This Kusto Query Language course teaches you how to leverage KQL to extract, analyze, and visualize data from various Azure services, including Azure Data Explorer and Microsoft Sentinel. You’ll learn the fundamental KQL syntax, explore advanced techniques like aggregations and time-series analysis, and discover how to apply KQL in real-world scenarios, ultimately enabling you to transform raw data into actionable insights.
Key Features of Course Divine:
Career Opportunities After Kusto Query Language:
Essential Skills you will Develop Kusto Query Language:
Kusto Query Language:
Tools Covered:
Syllabus:
Module 1: Introduction to KQL & Azure Data Explorer Overview of Azure Monitor and Data Explorer What is KQL and its role in log data querying Getting started with the Kusto environment Navigating the KQL web UI.
Module 2: Basic Syntax and Data Retrieval KQL syntax rules and operators
Understanding tables, columns, and records Basic queries: project, where, take, limit, etc. Working with search operator.
Module 3: Filtering and Sorting Data Using where, between, and comparison operators Logical operators: and, or, not order by and sorting data Using time filters and datetime functions.
Module 4: Data Transformation and Projection Using project, extend, and summarize parse and parse_json functions Calculated columns and expressions Data shaping best practices.
Module 5: Aggregation and Summarization Grouping with summarize and count()
Aggregation functions: avg(), min(), max(), percentile() Using bin() for time-based grouping Visualization of summarized data.
Module 6: Joins and Lookups Join types in KQL: inner, left outer, right outer, etc. Syntax for joins and matching keys Lookup pattern with join kind=inner Real-time examples using two datasets.
Module 7: Advanced Functions and Operators Working with string, math, and datetime functions Using case substring() Set operators: union, intersect, except Creating reusable functions.
Module 8: Anomaly Detection and Time Series Analysis Introduction to time series in KQL  Anomaly detection using Moving averages and trend analysis Visualizing time series charts.
Module 9: Working with Azure Monitor Logs Using KQL in Azure Log Analytics  Querying Activity Logs, Metrics, and Diagnostic Logs Alerts and dashboards in Azure Monitor Monitoring performance and troubleshooting.
Module 10: Real-World Scenarios & Capstone Project Use cases: Monitoring servers, app telemetry, and network logs Hands-on case study with real Azure data
Best practices for query optimization Final project and certification assessment.
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How To Apply:
Mobile: 9100348679
Email: coursedivine@gmail.com
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