Azure Log Analytics Best Practices for Efficient Data Management

To manage log data efficiently in Azure Log Analytics, it's essential to implement a data retention policy. This policy will help you determine how long you want to keep your log data, which can range from 31 days to 730 days.

Azure Log Analytics allows you to set a default retention period, which applies to all data unless you specify a custom retention period for a particular workspace. A default retention period of 31 days is recommended for most use cases.

Having a data retention policy in place will also help you avoid unnecessary storage costs and reduce the risk of data overflow. This is especially important for large-scale Azure environments.

By implementing a data retention policy, you'll be able to maintain a healthy balance between data retention and storage costs.

Azure Monitor's data collection capabilities let you collect data from all of your applications and resources running in Azure, other clouds, and on-premises.

You can collect data from various sources, including Azure, other clouds, and on-premises resources, to get a comprehensive view of your system.

A powerful ingestion pipeline enables filtering, transforming, and routing data to destination tables in your Log Analytics workspace to optimize costs, analytics capabilities, and query performance.

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

Transformations in the workspace transformation DCR apply to all data sent to a table, even if sent from multiple sources, and can be defined for each table in the workspace.

You can create a transformation for a table that collects resource logs and filters the data for only records that you want, saving you the ingestion cost for records you don't need.

Transformations can also extract important data from certain columns and store it in other columns in the workspace to support simpler queries.

Querying data in Azure Log Analytics is a powerful tool that can analyze millions of records quickly. You can use Kusto Query Language (KQL) 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, and it's a great way to get started with querying data in Azure Log Analytics. You can use the Log Analytics portal to run log queries and analyze their results.

To write a query, simply double-click on the table name or hover over it and click "Use in editor" to add it to the query window. You can also type directly in the window, and even get IntelliSense to help complete the names of tables in the current scope and KQL commands.

The simplest query you can write just returns all the records in a table, and you can run it by selecting the Run button or by selecting Shift+Enter with the cursor positioned anywhere in the query text. This will return up to 30,000 results, which is the maximum number of results you can retrieve in the Log Analytics portal experience.

If you're familiar with KQL, you can use the Log Analytics KQL mode to edit and create queries, which you can then use in Azure Monitor features such as alerts and workbooks, or share with other users.

You can also use the Azure CLI to execute an Analytics query for data using the `azure-log-analytics-execute-query` command. This command takes a query as input and returns the results, along with the name of the query table.

Data collection and transformation are key components of Azure Log Analytics, allowing you to collect data from various sources, including Azure, other clouds, and on-premises environments.

A powerful ingestion pipeline enables filtering, transforming, and routing data to destination tables in your Log Analytics workspace to optimize costs, analytics capabilities, and query performance.

Data collection rules can define data coming into Azure Monitor, including transformations that filter and transform data before ingestion. These rules can also include workspace transformations that apply to all data sent to a specific table.

You can create custom tables in your Log Analytics workspace to store data from non-Azure resources and applications, based on the data model of the log data you collect.

Table management settings let you control access to specific tables, manage the data model, retention, and cost of data in each table.

To open Log Analytics, you can either open the demo environment or select Logs from the Azure Monitor menu in your subscription.

This sets the initial scope to a Log Analytics workspace, so your query will select from all data in that workspace.

If you select Logs from an Azure resource's menu, the scope will be set to only records from that resource.

You can view the scope in the upper-left corner of the Logs experience, below the name of your active query tab.

In your own environment, you'll see an option to select a different scope, but this option isn't available in the demo environment.

Tables are a crucial part of data management in Azure Monitor.

Azure Monitor Logs automatically creates tables required to store monitoring data you collect from your Azure environment.

You can create custom tables to store data you collect from non-Azure resources and applications, based on the data model of the log data you collect and how you want to store and use the data.

Table management settings let you control access to specific tables, manage the data model, retention, and cost of data in each table.

Each Log Analytics workspace contains multiple tables where Azure Monitor Logs stores data you collect.

Data retention is crucial for any data management strategy, and Azure Log Analytics has a robust system in place. Data in a Log Analytics workspace is retained in two states: interactive retention and long-term retention.

During interactive retention, you can retrieve data from a table through queries, and it's available for visualizations, alerts, and other features. This means you can easily access and analyze your data when you need it.

Each table in your Log Analytics workspace lets you retain data up to 12 years in low-cost, long-term retention. This is a significant advantage, as it allows you to store large amounts of data without worrying about storage costs.

To manage your log data efficiently, you can retrieve specific data from long-term retention to interactive retention using a search job. This process is seamless, and you don't need to move data to external storage.

Data access in Log Analytics workspaces 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.

Permission to access data is determined by the access control mode setting on each workspace. This can be either explicit access through a built-in or custom role, or implicit access through access to the underlying Azure resources.

To give users explicit access, you need to use a built-in or custom role. This can be done by navigating to the workspace settings and assigning the necessary permissions.

Here are the steps to give users explicit access:

Alternatively, you can allow access to data collected for Azure resources to users with access to those resources. This is done by setting the access control mode to allow implicit access.

Here are the settings for implicit access:

You can also manage access to log data and workspaces in Azure Monitor. This involves setting up access control and assigning permissions to users and groups.

Here's a table summarizing the access control modes:

By understanding how access control works in Log Analytics workspaces, you can ensure that your users have the right permissions to access the data they need.

You can monitor the performance and availability of your cloud and hybrid applications with Azure Monitor's ready-to-use, curated Insights experiences. They store data in Azure Monitor Logs and present it in an intuitive way.

These insights are available for various resources and services, providing a customized monitoring experience. You can also create your own visualizations and reports using workbooks, dashboards, and Power BI.

With Power BI, you can export the results of a query to use different visualizations and share with people outside Azure. This allows for a more in-depth analysis of your data and its presentation.

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

By creating custom dashboards and visualizations, you can gain a deeper understanding of your application's performance and make data-driven decisions.

Azure Log Analytics provides robust security and compliance features to protect your data. It integrates with Azure Security Center to detect and respond to threats in real-time.

With Azure Log Analytics, you can configure alerting and notification policies to stay on top of security incidents. This ensures you're always aware of potential threats and can take swift action to mitigate them.

Azure Log Analytics also supports regulatory compliance with features like data masking and redaction, which allow you to hide sensitive information from unauthorized users.

Microsoft Sentinel and Microsoft Defender for Cloud are powerful tools for security monitoring in Azure. They store their data in Azure Monitor Logs, allowing for analysis with other log data collected by Azure Monitor.

These services provide a centralized view of security-related data, making it easier to identify potential threats and take action. They also integrate with other Azure services to provide a comprehensive security solution.

Microsoft Sentinel and Microsoft Defender for Cloud can help you detect and respond to security threats in real-time, reducing the risk of data breaches and other security incidents.

To connect to Azure Log Analytics, you'll need to use either the Cortex XSOAR Azure App or the Self-Deployed Azure App.

The Azure account must have permission to manage applications in Azure Active Directory (Azure AD).

To achieve this, you can assign the user one of the following Azure AD roles: Application administrator, Application developer, or Cloud application administrator.

For search job commands, the user needs to be assigned the Log Analytics Contributor role.

In addition to these roles, the user that granted the authorization must be assigned the Log Analytics Reader role.