Microsoft Azure Data Explorer with Advanced KQL Course Overview

Microsoft Azure Data Explorer with Advanced KQL Course Overview

The Microsoft Azure Data Explorer with Advanced KQL course is a comprehensive learning path for professionals who aim to master data analytics within the Azure environment. This course dives deep into Azure Data Explorer (ADX), a high-performance analytics service designed for real-time analysis on large volumes of data streaming from applications, websites, IoT devices, and more.

Starting with an overview of Azure Data Explorer and its architecture, learners will understand its key characteristics, use cases, and security aspects. They will then progress to building and managing the infrastructure, including cluster creation, scaling, and cost management. The core of the course focuses on querying data with KQL (Kusto Query Language), exploring its syntax, operators, and advanced features to manipulate and extract insights from data.

Learners will also gain skills in visualizing data, leveraging various tools such as Power BI and Grafana for compelling data representation. The course includes modules on monitoring ADX for optimal performance, user analytics, geographic analysis, and diagnostic analysis. Advanced topics cover time series analysis, anomaly detection, forecasting, and extending ADX capabilities using inline Python and R for sophisticated data analysis.

This course equips learners with the expertise to harness the full potential of Azure Data Explorer and KQL, making them valuable assets in data-driven organizations.

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850

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Course Fee 850
Total Fees
850 (USD)
  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request
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  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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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.

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