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Category: KQL

Monitoring DirectQuery Connection Openings in Power BI

Published 2024-08-12 by Kevin Feasel

Chris Webb digs into the numbers:

In the past I have blogged about how important the number of connections from a Power BI DirectQuery semantic model to a data source is for performance. It’s also true that in some cases opening a connection, or some of the operations associated with opening a connection, can be very slow. As a result it can be useful to see when your semantic model opens connections to a data source, and you can do this with Log Analytics.

Click through to see how you can do this and some of the information it provides.

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Building Real-Time Dashboards from Lakehouse Data in Microsoft Fabric

Published 2024-08-09 by Kevin Feasel

Dennes Torres gets around a limitation:

Real-Time dashboards are a great feature in Real Time Intelligence experience to monitor our data. However, by default it’s made to work only with Kusto Databases. The options to create a real time dashboard or to define its data source only accept Kusto Databases.

What if we would like to see in real time the information we have in a lakehouse as well? Let’s discover a solution for this.

Read on for the solution.

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Ad Hoc Data Exploration with Azure Data Explorer

Published 2024-08-01 by Kevin Feasel

Michal Bar introduces a new feature:

We are excited to introduce the new Data Exploration feature, designed to enhance your ability to delve deeper into the data presented on any Dashboard.

If the information you’re seeking isn’t readily available on the dashboard, this feature allows you to extend your exploration beyond the data displayed in the tiles, potentially uncovering new insights.

Directly from a dashboard, you can refine your exploration using a user-friendly, form-like interface. This intuitive and dynamic experience is tailored for insights explorers seeking insights based on high volumes of data in near real time.

Click through to see the new feature in action.

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Real-Time Intelligence in Microsoft Fabric

Published 2024-07-05 by Kevin Feasel

Dennes Torres takes a peek at a service with a new name:

When everyone starts to announce Real-Time Intelligence in Microsoft Fabric as something new, I need to double check what’s happening: Am I crazy or is everyone else? Wasn’t this already there?

Finally, I realize that Real-Time Intelligence is a new name for Real-Time Analytics, and they are doing this so fast we don’t even have time to notice the difference.

What’s Real-Time Intelligence and what’s the difference from Real-Time Analytics?

Read on for those answers.

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Updating Records in a Kusto Database

Published 2024-06-18 by Kevin Feasel

Vincent-Philippe Lauzon has an announcement:

Kusto databases, either in Fabric (KQL Database) or in Azure (Azure Data Explorer), are optimized for append ingestion.

In recent years, we’ve introduced the .delete command, allowing you to selectively delete records.

In February, we introduced the .update command in public preview.  This command allows you to update records by deleting existing records and appending new ones in a single transaction.

Today, the .update is Generally Available (GA)!

Click through for more details, including a link to the documentation, where you can see several examples of the syntax.

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Updating Records in a Kusto Database

Published 2024-02-21 by Kevin Feasel

Vincent-Philippe Lauzon shows off a feature now in public preview:

Kusto databases, either in Azure Data Explorer or in Fabric KQL Database, are optimize for append ingestion.

In recent years, we’ve introduce the .delete command allowing you to selectively delete records.

Today we are introducing the .update command.  This command allows you to update records by deleting existing records and appending new ones in a single transaction.

This command comes with two syntaxes, a simplified syntax covering most scenarios efficiently and an expanded syntax giving you the maximum of control.

Read on for more information and a pair of examples of how updating works.

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Using KQL in Azure SQL DB Audits

Published 2023-12-27 by Kevin Feasel

Josephine Bush tracks what’s happening on that Azure SQL Database:

According to Microsoft, “Kusto Query Language (KQL) is a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more. The query uses schema entities that are organized in a hierarchy similar to SQLs: databases, tables, and columns.”

Note: KQL is case-sensitive for everything. Also, remember to refrain from querying everything just like you wouldn’t with SQL — don’t do the equivalent of SELECT * from gianttable.

Microsoft also has a lot of documentation with best practices and a quick reference guide to the Kusto commands. This blog post covers the ones I use the most.

Read on for a primer on the language, specifically some of the things you can do when reading Azure SQL Database audit information.

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Exposing KQL Data in OneLake

Published 2023-11-28 by Kevin Feasel

Brian Bønk gets in on the Microsoft Fabric fun:

Microsoft has released the final piece of the current puzzle around the OneLake as a one-stop-shopping service for dat in Fabric. Until now we had only access to the KQL data in the KQL database.

With this addition, we can now finally say that OneLake is the one place for your data in Fabric.

Read on to see how you can make data in an existing KQL database usable in OneLake.

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Schema Validation with Kusto Databases

Published 2023-11-24 by Kevin Feasel

Vincent-Philippe Lauzon tests the schema:

Kusto allows you to very quickly get productive.  You can setup an ingestion pipeline in minutes that will ingest Terabytes (TBs) of data per day.

Like any piece of code, your database schema is as good as the intent you convey when you wrote it.  But over time, the intent diffuses and different priorities, authors and just plain miscommunication can diminish the quality of your code.

Read on to see what it does and the benefits it provides.

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Many-to-Many Power BI Relationships and Table Refreshes

Published 2023-11-20 by Kevin Feasel

Dany Hoter gives us a reason to minimize use of many-to-many relationships in Power BI:

I must admit that in the last two years I’ve told many Power BI/Kusto customers not to worry about relationships that are created as M:M.

I was pretty sure that with Direct Query, such relationships are fine,

Indeed, the generated queries looked fine and performed as expected.

I recently became aware that the number of queries generated for some visuals e.g. Matrix and tables can be affected by the type of relationships between the participating tables.

Read on for a description of why you shouldn’t load your Power BI semantic models with many-to-many relationships, especially once Kusto is involved.

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