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Category: KQL

Choosing a SKU for Azure Data Explorer

Brian Bønk makes a choice:

When creating the clusters from the Azure portal, you are presented with 3 options when choosing the compute specification.

The compute specification is the method of setting up the clusters for the specific workload you are planning to put on the Kusto cluster.

The portal gives you these three options:

Read on for the options, as well as some recommendations on when you might choose each.

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Tips for Power BI Modeling with ADX

Dany Hoter shares some tips on creating star schema models with Azure Data Explorer:

Relationships between DQ tables are created as M:M by default. This is not a problem and even recommended with single direction.

Read on for several tips. What’s interesting as I read this is just how radically different the advice is for ADX utilization versus Power BI utilization, such as using strings to join dimensions to facts. That would be heresy in a Kimball-style model and is a common cause for slow-down in Power BI. Yet that’s the recommendation here for working with ADX, unless I’m misunderstanding Dany’s post.

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ADX Dashboards Now Generally Available

Michal Bar provides an overview of Azure Data Explorer functionality now generally available :

Each ADX dashboard is a collection of tiles, optionally organized in pages, where each tile has an underlying query and a visual representation. Using the web UI, you can natively export Kusto Query Language (KQL) queries to a dashboard as visuals and later modify their underlying queries and visual formatting as needed. In addition to ease of data exploration, this fully integrated Azure Data Explorer dashboard experience provides improved query and visualization performance.

Read on to learn more.

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The Let Operator in KQL

Robert Cain continues a series on KQL:

Let me tell you about let, my favorite operator in the Kusto Query Language. Why my favorite?

It is extremely flexible. It lets you create constants, variables, datasets, and even reusable functions. Let me tell you, it’s very powerful.

My big problem with let, specifically with variable creation, is that the variables do not persist between batches. You can use variables between statements but only if you execute all relevant statements in one batch. This makes it harder for exploratory query building.

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Data Updates in Azure Data Explorer

Hiram Fleitas updates the data:

I recently ran into a Kustomer that migrated from TSI to ADX (Azure Data Explorer). They were really excited about using Kusto Trender but one item they couldn’t wrap their head around was how to update their hierarchy table(s) in ADX. i.e.  

- Contoso WindFarm Hierarchy (Levels: Plant > Unit > System > Name)
-- Plant
--- Unit
---- System
----- Name 

As a big data platform ADX is an append-only data store, so we don’t have the options to do updates, right? Well, that’s not completely true. We absolutely don’t support updates, but we do have a couple options to simulate updates.

Read on to see what options are available to you.

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Take Any from KQL

Robert Cain isn’t picky:

The take_any function is a random row generator. Based on the parameters passed it, it will select a random row from the dataset being piped into it. It also has a variant, take_anyif, we’ll see both in this post.

Note that take_any was originally called any and was renamed. While any still works, it has been deprecated and you should now use take_any.

As always, Robert shares plenty of examples of how the operator works, so check it out.

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Max and Min Functions in KQL

Robert Cain goes extreme:

The max and min aggregation functions are common to almost every language, and the Kusto Query Language is no exception. As you would think, when you pipe in a dataset max returns the maximum value for the column name you pass in. Likewise min returns the lowest value.

In addition, there are variants for each, maxif and minif. We’ll see examples for all of these in this post.

Click through for a few functions you can call via the summarize operator.

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Top-nested in KQL

Robert Cain continues a series on KQL:

Back in June of 2022 I covered the top operator in my Fun With KQL – Top post. We showed how to create your own top 10 lists, for example what were the top 5 computers ranked by free disk space.

What if you needed your top results in a nested hierarchy? For example, you wanted to know which three objects in the Perf table had the most entries? But, for each one of those, what were the three counters with the most entires?

That’s where the top-nested operator comes in. It allows you to create top lists in nested, also called hierarchical levels.

Click through for the normal slew of examples on how to use this operator.

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