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Category: KQL

The Print Operator in KQL

Robert Cain continues a series on KQL:

In this post we’ll cover the print operator. This Kusto operator is primarly used as a development tool, to test calculations.

The samples in this post will be run inside the LogAnalytics demo site found at https://aka.ms/LADemo. This demo site has been provided by Microsoft and can be used to learn the Kusto Query Language at no cost to you.

Importantly, this is an operator and not a statement. This is in contrast to languages like T-SQL.

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Power BI Aggregations from Azure Data Explorer Data

Dany Hoter has some recommendations if you’re aggregating data from Azure Data Explorer into Power BI:

Every visual shown in a report in PBI, contains some form of aggregation

The question is how the aggregations are calculated and at which step in the pipe of bringing the data from the data source to the report.

In this article, I’ll be using data coming from Azure Data Explorer aka Kusto aka ADX.

Most of the content is relevant for other sources as well.

Read on for the advice, which I’d call fairly unexpected—I actually expected the recommendation to go the other way for performance reasons.

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Guidance on When to Use Azure Data Explorer

Tzvia Gitlin Troyna has a flow chart for us:

Azure Data Explorer is a big data interactive analytics platform that empowers people to make data driven decisions in a highly agile environment. The factors listed below can help assess if Azure Data Explorer is a good fit for the workload at hand. These are the key questions to ask yourself.

The following flowchart table summarize the key questions to ask when you’re considering using Azure Data Explorer.

In addition to the flow chart, there is a table of three common patterns of interaction which ADE can do well.

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Unit Testing ADX Functions

David Giard builds some tests:

Our application contains many functions that return data stored in Azure Data Explorer (ADX). We wrote these functions in Kusto Query Language (KQL) and each function returns a set of data based on the arguments passed. Although developers tested these functions as they wrote them, we needed a way to validate that the functions continued to work as the code and the data changed.

Automated Unit testing is an essential part of any application development life cycle. It validates that code works properly and minimizes the risk that future code changes will break existing functionality.

In this article, I will discuss the approach we took in automating the testing of ADX functions.

Click through to see how to use the assert() function and build some tests.

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Projecting (Selecting) Results with KQL

Robert Cain continues a series on the Kusto Query Language:

So far in my Fun With KQL series, we have used the column tool, found on the right side of the output pane and described in my original post Fun With KQL – The Kusto Query Language, to arrange and reduce the number of columns in the output.

We can actually limit the number of columns, as well as set their order, right within our KQL query. To accomplish this we use the project operator.

Read on for several good uses of the project operator.

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Bounding Box Queries in Azure Data Explorer

David Giard draws boxes:

For our current project, we are capturing into ADX the location of vehicles over time. Our customer asked us to create a function that would return all vehicles that are within a given bounding box in a given time period. This is useful information when they want to know when a vehicle returns to a building, a neighborhood, or a city.

In this article, I will show how this can be accomplished using built-in functions, the limitations of those functions, and ways to overcome those limitations.

Read on for the naive approach as well as a very interesting one using S2 cells.

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The KQL Extend Operator

Robert Cain continues a series on learning KQL:

When dealing with data, it’s not at all uncommon to want to create a new column of data by performing a calculation with two other columns. A common example is taking two stored columns, the purchase price of an item, and its shipping cost, then adding them together to get a column which wasn’t stored in your dataset, the total amount of the sale.

The Kusto Query Language lets you accomplish this through the extend operator. This operator allows you to manifest new columns in your output data, based on calculations.

As always, Robert has plenty of examples available to view.

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