Exploring Log Analytics

1. Exploring Log Analytics

Welcome back! In the last chapter, we learned about availability tests. Let's now take a look at log analytics within Azure!

2. Logs everywhere

Every Azure service generates logs such as activity logs, metrics and diagnostics. But when those logs live in different places, troubleshooting becomes guesswork. You waste time jumping between tools trying to piece together what happened.

3. Log analytics workspace

A Log Analytics Workspace solves this problem. It brings logs from many Azure resources into one central place, where you can query, correlate, and investigate issues much faster.

4. What is log analytics workspace

A Log Analytics workspace is a central store for logs, collecting data from multiple Azure resources in one place. It uses the Azure Data Explorer engine to handle large volumes of data efficiently. The workspace supports KQL for searching and analyzing logs. It works across subscriptions and resource groups, making cross-environment troubleshooting easier. It also forms the foundation of Azure Monitor, enabling alerts, dashboards, and deeper insights.

5. Why it matters

Without a Log Analytics workspace, logs are stored in separate places, making it difficult to correlate issues across services. Engineers often jump between tools, which leads to slow incident response and makes root cause analysis much harder. With a workspace, logs are brought together into a unified view, enabling end-to-end tracing across applications and infrastructure. This allows teams to correlate related events quickly and achieve faster root cause analysis, significantly reducing troubleshooting time.

6. What data can you send?

You can send almost any type of data into a Log Analytics workspace, including platform logs, performance metrics, application traces, and security logs. By bringing all of this data together, the workspace acts as a universal logging pipeline, giving you a single place to monitor behavior, investigate issues, and analyze activity across your entire Azure environment.

7. Exploring logs in the workspace

Once data flows into the workspace, you can use its query editor to explore and investigate it. Logs are organized in tables, making it easy to find what you need. You can filter by time range, resource, or type of event, and combine filters to uncover patterns, spot anomalies, or investigate specific issues across your environment.

8. The login mystery

Imagine users report problems at 2:15 PM. You open the workspace and write a single query that correlates App Service requests, API responses, and database connections within that timeframe. Within minutes, you see the pattern: a spike in database connection timeouts started at 2:14 PM, causing API failures, which caused login errors.

9. The login mystery

Drilling deeper, you find a runaway query from a reporting job consuming all database connections. You stop the job, connections recover, and logins work again. Total investigation time: ten minutes instead of an hour.

10. Introduction to KQL

KQL or Kusto Query Language is the language used to explore and analyze data inside your Log Analytics workspace. It's designed to be fast and optimized for huge volumes of logs collected across Azure. Unlike SQL, KQL is read-only, making it perfect for safe exploration. You use KQL to filter events, find errors, correlate issues across resources, and generate insights for dashboards and alerts. It’s the core tool for turning raw log data into answers during monitoring and troubleshooting.

11. Summary

Log Analytics makes up the backbone of Azure Monitor. It collects logs, stores them efficiently, and gives you powerful tools to query and visualize them. From troubleshooting to performance tuning to alerting, everything starts with a well configured Log Analytics Workspace.

12. Let's practice!

Let's jump in and get hands on!

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