ABC company has multiple data pipeline setup using Azure Data factory.
The data team is trying to use Azure monitor for keeping the pipeline run data.
The following are the main requirements.
Keep run data for at least 60 days. Write complex queries on metrics published by the data factory. Monitor Multiple data pipelines in a single workspace. Which of the options will be the best target for Azure Monitor in this scenario?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: D
The first requirement is storing the data for 60 days.
By default, the data pipeline will be stored by Azure Data Factory for almost 45 days.
But for storing data for more than 45 days, an Azure monitor should be used.
The next requirement is to have the ability to run complex queries on this data which is stored.
You can think of running some complex queries and creating some alerts based on those queries.
This is a feature available with the Log Analytics workspace.
To confirm our answer, we can look at the third feature, which says there should be a single workspace that can be used for all the data pipelines.
Creating a single workspace in Log Analytics and collecting data from multiple inputs can be done easily.
Option A incorrect: Storage account does not give you complex query and single workspace options.
Option B is incorrect: Even though the event hub can be used as a target to Azure Monitor, its main purpose is to process streaming data.
The feature of querying and workspace is not available here.
Option C is incorrect: Event grid is generally not used as a target for Azure Monitor and is used mainly to build applications with event-based architectures.
Option D is correct: It has query ability, and the log analytics workspace can act as a single target for metrics from different pipelines.
To know more about Monitoring Data Factory pipelines, please refer to the doc below:
In this scenario, the best target for Azure Monitor would be Log Analytics (option D).
Azure Monitor is a comprehensive monitoring solution for Azure resources and services that allows users to collect, analyze, and act on telemetry data. Azure Data Factory is a cloud-based data integration service that allows users to create, schedule, and manage data pipelines. By using Azure Monitor, users can monitor the status and performance of their Azure Data Factory pipelines and gain insights into the health of their data integration workflows.
Here are the reasons why Log Analytics is the best target for Azure Monitor in this scenario:
Retain run data for at least 60 days: Log Analytics is designed to store and retain log data for extended periods of time, making it a good choice for meeting the requirement of keeping run data for at least 60 days.
Write complex queries on metrics: Log Analytics provides a powerful query language that enables users to write complex queries on the metrics published by the Azure Data Factory pipelines. The queries can be used to identify performance issues, track pipeline failures, and troubleshoot errors.
Monitor multiple data pipelines in a single workspace: Log Analytics allows users to collect data from multiple sources and consolidate them into a single workspace. This feature enables users to monitor multiple data pipelines in a single workspace, making it easier to manage and analyze the data.
Azure Event Hub (option B) and Azure Event Grid (option C) are messaging services that enable users to collect and process events from multiple sources. Although these services can be used to collect telemetry data from Azure Data Factory pipelines, they are not optimized for storing and querying log data for extended periods of time. Therefore, they may not be the best choice for meeting the requirement of retaining run data for at least 60 days and writing complex queries on metrics.
A Storage Account (option A) is a basic storage service that enables users to store and retrieve data in the cloud. Although a Storage Account can be used to store log data, it does not provide the advanced analytics and querying capabilities of Log Analytics. Therefore, it may not be the best choice for monitoring and analyzing Azure Data Factory pipelines.