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learn-pr/azure/multi-table-queries-with-kusto-query-language/1-introduction.yml

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unitType: introduction
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title: This unit introduces you to writing Kusto Query Language (KQL) queries to combine and retrieve data from two or more tables.
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description: Introduction
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ms.date: 01/03/2024
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ms.date: 03/07/2025
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author: shsagir
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learn-pr/azure/multi-table-queries-with-kusto-query-language/2-multi-table-queries.yml

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unitType: learning-content
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title: Combine and optimize data
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description: In this unit, you learn about various ways to combine and optimize data by using Kusto queries.
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ms.date: 03/07/2025
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author: shsagir
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learn-pr/azure/multi-table-queries-with-kusto-query-language/3-exercise-connect-to-resources.yml

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unitType: exercise
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title: Exercise - Connect to resources
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description: In this exercise, you learn how to connect to a database that holds the sales data.
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ms.date: 03/07/2025
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learn-pr/azure/multi-table-queries-with-kusto-query-language/4-exercise-join-types.yml

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title: Exercise - Combine table results by using the join operator
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description: In this exercise, you'll learn how to combine table results by using the join operator.
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description: In this exercise, you learn how to combine table results by using the join operator.
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author: shsagir
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learn-pr/azure/multi-table-queries-with-kusto-query-language/5-exercise-lookup.yml

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title: Exercise - Combine table results by using the lookup operator
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description: In this exercise, you'll combine table results by using the lookup operator.
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description: In this exercise, you combine table results by using the lookup operator.
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author: shsagir
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learn-pr/azure/multi-table-queries-with-kusto-query-language/6-exercise-union.yml

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title: Exercise - Combine table results by using the union operator
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description: In this exercise, you'll use the union operator to combine sales facts that were collected from various countries/regions.
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description: In this exercise, you use the union operator to combine sales facts that were collected from various countries/regions.
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learn-pr/azure/multi-table-queries-with-kusto-query-language/7-exercise-materialize.yml

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title: Exercise - Optimize queries by using the materialize function
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description: In this exercise, you'll use the materialize function to optimize queries.
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description: In this exercise, you use the materialize function to optimize queries.
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learn-pr/azure/multi-table-queries-with-kusto-query-language/8-knowledge-check.yml

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title: Knowledge check
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description: "Review what you learned about multi-table queries in the Kusto Query language."
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learn-pr/azure/multi-table-queries-with-kusto-query-language/9-summary.yml

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title: Summary
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title: summary
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title: Summary
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description: "In this module, you learned how to combine data from several tables to gain further insights from the enriched data."
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learn-pr/azure/multi-table-queries-with-kusto-query-language/includes/4-exercise-join-types.md

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In this exercise, you learn how to use the `join` operator. Recall that the `join` operator merges the rows of two tables by matching values of the specified columns from each table.
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Let's use the results of the `join` operator to answer questions about sales.
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Let's use the results of the `join` operator for answering questions about sales.
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## Use the `join` operator
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Your sales team wants to know the total sales by product category. When you start reviewing the available data, you realize that you need the *Products* table to get the list of product categories and the *SalesFact* table to get the sales data. You also realize that you want to count the sales for each category and list all product categories.
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Having analyzed the request, you choose to use the *rightouter* `join`, because it returns all sales records from the right table, enriched with matching data product category from the left table. You write your query by using the *Products* table as the left dimension table, matching data from the *SalesFact* facts table, and grouping the result by product category.
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After analyzing the request, you decide to use the *rightouter* `join`, because it returns all sales records from the right table, enriched with matching data product category from the left table. You write your query by using the *Products* table as the left dimension table, matching data from the *SalesFact* facts table, and grouping the result by product category.
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1. Run the following query.
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