Interview Questions for Azure Log Analytics

Azure Log Analytics architecture is built on a scalable, cloud-based platform designed for efficient data ingestion, storage, and analysis. It leverages a distributed system to handle massive volumes of log data from diverse sources. Think of it like a giant, highly organized library for your logs.

• Data Ingestion: Data from various sources (detailed in the next answer) is ingested into Log Analytics workspaces. This process is handled by agents or direct connections, ensuring data gets to the right location.
• Data Storage: The ingested data is stored in a highly optimized data lake within Azure. This storage is designed for fast query performance, even on massive datasets. The data is organized to allow for efficient searches and analysis.
• Query Processing: Log Analytics uses the Kusto Query Language (KQL), a powerful query language designed for fast and efficient analysis of large datasets. KQL queries are processed by the Log Analytics engine which uses distributed computing to quickly return results.
• Data Visualization & Analysis: The processed data is presented through various visualization tools within the Azure portal, providing dashboards and reports for monitoring and insights. You can create custom dashboards to track key metrics.
• Security: The entire architecture is secured using Azure’s robust security infrastructure, including encryption at rest and in transit. Access control mechanisms ensure only authorized personnel can access sensitive log data.

In essence, it’s a robust, scalable, and secure system designed to handle the demands of modern enterprise-level logging and monitoring needs.

Azure Log Analytics can ingest data from a wide range of sources. Imagine it as a central hub receiving information from various departments within a company.

• Azure Services: This is a primary source. Many Azure services (like Azure VMs, storage accounts, and App Services) automatically send logs to Log Analytics.
• On-premises Servers and Applications: Using the Log Analytics agent, you can collect logs from servers and applications running on your own infrastructure. This allows centralized monitoring across your entire IT environment.
• Third-party Solutions: Many security and monitoring tools integrate with Log Analytics, sending their data for centralized analysis. This provides a unified view of your security posture.
• Custom Applications: You can design your own applications to send custom logs to Log Analytics, providing detailed insights into your application performance and behavior.
• CSV and JSON Files: You can even directly upload CSV or JSON files containing log data, providing flexibility for integrating data from various sources.

The variety of sources ensures comprehensive monitoring and analysis across your entire IT ecosystem.

Managing Log Analytics workspaces is straightforward through the Azure portal. Let’s break down the process:

• Creation: Navigate to the Log Analytics section in the Azure portal. Click ‘Create’ and specify details like the region, resource group, and pricing tier. This is like setting up a new filing cabinet for your logs.
• Resource Group: Organize your workspaces using resource groups. This allows for easy management and deletion of resources.
• Region: Choosing the right region is essential for performance and compliance. Keep your workspace close to your data sources to minimize latency.
• Pricing Tier: Log Analytics offers different pricing tiers based on the volume of ingested data. Select a plan that fits your needs and budget.
• Data Retention: You can define the data retention policy, specifying how long data will be stored in the workspace. This is important for cost management and compliance.
• Access Control: Implement Role-Based Access Control (RBAC) to manage access permissions. You can grant specific users or groups the rights to view, query, or manage the workspace data.

Once created, you can easily manage your workspace via the portal, modifying settings, connecting data sources and configuring alerts.

Log Analytics workspaces are the central repositories for all your log data within Azure. Imagine them as individual databases, each dedicated to a specific purpose or set of resources.

• Data Storage: Workspaces store all ingested log data, organizing it efficiently for retrieval and analysis.
• Querying: They are the environment where you execute KQL queries to analyze your data.
• Data Visualization: You can create dashboards and visualizations based on data within the workspace to monitor performance and identify issues.
• Alerting: Set up alerts based on specific criteria within the workspace to be notified of potential problems.
• Isolation: Each workspace is isolated, providing security and organizational benefits. This allows you to separate data from different environments or departments.

In short, Log Analytics workspaces are fundamental to leveraging the full power of Log Analytics for monitoring, analysis, and alerting.

Log Analytics workspaces are crucial for Azure monitoring because they provide a centralized location for collecting, storing, and analyzing logs from various Azure services and on-premises resources. They are the foundation of your observability strategy.

• Centralized Logging: Consolidates logs from diverse sources into a single location, simplifying monitoring and troubleshooting.
• Scalability: Designed to handle massive volumes of data, adapting to growing infrastructure and application needs.
• Advanced Analytics: KQL allows for powerful data analysis, enabling proactive issue identification and performance optimization.
• Security Monitoring: Collect security logs to monitor for threats and ensure compliance.
• Cost Optimization: Data retention policies allow for cost management by controlling the storage required.

Without Log Analytics workspaces, effective monitoring and management of a complex Azure environment would be significantly more challenging.

Azure Log Analytics offers a wide variety of queries using the Kusto Query Language (KQL). Here are some examples to illustrate the flexibility:

• Basic Queries: Retrieve specific data using where and project clauses. For example, finding all events related to a specific error code.
• Aggregation Queries: Calculate statistics such as counts, averages, and sums. For instance, computing the average CPU utilization of your virtual machines.
• Time Series Analysis: Analyze data trends over time. Useful for identifying patterns and detecting anomalies in resource utilization.
• Join Queries: Combine data from multiple tables. This is invaluable for correlating data from different sources to understand the big picture.
• Filtering Queries: Refine search criteria using various filter operators to isolate the events you need.
• Summarization Queries: Create summaries and aggregations of large datasets, making it easier to grasp key trends.

The breadth of query types allows for detailed and granular analysis across all your logged data.

Writing KQL queries in Log Analytics is intuitive once you understand the basic syntax. Let’s illustrate with examples:

• Simple Query: Heartbeat | where Computer == 'MyServer' This query retrieves all ‘Heartbeat’ events from the computer named ‘MyServer’.
• Query with Aggregation: Perf | summarize avg(CPUPercentage) by Computer This computes the average CPU percentage for each computer.
• Query with Filtering and Projection: SecurityEvent | where EventID == 4624 | project TimeGenerated, Computer, User This filters for specific events and displays only the timestamp, computer, and user.
• Join Query (Example): Imagine two tables, TableA and TableB, both having a common column ‘ID’. TableA | join kind=inner TableB on ID joins these tables based on their common ‘ID’.

The key to effective KQL is understanding its operators and functions. Azure provides excellent documentation and examples to help you master the language.

Remember to utilize the KQL editor within the Log Analytics workspace. It provides auto-completion and syntax highlighting which makes writing and debugging queries much easier.

Kusto Query Language (KQL) is the powerful query language used in Azure Log Analytics. It’s similar to SQL but specifically designed for analyzing large volumes of log data. It features a wide array of operators and functions categorized for easier understanding.

Operators: These manipulate data within your queries. Common examples include:

• Comparison Operators: == (equals), != (not equals), > (greater than), < (less than), >= (greater than or equals), <= (less than or equals).
• Logical Operators: and, or, not – used to combine conditions.
• Arithmetic Operators: +, -, *, /, % (modulo) – perform calculations.
• Set Operators: union (combines results), join (combines data from different tables based on a common field), distinct (removes duplicates).

Functions: These perform specific actions on your data, transforming and summarizing it. Some key functions are:

• Aggregation Functions: count(), sum(), avg(), min(), max() – used for statistical analysis.
• String Functions: startswith(), endswith(), substring(), tolower(), toupper() – manipulate text data.
• Date and Time Functions: date(), time(), datetime(), getmonth(), getyear() – extract and format date/time information.
• Scalar Functions: isnull() (checks for null values), isempty() (checks for empty strings) – used for data cleaning and validation.

Example: Let's say you want to find all failed login attempts in the last hour. A KQL query might look like this:

This query uses the where operator with comparison operators to filter events.

Creating and managing dashboards in Azure Log Analytics involves leveraging the Azure portal. Think of dashboards as custom-built visual representations of your data. They allow you to monitor key metrics and easily identify potential issues.

Creating a Dashboard:

1. Navigate to your Log Analytics workspace in the Azure portal.
2. Select 'Logs'.
3. Run a KQL query to generate the data you want to visualize.
4. Click 'Pin to dashboard' to add the visualization (chart, table, etc.) to a new or existing dashboard.
5. Customize the dashboard's layout and name as needed.

Managing a Dashboard:

• Adding/Removing Tiles: Easily add new visualizations or remove existing ones as your monitoring needs evolve.
• Organizing Tiles: Arrange tiles to optimize readability and workflow.
• Sharing Dashboards: Share dashboards with other team members for collaborative monitoring.
• Saving and Exporting: Save your progress and export dashboards for future use or sharing.

Example: You might create a dashboard showing CPU utilization, memory usage, and network traffic for your critical servers. This allows for quick assessment of their health and performance.

Azure Monitor is a comprehensive monitoring service, and Log Analytics is a core component within it. Think of Azure Monitor as the overarching platform, while Log Analytics focuses on collecting, storing, and analyzing log data.

Azure Monitor integrates data from various sources including Log Analytics, metrics, and activity logs. It provides a unified view of your entire Azure environment's health and performance. Log Analytics, in turn, enables the powerful KQL querying and visualization of log data that fuels many of Azure Monitor's capabilities.

In essence, Log Analytics provides the detailed analysis of log data, while Azure Monitor provides the overall framework for monitoring and alerting based on that data and other sources.

Example: Azure Monitor might alert you about high CPU utilization on a virtual machine. This alert is likely triggered by analyzing metric data. To understand the *reason* for high CPU, you can dive into the Log Analytics data using KQL to investigate related log entries.

Troubleshooting performance issues using Azure Log Analytics involves a systematic approach: identify the bottleneck, analyze the root cause, and implement solutions.

Steps:

1. Identify the affected resource: Determine which component (VM, database, application) is experiencing performance degradation.
2. Gather relevant logs: Collect logs from the resource using KQL queries that target event IDs related to performance metrics. (e.g., high CPU, slow database queries, etc.) For example, you might focus on performance counter logs.
3. Analyze the logs: Use KQL to analyze patterns, anomalies, and trends in the collected logs. Look for specific error messages, slow response times, and resource exhaustion indicators.
4. Isolate the root cause: Based on your analysis, identify the root cause of the performance issue. Is it a code bottleneck, insufficient resources, network latency, etc.?
5. Implement solutions: Based on the root cause, implement appropriate solutions. This might include scaling resources, optimizing code, improving network connectivity, or patching systems.
6. Monitor the results: After implementing solutions, monitor the affected resource to ensure performance improvements.

Example: If you notice slow website response times, you might query the IIS logs using KQL in Log Analytics to identify slow requests and the underlying causes, potentially leading to code optimization or infrastructure upgrades.

Azure Log Analytics excels at monitoring the health of your Azure resources. This involves using KQL to analyze data from various sources such as diagnostic logs, activity logs, and performance counters.

Monitoring Strategies:

• Resource Health: Use KQL to query logs related to resource health, such as error messages, critical exceptions, and performance degradation. This can proactively identify potential issues before impacting users.
• Performance Monitoring: Monitor key performance indicators (KPIs) like CPU utilization, memory usage, disk I/O, and network traffic using performance counter logs within Log Analytics.
• Application Health: Monitor application logs to identify errors, exceptions, and slow requests. This helps pinpoint application-specific issues affecting resource health.
• Security Monitoring: Analyze security-related logs (e.g., authentication failures) to detect potential security breaches and vulnerabilities that can impact resource reliability.

Example: You could create a dashboard that displays the status of all your virtual machines, highlighting any VMs with high CPU or memory utilization, and potentially linking that directly to application logs to isolate the cause.

Setting up alerts based on Log Analytics data involves using Azure Monitor's alert rules. These rules define conditions based on your KQL queries, triggering alerts when specific criteria are met.

Steps:

1. Navigate to Azure Monitor: In the Azure portal, go to your Log Analytics workspace and then to 'Alerts'.
2. Create a new alert rule: Select 'New alert rule'.
3. Specify the scope: Define the resources or subscriptions the alert applies to.
4. Define the signal logic: This is where you use KQL to define the conditions that trigger the alert. Example: SecurityEvent | where EventID == 4625 | where Result == 'Failure' | summarize count() by User | where count_ > 10 This would alert if more than 10 failed login attempts occur from a single user.
5. Configure the alert criteria: Define thresholds, such as the number of failures or the duration exceeding a certain limit.
6. Select the action group: Define how you'll receive notifications (email, SMS, webhook, etc.).
7. Review and create: Verify your alert rule's configuration and create the rule.

Example: You might create an alert that notifies you if the average CPU utilization of your web servers exceeds 80% for more than 15 minutes. This proactive alerting can help you avoid performance issues before they escalate.

Log Analytics alerts are automated notifications triggered when specific conditions defined by KQL queries are met within your log data. They are critical for proactive monitoring and rapid response to incidents.

Alert Configuration:

• Condition: This is defined by a KQL query, specifying the criteria that must be met to trigger the alert. The query assesses whether data points exceed thresholds, observe unexpected patterns or show unusual activity.
• Severity: You assign a severity level (e.g., critical, warning, informational) to categorize the alert's importance.
• Frequency: Define how often the alert condition is evaluated. It can be set to trigger immediate alerts on changes or trigger at intervals (e.g. every 5 minutes).
• Action Group: This specifies how you'll be notified, such as through email, SMS messages, or by integrating with other tools like PagerDuty. An action group can also initiate automated remediation steps.
• Time aggregation: When evaluating aggregate criteria, specify a time window (e.g., last 5 minutes, last hour) to provide context to the alert and prevent false positives.

Example: An alert might be configured to notify you if the number of failed login attempts from a specific IP address exceeds 10 within a 5-minute window. This helps detect potential brute-force attacks promptly.

Azure Log Analytics helps with capacity planning by providing insights into resource utilization and performance trends. Think of it as a crystal ball for your Azure infrastructure. Instead of guessing how much storage or compute you'll need, you can use Log Analytics to analyze historical data, predict future needs, and avoid costly over-provisioning or performance bottlenecks.

Here's how it works: You can query your logs for metrics like CPU utilization, memory usage, disk I/O, and network traffic. By visualizing these metrics over time, you can identify peak usage periods, average resource consumption, and potential growth patterns. For example, if you see CPU consistently reaching 90% during certain hours, you can proactively scale up your virtual machines to prevent performance degradation. You can even use Log Analytics to project future resource requirements based on historical trends and predicted growth.

Example: Let's say you're monitoring the disk space of your web servers. Using Log Analytics, you could write a query to track free disk space over the past month. This data can then be used to estimate the rate of disk space consumption and predict when you might need to increase storage capacity. This avoids unexpected outages due to running out of space.

Azure Log Analytics offers several ways to visualize data, making it easy to understand complex information at a glance. Think of it like having a toolbox of different charts and graphs to represent your data effectively. The most common options include:

• Charts: Line charts for visualizing trends over time, bar charts for comparing different values, and pie charts for showing proportions.
• Tables: Simple tables displaying raw data, useful for detailed analysis.
• Maps: Visualizing data geographically, showing the location of resources or events.
• Pivot Tables: Interactive tables that allow you to group and summarize data in different ways.
• Custom Visualizations: Log Analytics allows you to integrate custom visualizations developed using tools like Power BI, giving you immense flexibility.

Example: To visualize CPU utilization across multiple virtual machines, you would typically use a line chart, where the x-axis is time and the y-axis represents CPU percentage. Each line represents a different VM, allowing you to quickly compare their performance.

Azure Log Analytics integrates seamlessly with a variety of other Azure services, extending its capabilities and providing a centralized view of your entire Azure environment. Imagine it as the central nervous system, connecting and interpreting data from various parts of your Azure body.

• Azure Monitor: Log Analytics is the core data platform for Azure Monitor, collecting logs and metrics from various Azure services. It's the primary way to analyze performance and troubleshoot issues.
• Azure Automation: Automate tasks based on Log Analytics data, creating proactive responses to system events or alerts.
• Azure Resource Graph: Allows you to query resources across your entire Azure subscription, complementing Log Analytics data with resource metadata.
• Azure Event Hubs: Ingest data from various sources into Log Analytics, enriching the data you analyze.
• Azure Functions: Process Log Analytics data using serverless functions, extending the analytical capabilities.

Example: You could integrate Log Analytics with Azure Automation to automatically scale up virtual machines when CPU utilization exceeds a threshold detected by Log Analytics queries.

Azure Sentinel leverages Azure Log Analytics as its data store, acting like a powerful security information and event management (SIEM) system. Think of Azure Sentinel as the security guard using Log Analytics data to monitor and protect your Azure environment.

Azure Sentinel ingests security logs from various sources, including Azure services, on-premises servers, and third-party security tools, storing them within a Log Analytics workspace. Sentinel then uses this data to detect security threats, automate incident response, and provide comprehensive security monitoring and analysis. The integration is seamless, providing a unified platform for both security monitoring and operational monitoring.

Example: Suspicious login attempts detected by Azure Active Directory are logged in a Log Analytics workspace. Azure Sentinel analyzes this data, automatically identifying potential security breaches and triggering alerts. This allows for rapid response and prevents potential damage.

Access control to your Log Analytics workspace is crucial for security and compliance. You need granular control over who can view, query, and manage your data. This is managed through Azure Role-Based Access Control (RBAC). RBAC allows you to assign specific roles to users or groups, defining their permissions.

You can create custom roles with specific permissions for different tasks like data viewing, query execution, or workspace administration. This minimizes the risk of unauthorized access and data breaches.

Example: You could create a role that allows a security analyst to only view and query security-related logs, while a system administrator has full access to manage the workspace. This ensures that only authorized personnel can access sensitive information and perform administrative tasks.

Azure Log Analytics pricing is based on the volume of data ingested and the number of queries executed. It operates on a pay-as-you-go model, meaning you only pay for what you use. There isn't a fixed set of 'tiers' in the traditional sense, but rather a flexible pricing structure based on data volume and query usage.

The cost is determined by factors like the amount of data ingested, the data retention period, and the number of queries run. Microsoft provides pricing calculators to estimate the cost based on your expected usage. Understanding your data ingestion patterns is key to managing costs.

Example: Ingesting more data will result in higher costs. Similarly, complex or frequently executed queries can also increase your bill.

Optimizing Log Analytics queries is essential for performance and cost efficiency. Slow queries can impact your ability to analyze data in a timely manner and can also lead to increased costs. Here are some key optimization techniques:

• Use the `summarize` operator effectively: Reduce the amount of data processed by summarizing your results before using other operators.
• Filter data early: Apply filters as early as possible in your query to reduce the data volume processed.
• Avoid wildcard characters (*) in your queries: Wildcards can significantly slow down query execution. Be specific with your filters.
• Use appropriate data types: Ensure that your data types match your query operators for efficient processing.
• Understand indexing: Azure Log Analytics uses indexing to speed up query performance. Ensure that your frequently queried fields are properly indexed.
• Use the `render` operator strategically: The `render` operator can impact performance, especially with large datasets. Use it only when necessary.

Example: Instead of querying all events from the past month, focus on a specific time range or specific event types using the `where` clause to significantly reduce processing time. Summarize count() by Computer is more efficient than counting each event individually.

Data retention in Azure Log Analytics refers to the length of time your log data is stored in your workspace. It's a crucial aspect of cost management and compliance. By default, data is retained for 30 days, but you can adjust this setting to anywhere between 2 days and 730 days (two years). Think of it like a digital filing cabinet; you need to decide how long you need to keep specific documents for auditing, troubleshooting, or reporting purposes. Longer retention means higher storage costs, while shorter retention might mean you miss crucial information for analysis if a problem arises weeks later.

You manage data retention in the Azure portal under your Log Analytics workspace settings. Increasing retention is straightforward, but remember to carefully consider the cost implications before making changes. Azure provides clear pricing information to help you make informed decisions. For example, if you're handling security logs requiring a longer audit trail, you'd extend retention to meet regulatory needs such as HIPAA or GDPR. Conversely, less critical logs from applications might benefit from shorter retention to reduce cost.

Handling large volumes of data in Azure Log Analytics involves a multi-faceted approach focused on optimization and efficient data management. Imagine trying to manage a massive library—you wouldn't just throw everything on the floor. Here's how we approach it:

• Data Reduction Techniques: Employing log collection filters to only ingest relevant data is paramount. Avoid ingesting unnecessary data points that clutter your workspace and increase costs. Think of it like using targeted search terms instead of searching the entire library.
• Data Compression: Azure Log Analytics automatically compresses data to save storage space and improve query performance. You don't need to actively manage this; it's a built-in feature.
• Optimized Queries: Writing efficient Kusto Query Language (KQL) queries is crucial. Poorly written queries can dramatically slow down performance, especially with large datasets. Leverage KQL's capabilities, such as summarizing and filtering data before analyzing it.
• Data Archiving: For long-term retention beyond the configured data retention period, consider archiving your data to a cheaper storage tier such as Azure Blob Storage. This allows you to retain historical information without incurring the high costs of storing it within Log Analytics for extended periods.
• Scaling your workspace: For extremely large volumes, you might need to consider scaling your Log Analytics workspace to handle the increased load. Azure allows you to provision different workspace sizes, each tailored to specific data ingestion and query demands.

Exporting data from Azure Log Analytics is valuable for tasks like deeper analysis outside of the Log Analytics interface, integrating with other tools, or satisfying compliance requirements. Think of it as taking notes from your research and putting it into another format for a different purpose.

• Export to Azure Storage: This is a common method for exporting your data to Azure Blob Storage or Azure Data Lake Storage. You can configure this export as a continuous or scheduled operation. This is ideal for long-term data retention and analysis outside of Log Analytics. You can use tools like Power BI or other data analysis platforms to explore your exported data.
• Export to Event Hub: This is a good choice for real-time streaming of log data. This method is suitable when you need immediate access to the data for event processing or analysis in real time. It's a popular method for continuous monitoring and alerting systems.
• Download as CSV or JSON: For smaller datasets or one-off reports, you can simply download your query results in CSV or JSON format. This is useful for quick analysis or sharing data with stakeholders who don't have direct access to Log Analytics.

Each method serves a distinct purpose, and choosing the right one depends on the size and type of data, the frequency of export, and the intended use of the exported data.

Securing and ensuring compliance of your Log Analytics data is critical. It's like protecting a highly valuable asset, and negligence can lead to serious consequences. Here's a multi-layered approach:

• Role-Based Access Control (RBAC): Use Azure RBAC to grant only necessary permissions to users and services accessing your Log Analytics workspace. This principle of least privilege limits the impact of potential breaches.
• Network Security: Restrict network access to your Log Analytics workspace using virtual networks (VNets) and firewalls. Don't expose it directly to the public internet.
• Data Encryption: Azure encrypts data at rest and in transit by default. However, you should ensure that your encryption keys are managed securely.
• Compliance Standards: Ensure your data retention policies and security measures meet the requirements of relevant industry standards and regulations such as ISO 27001, SOC 2, HIPAA, or GDPR. Regular audits are vital.
• Monitoring and Alerting: Implement monitoring to detect anomalous activity in your Log Analytics workspace. Set up alerts to notify you of suspicious events or security breaches.

A comprehensive security strategy incorporates both preventative and detective measures. Regularly review and update your security policies as needed.

Troubleshooting Log Analytics errors often involves a systematic approach. Think of it like diagnosing a car problem; you need to methodically check different parts.

• Check Data Ingestion: Ensure your data sources are configured correctly and sending data to your Log Analytics workspace. Check for any errors in your data collector configuration.
• Review Workspace Status: Verify the status of your Log Analytics workspace in the Azure portal. Look for any errors or warnings.
• Analyze Query Errors: When encountering query errors, carefully examine the error messages. They often provide clues about the cause of the issue. Use KQL syntax highlighting and IntelliSense to catch errors early.
• Check Data Schema: Confirm that your data is formatted correctly and adheres to the expected schema. Incorrect data formatting can prevent proper analysis.
• Examine Logs and Metrics: Use Azure Monitor to track the performance of your Log Analytics workspace. Examine the logs for any clues about errors or performance bottlenecks. Identify slow or failing queries and optimize them.
• Azure Support: If the problem persists, don't hesitate to reach out to Azure support. They have the expertise to diagnose and resolve complex issues.

Thorough log examination is often the key to identifying the root cause of many problems.

Designing a monitoring solution with Log Analytics starts with understanding the specific scenario. Let's consider a scenario: monitoring the health of a web application deployed in Azure App Service.

1. Define Objectives: What aspects of the application do we need to monitor? We'll focus on CPU utilization, memory usage, request latency, and error rates. We need to define thresholds for alerts based on these metrics.

2. Data Sources: We'll use Azure Diagnostics to collect performance counters and logs from the App Service. We can configure these settings within the App Service configuration panel.

3. Log Analytics Workspace: We'll create a Log Analytics workspace to store the collected data. The appropriate workspace pricing tier depends on expected data volume.

4. KQL Queries and Dashboards: We'll craft KQL queries to visualize the key metrics on dashboards in Log Analytics. These dashboards will show CPU, memory, response times, and error counts in real time or over specific intervals. We'll set up alerts based on predefined thresholds (e.g., CPU above 80%, error rate exceeding 5%).

5. Alerting: We'll configure alerts to notify us (via email, SMS, or other methods) when thresholds are breached. This ensures proactive issue detection.

Example KQL query (Illustrative):

This query averages CPU percentage over 5-minute intervals within the last hour. We can adapt it to monitor other metrics.

This design enables proactive monitoring, automated alerting, and detailed analysis of our web application's performance, helping us identify and resolve issues promptly.