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Course Overview
The Microsoft Azure Data Explorer with Advanced KQL offered by CounselTrain provides an in-depth exploration of data analytics within the Azure ecosystem. This course focuses on mastering Azure Data Explorer (ADX), a powerful analytics service designed for real-time analysis of large data volumes from applications, websites, IoT devices, and more.
The course begins with an overview of Azure Data Explorer, covering its architecture, key features, use cases, and security considerations. Learners will then progress to building and managing ADX infrastructure, including cluster creation, scaling, and cost management. Central to the course is an in-depth study of Kusto Query Language (KQL), including its syntax, operators, and advanced features for data manipulation and insight extraction.
Participants will also develop skills in data visualization, using tools like Power BI and Grafana to create compelling data representations. The course includes modules on monitoring ADX performance, conducting user and geographic analysis, and performing diagnostic analysis. Advanced topics cover time series analysis, anomaly detection, forecasting, and extending ADX capabilities with inline Python and R for sophisticated data analysis.
By completing this course, learners will acquire the expertise to fully leverage Azure Data Explorer and KQL, enhancing their ability to drive data-driven decision-making and become valuable assets in any data-centric organization.
Learning Options
Schedule Dates
Microsoft Azure Data Explorer with Advanced KQL Course
Microsoft Azure Data Explorer with Advanced KQL Course
Microsoft Azure Data Explorer with Advanced KQL Course
Microsoft Azure Data Explorer with Advanced KQL Course
Course Content
- What Is Azure Data Explorer and Why Should I Use It?
- ADX Key Characteristics and Use Cases
- ADX Architecture, Components, and Scalability
- ADX Security
- Understanding and Creating Azure Data Explorer Infrastructure
- ADX Cost: Selecting the SKU for Your Use Case
- Creating a Cluster
- Managing Cluster Scaling
- Creating a Database
- Managing Database Permissions
- The Azure Data Explorer Web UI
- Getting to Know the Kusto Query Language (KQL)
- Querying Azure Data Explorer, the Help Cluster, and the Sample Database2m
- Getting Started with Kusto Control Commands
- The Basics of KQL - Most Commonly Used Operators
- More KQL Operators
- Querying Data in Azure Monitor and Using the Flow Kusto Connector
- Visualizing the Results of a Query with the Render Operator
- Data Visualization Using the Azure Data Explorer Dashboard
- Visualizing Data Using Power BI
- Visualizing Data in Kibana with the K2Bridge Open-source Connector
- Visualizing Data in Tableau with the ODBC Connector
- Visualizing Data in Sisense with the JDBC Connector
- Monitoring in Azure Data Explorer
- Using Metrics to Monitor Cluster Health
- Adding Diagnostic Logs to Monitor Ingestion
- Use Resource Health to Monitor Cluster Health
- Troubleshooting
- Introduction
- Sliding Window Counts
- Active User Counts
- Activity Counts Metrics
- Activity Metrics
- Activity Engagement
- Nearby Events: Circle
- Nearby Events: Line
- Geofencing
- Clustering
- Geospatial Joins
- Introduction to Diagnostic and Root Cause Analysis
- Using Autocluster
- Using Basket
- Using Diffpatterns
- Performing Verification
- Make Series
- Series FIR
- Series Fit Line and Fit 2 Lines
- Seasonality Detection
- Series Subtract
- Time Series at Scale
- Decomposition
- Anomaly Detection
- Forecasting
- Scalability
- Introduction to Calling Python and R from KQL
- The Mechanics of Calling Python
- Time Series Analysis Using Python's Numpy
- Using Python's K Means Clustering from KQL
- Calling R from KQL
FAQs
Participants should have a basic understanding of data analysis concepts and experience with Azure Data Explorer or similar data platforms. Familiarity with basic KQL is also recommended.
You will learn to:
- Perform advanced data analysis using KQL in Azure Data Explorer.
- Create complex queries and visualizations.
- Optimize query performance and manage data efficiently.
- Apply best practices for analyzing large datasets with ADX.
Yes, the course includes practical labs where you will apply advanced KQL techniques to analyze and visualize data using Azure Data Explorer.
To enroll, visit the CounselTrain website and complete the registration process. For additional assistance, reach out to our support team.
The course enhances your ability to perform advanced data analysis and visualization using Azure Data Explorer, boosting your expertise and career prospects in data analysis and engineering.
Duration
Language
Batch Options
Upcoming Batch
Soft copy of Courseware
Certificate of Course Complition
Experienced and Professional Trainer
Lab and Exercises During Training 
Sample Questions for Exam Prepration
Mock Test on the Last Day of Training 
Exam Registration Guidance 
5-Star Training Environmment