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Month: May 2021

An Introduction to Power BI Goals

Published 2021-05-14 by Kevin Feasel

Imran Burki brings us an introduction to Power BI Goals:

One of the things I love about Power BI (and Microsoft in general) is that they empower everyone in the organization to utilize their software – Power BI Goals are so easy to set up. There’s absolutely no special skillset required. You just need a Power BI Premium or Premium Per User license. Power BI Goals essentially enable you to keep track of key performance indicators in a single, unified view. Goals, and the actuals, are data driven. Goals can also be hardcoded. The data for actuals and goals must reside in a report that you can access.

Click through for an example as well.

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Types of Memory Contention

Published 2021-05-14 by Kevin Feasel

Erik Darling is overdrawn at the memory bank (which was, sadly, not a very good MST3K episode):

Whomever decided to give “memory bank” its moniker was wise beyond their years, or maybe they just made a very apt observation: all memory is on loan.

Even in the context we’ll be talking about, when SQL Server has lock pages in memory enabled, the pages that are locked in memory may not have permanent residency.

If your SQL Server doesn’t have enough memory, or if various workload elements are untuned, you may hit one of these scenarios:

There are three of them, which is really that there are two of them but they can join forces in an effort to make your life a pain.

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Finding Index Fragmentation

Published 2021-05-14 by Kevin Feasel

Deepthi Goguri is hunting the most dangerous predator:

The bad page splits are the splits that we learned in the previous post which is the split that occurs when a random insert has to happen and there is no space on page, a new page gets created during the page split. These page splits are very expensive causing the fragmentation. Good page splits occurs when the append only inserts happen as the pages gets filled on the index pages to the right side of the index and new pages gets added as they gets filled up to the right side of the index. These types of good page splits doesn’t cause any index fragmentation. SQL Server will group these two types of page splits together and do not differentiate between them. So, how do we know to differentiate between the good and the bad page splits? Let’s learn more about this.

It is very difficult to differentiate these page splits by using the existing methods we have in the SQL Server like using the perfmon counter which has the pagesplits/sec counter. This counter will give the good and the nasty page splits together. There is a DMV sys.dm_db_index_operational_stats and an extended event page_split event to track the page splits.

Read on to see how we can find those undesirable page splits versus the benign ones.

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Resetting a Consumer Offset in Kafka

Published 2021-05-13 by Kevin Feasel

The Hadoop in Real World team shows how to update the consumer offset in Kafka:

In some scenarios, consumers which consumed the messages from a Kafka partition could have resulted in errors and the consumption would have been incomplete. In such cases of consumption failures you may have a need to re-consume the messages which were previously consumed. In such instances you would have to reset the consumer offset to an earlier offset.

This is one of the big advantages of a log-based message broker versus a queue: if you find a bug in a downstream consumer, it’s easy to generate correct results after fixing the bug, something which can be much harder to do otherwise.

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Showing Ranges of Data with a Single Slicer

Published 2021-05-13 by Kevin Feasel

Marco Russo and Alberto Ferrari have another good use case for calculation groups:

Because the axis of the visual must show months outside of the slicer selection, we cannot use the usual Date[Calendar Year Month] column. Indeed, if we put the Date[Calendar Year Month] column on the X-axis, the only visible month would be the selected month. It is worth remembering that the selection of the values to show on the axis is independent from the measure. If a slicer is filtering one month, there is no way to show additional months from the same table on either the rows or the columns (or the X-axis, as in the example).

Therefore, we must create a separate table that is not subject to filtering from the slicer. This way, columns from that table show all the rows, and we can control their visibility through a measure. Once the new table is in place, we write a measure that produces a value for only the last six months out of all the months visible, and leaves the remaining months blank in order to hide them.

Read on to see how.

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Data Hubs, Warehouses, and Lakes

Published 2021-05-13 by Kevin Feasel

Trevor Legg compares and contrasts data hubs, data warehouses, and data lakes:

Data hubs, data warehouses, and data lakes are significant investment areas for data and analytics leaders and are vital to support increasingly complex, distributed, and varied data workloads.

Gartner finds that 57% of data and analytics leaders are investing in data warehouses, 46% are using data hubs, and 39% are using data lakes. However, they also found that these same data and analytics leaders don’t necessarily understand the difference between the three…

To best support specific business requirements, it’s vital to understand the difference and purpose of each type of structure, and the role it can play in modern data management infrastructure.

Click through for the definitions and comparisons.

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Ending the Number Series Generator Challenge

Published 2021-05-13 by Kevin Feasel

Itzik Ben-Gan wraps up a great series:

This is the fifth and last part in the series covering solutions to the number series generator challenge. In Part 1, Part 2, Part 3 and Part 4 I covered pure T-SQL solutions. Early on when I posted the puzzle, several people commented that the best performing solution would likely be a CLR-based one. In this article we’ll put this intuitive assumption to the test. Specifically, I’ll cover CLR-based solutions posted by Kamil Kosno and Adam Machanic.

Many thanks to Alan Burstein, Joe Obbish, Adam Machanic, Christopher Ford, Jeff Moden, Charlie, NoamGr, Kamil Kosno, Dave Mason, John Nelson #2, Ed Wagner, Michael Burbea, and Paul White for sharing your ideas and comments.

Read on for the conclusion.

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From Azure Analysis Services to Power BI Premium Per User

Published 2021-05-13 by Kevin Feasel

Gilbert Quevauvilliers picks back up on a new series:

Welcome to the first in my blog post series on evaluating the different aspects when looking to migrate from Azure Analysis Services (AAS) to Power BI Premium Per User (PPU).

Apologies for this taking a few extra weeks to get started, life has been super busy, but as they say “Better late than never”.

In this post I am going to compare the Query Performance of an AAS Cube compared to a PPU Cube.

Click through to see how Power BI Premium Per User stacks up against Azure Analysis Services.

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Checking Out Power BI Goals

Published 2021-05-13 by Kevin Feasel

Treb Gatte takes a step back:

In part 1, I gave you an introduction to Power BI Goals and provided some context on what information could be tracked with the feature. I received some feedback that it would be great to do an “unboxing” post, to get a look at the feature. Thus, this post will be that. Part 3 will be a Power BI Goals 101 post where I step you through the process.

Power BI Goals requires you to either have a Power BI Premium per user license or to have access to Power BI Premium and have a Power BI Pro license assigned to you. If you do, then you will notice a new trophy icon when you go to PowerBI.com.

Click through for the overview.

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