Overview

Module 1: Evaluate whether Azure Data Explorer is appropriate to process and analyze your big data.
By the end of this module, you're able to:
- Evaluate whether Azure Data Explorer is appropriate to process and analyze your big data.
- Describe how the features of Azure Data Explorer work to turn your data streams into meaningful insights.
Module 2: Learn about the basics of Kusto Query Language (KQL), and the various Microsoft products that use it.
By the end of this module, you'll be able to:
- Identify common elements of a query
- Describe key features of a Kusto Query Language (KQL) query
- Describe the different environments in which you can use KQL
Module 3: Learn how to write simple queries in Kusto Query Language (KQL) by using the operators take, project, count, where, and sort.
By the end of this module, you'll be able to:
- Write your first query with KQL.
- Use KQL to explore data by using the most common operators.
Module 4: Learn how to write advanced queries in Kusto Query Language (KQL) by using the aggregation functions, the render operator, and variables.
By the end of this module, you'll be able to:
- Use the Kusto Query Language to gain insights from your data by using the aggregation functions count, dcount, countif, sum, min, max, avg, percentiles, and others
- Communicate query results visually using the render operator
- Assign variables by using a let statement
Module 5: Learn how to write Kusto Query Language (KQL) queries to combine and retrieve data from two or more tables by using the `lookup`, `join`, and `union` operators.
By the end of this module, you'll be able to:
- Use Kusto Query Language to combine and retrieve data from two or more tables by using the lookup, join, and union operators.
- Optimize multi-table queries by using the materialize operator to cache table data.
- Enrich your insights by using the new aggregation functions arg_min and arg_max.
Module 6: Use Azure Data Explorer to characterize an unfamiliar dataset. Explore the schema of the data, run queries to examine the range of the data, and share these insights with others.
By the end of this module, you'll be able to:
- Characterize an unfamiliar dataset using Azure Data Explorer queries and web UI.
- Share queries and query outputs.
Module 7: Learn how to create dashboards in Azure Data Explorer. You'll add tiles and visualizations, and then share the dashboard with others.
By the end of this module, you'll be able to:
- Generate dashboards that display multiple insights at a glance.
- Share dashboard insights with colleagues.

Syllabus

Module 1: Module 1: Introduction to Azure Data Explorer
- Introduction
- What is Azure Data Explorer?
- How Azure Data Explorer works
- When to use Azure Data Explorer
- Knowledge check
- Summary
Module 2: Module 2: Explore the fundamentals of data analysis using Kusto Query Language (KQL)
- Introduction
- Query language basics
- KQL query environments
- How a KQL query is built
- Exercise: Sample queries
- Types of KQL queries
- Exercise: Different types of KQL queries
- Knowledge check
- Summary
Module 3: Module 3: Write your first query with Kusto Query Language
- Introduction
- Understand the basic structure of a Kusto query
- Exercise - Connect to resources
- Exercise - Return a specific number of rows by using the take operator
- Exercise - Select columns to return by using the project operator
- Exercise - Filter data by using the where operator
- Exercise - Reorder returned data by using the sort operator
- Challenge
- Solution
- Knowledge check
- Summary
Module 4: Module 4: Gain insights from your data by using Kusto Query Language
- Introduction
- Group data using aggregate functions
- Exercise - Connect to resources
- Exercise - Count events using the count function
- Exercise - Visualize data with the render operator
- Exercise - Summarize data using aggregate functions
- Exercise - Introduce variables using the let statement
- Challenge
- Solution
- Knowledge check
- Summary
Module 5: Module 5: Write multi-table queries by using Kusto Query Language
- Introduction
- Combine and optimize data
- Exercise - Connect to resources
- Exercise - Combine table results by using the join operator
- Exercise - Combine table results by using the lookup operator
- Exercise - Combine table results by using the union operator
- Exercise - Optimize queries by using the materialize function
- Knowledge check
- Summary
Module 6: Module 6: Characterize an unfamiliar dataset with Azure Data Explorer
- Introduction
- Exercise: Connect to resources
- Characterize data with Azure Data Explorer
- Exercise: Take a first look at your data
- Explore the data structure using queries
- Exercise: Use queries to explore your data
- Exercise: Use queries to explore trends
- Exercise: Share queries with others
- Knowledge check
- Summary
Module 7: Module 7: Create dashboards in Azure Data Explorer
- Introduction
- Exercise: Connect to resources
- Dashboards in Azure Data Explorer
- Exercise: Create a dashboard from a query
- Exercise: Add a new tile
- Dashboard parameters
- Exercise: Create a dashboard parameter
- Exercise: Create a cross-filter
- Share dashboards
- Knowledge check
- Summary