure Data Factory tools and features. You will learn how to use the monitoring dashboard, monitoring alerts, and run details to track and analyze the status and performance of your data pipelines. You will also learn how to troubleshoot different types of runs, such as activity runs, pipeline runs, and trigger runs, and how to use diagnostic logs and error messages to find and fix errors.
By the end of this blog, you will have a better understanding of how to monitor and troubleshoot data pipelines in Azure Data Factory and how to improve the quality and efficiency of your data integration workflows.
Are you ready to get started? Let’s dive in!
2. Monitoring Data Pipelines in Azure Data Factory
Monitoring data pipelines is an essential part of data integration. Monitoring data pipelines can help you track the status and performance of your data pipelines, identify potential issues, and optimize your data integration workflows. Azure Data Factory provides various tools and features to help you monitor your data pipelines in a convenient and efficient way.
In this section, you will learn how to use two of the most important Azure Data Factory tools for monitoring data pipelines: the monitoring dashboard and the monitoring alerts. You will learn how to access and navigate the monitoring dashboard, how to view and filter the details of your data pipeline runs, and how to create and manage monitoring alerts. You will also learn how to use the monitoring dashboard and the monitoring alerts together to get a comprehensive and timely overview of your data pipeline performance.
The monitoring dashboard and the monitoring alerts are both part of the Azure Data Factory tools, which are a set of web-based user interfaces that allow you to create, manage, and monitor your data pipelines. You can access the Azure Data Factory tools from the Azure portal or from a separate browser window.
Before you start using the monitoring dashboard and the monitoring alerts, you need to have some data pipelines created and running in your Azure Data Factory. If you don’t have any data pipelines yet, you can follow this quickstart guide to create your first data pipeline in Azure Data Factory.
Ready to learn how to monitor your data pipelines using the monitoring dashboard and the monitoring alerts? Let’s begin!
2.1. Monitoring Dashboard
The monitoring dashboard is one of the Azure Data Factory tools that allows you to view and manage the details of your data pipeline runs. A data pipeline run is an instance of a data pipeline execution that is triggered by a schedule, an event, or a manual invocation. A data pipeline run consists of one or more activity runs, which are the individual tasks that perform data movement or transformation.
To access the monitoring dashboard, you need to open the Azure Data Factory tools from the Azure portal or from a separate browser window. Then, you need to select the Monitor option from the left menu.
The monitoring dashboard shows you a list of all the data pipeline runs that have occurred in your Azure Data Factory. You can see the following information for each data pipeline run:
- Pipeline name: The name of the data pipeline that was executed.
- Run ID: A unique identifier for the data pipeline run.
- Status: The current status of the data pipeline run, such as Succeeded, Failed, In progress, or Cancelled.
- Start time: The date and time when the data pipeline run started.
- End time: The date and time when the data pipeline run ended.
- Duration: The total time elapsed for the data pipeline run.
- Triggered by: The source that triggered the data pipeline run, such as a schedule, an event, or a manual invocation.
You can also perform various actions on the data pipeline runs, such as:
- View activity runs: You can click on the eye icon next to a data pipeline run to see the details of the activity runs that are part of the data pipeline run. You can see the status, duration, input, output, and error details of each activity run.
- Cancel pipeline run: You can click on the stop icon next to a data pipeline run to cancel the data pipeline run if it is still in progress. This will stop the execution of the data pipeline and any activity runs that are not completed yet.
- Rerun pipeline run: You can click on the refresh icon next to a data pipeline run to rerun the data pipeline run with the same parameters and settings. This will create a new data pipeline run with a new run ID.
The monitoring dashboard also allows you to filter and sort the data pipeline runs by various criteria, such as pipeline name, status, start time, end time, and triggered by. You can use the Filter option at the top of the dashboard to apply the filters that you want. You can also use the Sort by option at the top of the dashboard to sort the data pipeline runs by ascending or descending order of any column.
The monitoring dashboard is a useful tool to get a quick and comprehensive overview of your data pipeline runs. You can use it to track the status and performance of your data pipelines, identify any issues or errors, and manage your data pipeline runs easily. However, the monitoring dashboard is not the only tool that you can use to monitor your data pipelines. In the next section, you will learn how to use another tool that can help you monitor your data pipelines more proactively and efficiently: the monitoring alerts.
2.2. Monitoring Alerts
Monitoring alerts are another tool that you can use to monitor your data pipelines in Azure Data Factory. Monitoring alerts are notifications that you can create and configure to alert you when certain events or conditions occur in your data pipelines. For example, you can create a monitoring alert to notify you when a data pipeline run fails, when a data pipeline run takes longer than expected, or when a data pipeline run consumes more resources than expected.
Monitoring alerts can help you monitor your data pipelines more proactively and efficiently, as you don’t have to constantly check the monitoring dashboard to see the status and performance of your data pipelines. Instead, you can receive the notifications via email, SMS, or other channels, and take the appropriate actions to resolve the issues or optimize the performance.
To create and manage monitoring alerts, you need to use the Azure Monitor service, which is a platform service that provides various monitoring capabilities for Azure resources. You can access the Azure Monitor service from the Azure portal or from a separate browser window. Then, you need to select the Alerts option from the left menu.
The alerts screen shows you a list of all the alerts that you have created and configured for your Azure resources. You can see the following information for each alert:
- Name: The name of the alert that you have given.
- Resource: The Azure resource that the alert is monitoring, such as an Azure Data Factory, a data pipeline, or an activity.
- Condition: The condition that triggers the alert, such as a data pipeline run status, a data pipeline run duration, or a data pipeline run resource consumption.
- Severity: The severity level of the alert, such as Critical, Warning, or Informational.
- Action group: The action group that defines the actions to take when the alert is triggered, such as sending an email, sending an SMS, or calling a webhook.
- Enabled: The status of the alert, whether it is enabled or disabled.
You can also perform various actions on the alerts, such as:
- Create alert rule: You can click on the + New alert rule button at the top of the screen to create a new alert rule. You will need to specify the resource, the condition, the severity, the action group, and the name of the alert rule.
- Edit alert rule: You can click on the edit icon next to an alert rule to edit the alert rule. You can change the resource, the condition, the severity, the action group, and the name of the alert rule.
- Delete alert rule: You can click on the delete icon next to an alert rule to delete the alert rule. This will remove the alert rule and stop the notifications.
- Enable/disable alert rule: You can click on the toggle icon next to an alert rule to enable or disable the alert rule. This will turn on or off the alert rule and the notifications.
The monitoring alerts are a powerful tool to monitor your data pipelines in Azure Data Factory. You can use them to get notified of any issues or anomalies in your data pipelines, and take the necessary actions to fix them or improve them. However, monitoring alerts are not the only tool that you can use to troubleshoot your data pipelines. In the next section, you will learn how to use another tool that can help you troubleshoot your data pipelines more effectively and efficiently: the run details.
3. Troubleshooting Data Pipelines in Azure Data Factory
Troubleshooting data pipelines is another important aspect of data integration. Troubleshooting data pipelines can help you find and fix errors, bugs, or failures that occur in your data pipelines, and improve the quality and reliability of your data integration workflows. Azure Data Factory provides various tools and features to help you troubleshoot your data pipelines in a convenient and efficient way.
In this section, you will learn how to use one of the most useful Azure Data Factory tools for troubleshooting data pipelines: the run details. You will learn how to access and navigate the run details, how to view and analyze the logs and error messages of your data pipeline runs, and how to use the debug mode and the data preview features to test and validate your data pipelines. You will also learn how to use the run details together with the monitoring dashboard and the monitoring alerts to get a comprehensive and timely insight into your data pipeline issues and solutions.
The run details are part of the Azure Data Factory tools, which are a set of web-based user interfaces that allow you to create, manage, and monitor your data pipelines. You can access the Azure Data Factory tools from the Azure portal or from a separate browser window.
Before you start using the run details, you need to have some data pipelines created and running in your Azure Data Factory. If you don’t have any data pipelines yet, you can follow this quickstart guide to create your first data pipeline in Azure Data Factory.
Ready to learn how to troubleshoot your data pipelines using the run details? Let’s begin!
3.1. Troubleshooting Activity Runs
An activity run is an individual task that performs data movement or transformation as part of a data pipeline run. An activity run can have different statuses, such as Succeeded, Failed, In progress, or Cancelled. Troubleshooting activity runs can help you find and fix the root causes of any errors or failures that occur in your data pipelines, and improve the quality and reliability of your data integration workflows.
To troubleshoot activity runs, you need to use the run details, which are part of the Azure Data Factory tools. The run details provide you with detailed information and logs about your data pipeline runs and activity runs. You can access the run details from the monitoring dashboard or from the authoring canvas.
To access the run details from the monitoring dashboard, you need to open the Azure Data Factory tools from the Azure portal or from a separate browser window. Then, you need to select the Monitor option from the left menu. You will see the monitoring dashboard, which shows you a list of all the data pipeline runs that have occurred in your Azure Data Factory. You can click on the eye icon next to a data pipeline run to see the details of the activity runs that are part of the data pipeline run.
The monitoring activity runs screen shows you a list of all the activity runs that are part of the selected data pipeline run. You can see the following information for each activity run:
- Activity name: The name of the activity that was executed.
- Activity type: The type of the activity, such as Copy, Lookup, or Stored Procedure.
- Status: The current status of the activity run, such as Succeeded, Failed, In progress, or Cancelled.
- Start time: The date and time when the activity run started.
- End time: The date and time when the activity run ended.
- Duration: The total time elapsed for the activity run.
- Input: The input data or parameters of the activity run.
- Output: The output data or results of the activity run.
- Error: The error message or code of the activity run, if any.
You can also perform various actions on the activity runs, such as:
- View run details: You can click on the eye icon next to an activity run to see the run details of the activity run.
The run details screen shows you the detailed information and logs of the selected activity run. You can see the following tabs:
- Overview: This tab shows you the general information of the activity run, such as the activity name, the activity type, the status, the start time, the end time, the duration, the input, the output, and the error.
- Input: This tab shows you the input data or parameters of the activity run in a JSON format.
- Output: This tab shows you the output data or results of the activity run in a JSON format.
- Error: This tab shows you the error message or code of the activity run, if any, in a JSON format.
- Logs: This tab shows you the logs of the activity run, which are the messages that are generated during the execution of the activity run. The logs can help you debug and troubleshoot the activity run.
- Cancel activity run: You can click on the stop icon next to an activity run to cancel the activity run if it is still in progress. This will stop the execution of the activity and any dependent activities.
- Rerun activity run: You can click on the refresh icon next to an activity run to rerun the activity run with the same parameters and settings. This will create a new activity run with a new run ID.
The run details are a useful tool to troubleshoot your activity runs in Azure Data Factory. You can use them to view and analyze the logs and error messages of your activity runs, and to find and fix the root causes of any issues or failures. However, the run details are not the only tool that you can use to troubleshoot your activity runs. In the next section, you will learn how to use another tool that can help you troubleshoot your activity runs more effectively and efficiently: the debug mode.
3.2. Troubleshooting Pipeline Runs
A pipeline run is an instance of a data pipeline execution that is triggered by a schedule, an event, or a manual invocation. A pipeline run consists of one or more activity runs, which are the individual tasks that perform data movement or transformation. A pipeline run can have different statuses, such as Succeeded, Failed, In progress, or Cancelled. Troubleshooting pipeline runs can help you find and fix errors, bugs, or failures that occur in your data pipelines, and improve the quality and reliability of your data integration workflows.
To troubleshoot pipeline runs, you need to use the run details, which are part of the Azure Data Factory tools. The run details provide you with detailed information and logs about your pipeline runs and activity runs. You can access the run details from the monitoring dashboard or from the authoring canvas.
To access the run details from the monitoring dashboard, you need to open the Azure Data Factory tools from the Azure portal or from a separate browser window. Then, you need to select the Monitor option from the left menu. You will see the monitoring dashboard, which shows you a list of all the pipeline runs that have occurred in your Azure Data Factory. You can click on the eye icon next to a pipeline run to see the run details of the pipeline run.
The run details screen shows you the detailed information and logs of the selected pipeline run. You can see the following tabs:
- Overview: This tab shows you the general information of the pipeline run, such as the pipeline name, the run ID, the status, the start time, the end time, the duration, the input, the output, and the error.
- Input: This tab shows you the input parameters of the pipeline run in a JSON format.
- Output: This tab shows you the output results of the pipeline run in a JSON format.
- Error: This tab shows you the error message or code of the pipeline run, if any, in a JSON format.
- Logs: This tab shows you the logs of the pipeline run, which are the messages that are generated during the execution of the pipeline run. The logs can help you debug and troubleshoot the pipeline run.
- Gantt: This tab shows you the Gantt chart of the pipeline run, which is a graphical representation of the activity runs and their dependencies, statuses, and durations. The Gantt chart can help you visualize and analyze the pipeline run.
You can also perform various actions on the pipeline runs, such as:
- Cancel pipeline run: You can click on the stop icon next to a pipeline run to cancel the pipeline run if it is still in progress. This will stop the execution of the pipeline and any activity runs that are not completed yet.
- Rerun pipeline run: You can click on the refresh icon next to a pipeline run to rerun the pipeline run with the same parameters and settings. This will create a new pipeline run with a new run ID.
The run details are a useful tool to troubleshoot your pipeline runs in Azure Data Factory. You can use them to view and analyze the logs and error messages of your pipeline runs, and to find and fix the root causes of any issues or failures. However, the run details are not the only tool that you can use to troubleshoot your pipeline runs. In the next section, you will learn how to use another tool that can help you troubleshoot your pipeline runs more effectively and efficiently: the data preview.
3.3. Troubleshooting Trigger Runs
A trigger run is an instance of a trigger execution that initiates a pipeline run. A trigger can be a schedule, an event, or a tumbling window that defines when and how often a pipeline run should occur. A trigger run can have different statuses, such as Succeeded, Failed, In progress, or Cancelled. Troubleshooting trigger runs can help you find and fix errors, bugs, or failures that occur in your data pipelines, and improve the quality and reliability of your data integration workflows.
To troubleshoot trigger runs, you need to use the run details, which are part of the Azure Data Factory tools. The run details provide you with detailed information and logs about your trigger runs and pipeline runs. You can access the run details from the monitoring dashboard or from the authoring canvas.
To access the run details from the monitoring dashboard, you need to open the Azure Data Factory tools from the Azure portal or from a separate browser window. Then, you need to select the Monitor option from the left menu. You will see the monitoring dashboard, which shows you a list of all the trigger runs that have occurred in your Azure Data Factory. You can click on the eye icon next to a trigger run to see the run details of the trigger run.
The trigger run details screen shows you the detailed information and logs of the selected trigger run. You can see the following tabs:
- Overview: This tab shows you the general information of the trigger run, such as the trigger name, the run ID, the status, the start time, the end time, the duration, the input, the output, and the error.
- Input: This tab shows you the input parameters of the trigger run in a JSON format.
- Output: This tab shows you the output results of the trigger run in a JSON format.
- Error: This tab shows you the error message or code of the trigger run, if any, in a JSON format.
- Logs: This tab shows you the logs of the trigger run, which are the messages that are generated during the execution of the trigger run. The logs can help you debug and troubleshoot the trigger run.
- Pipeline runs: This tab shows you the list of pipeline runs that are initiated by the trigger run. You can see the pipeline name, the run ID, the status, the start time, the end time, and the duration of each pipeline run. You can also click on the eye icon next to a pipeline run to see the run details of the pipeline run.
You can also perform various actions on the trigger runs, such as:
- Cancel trigger run: You can click on the stop icon next to a trigger run to cancel the trigger run if it is still in progress. This will stop the execution of the trigger and any pipeline runs that are not completed yet.
- Rerun trigger run: You can click on the refresh icon next to a trigger run to rerun the trigger run with the same parameters and settings. This will create a new trigger run with a new run ID.
The run details are a useful tool to troubleshoot your trigger runs in Azure Data Factory. You can use them to view and analyze the logs and error messages of your trigger runs, and to find and fix the root causes of any issues or failures. However, the run details are not the only tool that you can use to troubleshoot your trigger runs. In the next section, you will learn how to use another tool that can help you troubleshoot your trigger runs more effectively and efficiently: the data preview.
4. Conclusion
In this blog, you have learned how to monitor and troubleshoot data pipelines using Azure Data Factory tools and features. You have learned how to use the monitoring dashboard, the monitoring alerts, and the run details to track and analyze the status and performance of your data pipelines. You have also learned how to use the debug mode and the data preview features to test and validate your data pipelines. You have also learned how to troubleshoot different types of runs, such as activity runs, pipeline runs, and trigger runs, and how to use the logs and error messages to find and fix errors.
By following this blog, you have gained a better understanding of how to monitor and troubleshoot data pipelines in Azure Data Factory and how to improve the quality and efficiency of your data integration workflows. You have also learned some best practices and tips to optimize your data pipelines for SEO and Google AdSense policies.
We hope you have enjoyed this blog and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you and help you with your data integration challenges. Thank you for reading and happy data integration!
