Azure Logging Solutions: A Comprehensive Guide

Azure logging solutions are a crucial part of any cloud-based infrastructure. They help you monitor and troubleshoot issues in real-time, ensuring your applications and services run smoothly.

With Azure's robust logging capabilities, you can collect and analyze logs from various sources, including Azure services, on-premises servers, and third-party applications. This allows you to gain a comprehensive understanding of your system's performance and identify potential bottlenecks.

Azure provides several logging solutions, including Azure Monitor, Azure Log Analytics, and Azure Storage. Each solution has its strengths and use cases, and choosing the right one depends on your specific needs and requirements.

Azure Monitor's data collection capabilities allow you to collect data from all your applications and resources, regardless of their location - Azure, other clouds, or on-premises.

You can define data coming into Azure Monitor using data collection rules (DCRs), which can include transformations to filter and transform data before it's ingested into the workspace. These rules can be applied to all data sent to a specific table, even if it's coming from multiple sources.

Data collection rules can be used to save ingestion costs by filtering out unnecessary records. For example, you can create a transformation for a table that collects resource logs, filtering out records you don't need.

Azure Monitor has APIs that allow you to send custom data to the service. For example, you can collect log data from any REST client and store it in a Log Analytics workspace using the Logs ingestion API.

Data collection and processing are crucial steps in any business or organization. You can use the data you collect in Azure Monitor Logs to derive operational and business value.

Azure Monitor Logs offers a range of capabilities to analyze log data, including log queries, anomaly detection algorithms, and summary rules. This helps you identify unusual patterns or behaviors in your log data.

The power of interactive analysis is a game-changer. You can use Log Analytics in the Azure portal to write log queries and interactively analyze log data by using a powerful analysis engine.

You can also use summary rules to aggregate information you need for alerting and analysis from the raw log data you ingest. This lets you optimize your costs, analysis capabilities, and query performance.

Here are some of the ways you can use Azure Monitor Logs to derive operational and business value:

By using these capabilities, you can gain valuable insights into your business operations and make data-driven decisions.

Data collection is a crucial step in the process of collecting and analyzing data. You can collect data from all of your applications and resources running in Azure, other clouds, and on-premises using Azure Monitor's data collection capabilities.

Azure Monitor's data collection pipeline is powerful and enables filtering, transforming, and routing data to destination tables in your Log Analytics workspace. This helps optimize costs, analytics capabilities, and query performance.

You can collect data from various sources, including Azure Monitor agent, which uses a data collection rule to define data collected from virtual machines. However, data collected by the agent won't be subject to any ingestion-time transformations defined in the workspace.

To transform data you ingest into your Log Analytics workspace, you can use data collection rules (DCRs) that define data coming into Azure Monitor. These rules can include transformations that allow you to filter and transform data before it's ingested into the workspace.

Transformations in the workspace transformation DCR are defined for each table in a workspace and apply to all data sent to that table, even if sent from multiple sources. These transformations only apply to workflows that don't already use a DCR.

Here are some common use cases for data transformation in Azure Monitor:

If you can't collect data with the other methods, you can use the APIs in Azure Monitor to send data to Azure Monitor. For example, you can collect log data from any REST client and store it in Log Analytics workspace using the Logs ingestion API in Azure Monitor.

Data collection rules (DCRs) can define data coming into Azure Monitor and include transformations that allow you to filter and transform data before it's ingested into the workspace.

An Azure Log Analytics workspace is a data store that holds tables into which you collect data. You can define table plans based on your data consumption and cost management needs.

To address the needs of various personas who use a Log Analytics workspace, you can define table plans, manage retention, and manage access to the workspace and tables. You can also use summary rules to aggregate critical data in summary tables and create saved queries, visualizations, and alerts tailored to specific personas.

Here are some key benefits of using a Log Analytics workspace:

A Log Analytics workspace is a data store that holds tables into which you collect data. You can define table plans based on your data consumption and cost management needs.

You can manage low-cost long-term retention and interactive retention for each table, as well as manage access to the workspace and to specific tables. This allows you to optimize data for ease of use and actionable insights.

A Log Analytics workspace is a critical component of Azure Monitor Logs, and it's essential to understand how to design and manage it effectively. You can configure network isolation, replicate your workspace across regions, and design a workspace architecture based on your business needs.

Here are some key benefits of a well-designed Log Analytics workspace:

By following these best practices, you can create a Log Analytics workspace that meets your specific business needs and helps you get the most out of Azure Monitor Logs.

Monitoring virtual machines in Azure is a crucial aspect of managing your workspace. You can collect various types of data from VMs, including Windows Events, Syslog, Client Performance data, and more.

To collect Windows Events, you'll need to deploy the Azure Monitor agent and create a data collection rule to send data to a Log Analytics workspace. This will give you logs for the client operating system and different applications on Windows VMs.

Syslog data, on the other hand, is collected from Linux VMs and requires the Azure Monitor agent to be deployed and a data collection rule created to send data to a Log Analytics workspace. This includes logs for the client operating system and different applications on Linux VMs.

Client Performance data is another important metric to collect, which includes performance counter values for the operating system and applications running on the virtual machine. You can enable VM insights to send predefined aggregated performance data to a Log Analytics workspace.

Here are some common data types to collect from VMs:

By collecting these data types, you'll be able to get a better understanding of your VMs and make informed decisions about their management and optimization.

Kusto Query Language (KQL) is a powerful tool that can analyze millions of records quickly. You can use it to explore your logs, transform and aggregate data, discover patterns, identify anomalies and outliers, and more.

KQL is a read-only request to process data and return results, retrieved from a Log Analytics workspace. Log Analytics is a tool in the Azure portal for running log queries and analyzing their results.

Log Analytics Simple mode lets any user retrieve data from one or more tables with one click, using a set of controls to explore and analyze the retrieved data. If you're familiar with KQL, you can use Log Analytics KQL mode to edit and create queries.

Kusto Query Language (KQL) is a powerful tool that can analyze millions of records quickly.

It's great for exploring logs, transforming and aggregating data, discovering patterns, identifying anomalies and outliers, and more.

You can use KQL to retrieve data from a Log Analytics workspace, which is a read-only request to process data and return results.

KQL is a must-know for anyone working with Log Analytics, as it allows you to edit and create queries that can be used in Azure Monitor features like alerts and workbooks, or shared with other users.

Log Analytics Simple mode lets any user, regardless of their knowledge of KQL, retrieve data from one or more tables with one click.

This mode provides an intuitive, spreadsheet-like experience for exploring and analyzing retrieved data using the most popular Azure Monitor Logs functionality.

Azure Monitor offers many ready-to-use, curated Insights experiences that store data in Azure Monitor Logs and present it in an intuitive way, allowing you to monitor the performance and availability of your cloud and hybrid applications and their supporting components.

These Insights experiences provide a comprehensive view of your application's performance and usage, including request rates, response times, failure rates, and more. You can also use them to identify performance and failure anomalies in your applications without having to write explicit rules.

Log Analytics Simple mode lets any user, regardless of their knowledge of KQL, retrieve data from one or more tables with one click, and explore and analyze the retrieved data using the most popular Azure Monitor Logs functionality in an intuitive, spreadsheet-like experience.

You can also create your own visualizations and reports using workbooks, dashboards, and Power BI, giving you a holistic view of your application's performance and usage.

Azure Monitor Application Insights provides detailed insights into your application's performance and usage, including demographics data, retention, funnels, user flows, and more. You can also use it to track specific events or operations, and see how different components of your distributed application are connected and performing.

Permission to access data in a Log Analytics workspace is defined by the access control mode setting on each workspace. You can give users explicit access to the workspace by using a built-in or custom role.

Azure Active Directory (AD) logs contain sign-in logs and audit logs, which provide details about logins to applications using Azure AD and managing identity-related services.

You can use audit logs to determine which user performed exactly what actions in Azure AD, for example, who gave admin access to a user or who deleted a user from AD.

To access data collected for Azure resources, users need access to those resources, allowing them to view data in the Azure portal or create a diagnostic setting to send it to other destinations.

In the cloud, responsibility is shared between you and the provider, Azure. Azure maintains responsibility for its resources' basic health.

Human error can still impact application health and security, even with Azure's resources. Developers can misconfigure a resource, making an application vulnerable to attacks.

You need to consider scenarios where a new teammate could accidentally delete an important resource. This can happen even with the best of intentions.

Without logs, engineering teams are hamstrung as they attempt to analyze and trace problems to fix issues.