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Day: May 13, 2021

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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