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Author: Kevin Feasel

Cross-Database and Cross-Cluster ADX Joins in Power BI

Published 2023-08-17 by Kevin Feasel

Dany Hoter makes a connection:

You may have more than one ADX database and probably more than one ADX cluster.

In some cases, you want to join tables or functions from more than one database/cluster.

In this article you’ll see how to make sure that such joins are folded and sent to the ADX backend instead of executing at the level of the Power Query mashup engine.

Everything mentioned here is applicable to Azure Data Explorer, Synapse Data Explorer, and Fabric RTA.

Read on for the two examples.z

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Deploying Resource Governor with Minimal Blocking

Published 2023-08-17 by Kevin Feasel

Michael J. Swart doesn’t want to wait (or cause anyone else to):

Just like sp_configure, Resource Governor is configured in two steps. The first step is to specify the configuration you want, the second step is to ALTER RESOURCE GOVERNOR RECONFIGURE.
But unlike sp_configure which has a “config_value” column and a “run_value” column, there’s no single view that makes it easy to determine what values are configured, and what values are in use. It turns out that the catalog views are the configured values and the dynamic management views are the current values in use:

Read on for a variety of scripts to help configure resource governor.

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Creating a Log Analytics Workspace

Published 2023-08-17 by Kevin Feasel

Gilbert Quevauvilliers begins a new series:

As with most of my blog posts it involves a client from a customer where I am consulting, which I think will help others.

The requirement was to analyse the Power BI Query usage patterns of the users. The initial requirement was to find out how many users were using Excel to gain access to the Power BI Dataset.

I knew that I could get this using Azure Log Analytics. Not only could I find out how many users are using Excel, but I could also find out what queries they are running, how long they took.

Read on for the first part in this series, which details setting up Azure Log Analytics.

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Stream Processing with Flink and Kafka

Published 2023-08-16 by Kevin Feasel

Konstantin Knauf starts a new series:

There was a huge amount of buzz about Apache Flink® at this year’s Kafka Summit London. From an action-packed keynote to standing-room only breakout sessions, it’s clear that the Apache Kafka® community is hungry to learn more about Flink and how the stream processing framework fits into the modern data streaming stack.

That’s why we’re excited to introduce our new “Inside Flink” blog series that takes a deeper look at why developers and organizations everywhere are shifting their stream processing technologies to Flink. Our first blog post explains what Flink is and how it can enhance your streaming use cases running on Kafka. Future topics will include common Flink use cases, an inside look at Flink SQL, and much more.

Click through for the first post in the series, which covers what Flink is and how the two products can interoperate.

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Creating a Calendar View in Power BI

Published 2023-08-16 by Kevin Feasel

Martin Schoombee needs a calendar:

It’s pretty sad that we don’t have a built-in calendar visualization in Power BI, and the custom visuals in the marketplace don’t have everything I need/want for my own internal reporting…so I decided to experiment a little and see how close I could get with the standard graphs that are available.

Read on to see how close Martin could get. It’s actually more calendar-looking than I would have expected, though also frustratingly limited.

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Solving Systems of Equations in R

Published 2023-08-16 by Kevin Feasel

Steven Sanderson needs a solution:

In mathematical modeling and data analysis, it is often necessary to solve systems of equations to find the values of unknown variables. R provides the solve() function, which is a powerful tool for solving systems of linear equations. In this blog post, we will explore the purpose of solving systems of equations, explain the syntax of the solve() function, and provide three examples of increasing complexity to demonstrate its usage.

This post got me thinking about linear programming, which is a different topic but still pretty easy to do in R.

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ANSI_PADDING and Its Relevance

Published 2023-08-16 by Kevin Feasel

Vitaly Bruk won’t be late to meetings anymore:

Today, I have late for the team meeting … 🙁

When I joined a Teams meeting, the team discussed ANSI_PADDING. One of the teammates fires at me with the teacher’s voice: “What are we talking about? What is ANSI_PADDING? Shoot!”.

That was a joke, but … just in case, let’s see what is this.

Read on to learn what the ANSI_PADDING attribute is and why it’s relevant.

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Don’t Start Stored Procedure Names with sp_

Published 2023-08-16 by Kevin Feasel

Chad Callihan provides solid advice:

Everyone has an opinion on naming stored procedures. One opinion that can be agreed upon is that starting a stored procedure with “sp_” is not the way to go. Using “sp_” can only do harm. Let’s take a look at why that is.

Read on for Chad’s reasoning.

I don’t like prefixes at all for stored procedures (or tables). It’s a common misunderstanding of Hungarian notation, as you don’t provide any new and relevant information in the object name: of course it’s a stored procedure; I’m using “EXEC” to execute it, so what else could it be?

The exception to a very good rule is something which you intend to put in the master database and execute from any database context. The best example of this is sp_whoisactive.

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Dense Rank in KQL

Published 2023-08-15 by Kevin Feasel

Robert Cain continues a series on KQL:

The Kusto Windowing Function row_rank_dense is an interesting function. It lets you get a unique count of a specific column in a dataset. Unlike other methods of getting counts, row_rank_dense allows you to see each individual row of data.

First though, if you’ve not read the introductory post on Windowing Functions, Fun With KQL Windowing Functions – Serialize and Row_Number, you should do so now as it introduced several important concepts needed to understand how these Windowing Functions work.

Read on to see how this window function works.

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Adaptive Query Execution in Spark 3.0

Published 2023-08-15 by Kevin Feasel

The Big Data in Real World team talks on-the-fly execution plan changes:

Apache Spark, the popular distributed computing framework, has been widely adopted for processing large-scale data. With the release of Apache Spark 3.0, a groundbreaking feature called Adaptive Query Execution (AQE) was introduced. AQE addresses the limitations of traditional static execution plans by dynamically optimizing query execution based on runtime statistics. In this blog post, we will explore how AQE works and how it significantly improves the performance of Spark applications.

This sounds pretty similar to adaptive query processing in SQL Server, though a look at the Spark documentation shows that there are some practical differences in implementation versus what SQL Server does.

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