Microsoft Azure Data Explorer with Advanced KQL Course Overview

To ensure that learners are well-prepared and can derive maximum benefit from the Microsoft Azure Data Explorer with Advanced KQL course, the following are the minimum required prerequisites:

• Basic understanding of cloud computing concepts and services.
• Familiarity with database concepts and data analytics principles.
• Some experience with Microsoft Azure services, particularly Azure Data Explorer (Kusto) is helpful but not mandatory.
• Knowledge of basic SQL or any other query language will be advantageous for learning KQL.
• Ability to navigate and use the Azure portal for creating and managing resources.
• A willingness to learn and explore complex data queries and analytics techniques.

These prerequisites are designed to provide a foundation upon which the course content can build. They are not intended to be barriers but rather guidelines to ensure a productive and enriching learning experience.

Target Audience for Microsoft Azure Data Explorer with Advanced KQL

The Microsoft Azure Data Explorer with Advanced KQL course is ideal for IT professionals seeking to master data analytics and querying on Azure.

• Data Engineers
• Data Scientists
• Data Analysts
• Cloud Solutions Architects
• Database Administrators
• IT Professionals working with big data and analytics
• DevOps Engineers focusing on monitoring and diagnostics
• Business Intelligence Professionals
• System Administrators looking to scale and secure Azure Data Explorer deployments
• Developers integrating Azure Data Explorer into applications
• Technical Team Leads managing data analytics projects
• Security Analysts involved in monitoring and securing data on Azure
• Data Consultants providing insights on Azure-based data solutions
• Professionals preparing for Microsoft’s data and AI certifications

Learning Objectives - What you will Learn in this Microsoft Azure Data Explorer with Advanced KQL?

Introduction to Learning Outcomes:

Gain mastery of Microsoft Azure Data Explorer and Advanced KQL to efficiently manage, query, and visualize big data, conduct analytics, and leverage extensibility with Python/R for in-depth analysis.

Learning Objectives and Outcomes:

• Understand the core functionalities and architecture of Azure Data Explorer (ADX) to make informed decisions on its deployment and use.
• Identify ADX use cases and appreciate its scalability and security features for enterprise-level data management.
• Create and manage ADX infrastructure, including clusters and databases, while understanding cost implications and permission settings.
• Master Kusto Query Language (KQL) for advanced data querying, manipulation, and control commands within ADX environments.
• Utilize powerful KQL operators and develop proficiency in writing advanced queries to extract and analyze complex datasets.
• Learn to visualize data effectively using ADX's built-in tools and integrate with external visualization platforms like Power BI, Grafana, and Tableau.
• Apply monitoring techniques using ADX metrics and diagnostic logs to maintain cluster health and troubleshoot issues.
• Perform user analytics with KQL to derive insights on user behavior, engagement, and activity patterns.
• Conduct geographic analysis and root cause diagnosis through ADX and KQL, enhancing spatial data interpretation and issue resolution.
• Explore time series analysis, anomaly detection, and forecasting within ADX, and extend KQL capabilities using inline Python and R for sophisticated data science tasks.