Microsoft Azure Data Explorer with Advanced KQL

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About Course

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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What Will You Learn?

  • 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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