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Category: Microsoft Fabric

Cache Management and Semantic Link in Fabric Notebooks

Published 2023-10-12 by Kevin Feasel

Marc Lelijveld warms up the cache:

In the previous blog, I wrote about data temperature as part of Fabric when you’re using Direct Lake storage mode. In that blog, I explained how you can get insights in the temperature of a column, what that temperature means and what the impact of the temperature is on columns that are queried more often.

In this blog, I will continue this story by elaborating on a process called framing and how you can influence data eviction to drop data from memory. Finally, this blog goes into more details on how you could use Semantic Link in Fabric Notebooks to warm up the data for most optimal end-user performance.

The SQL Server analog here is having some automated queries which keep specific pages in the buffer pool, like a warm-up script for an instance with plenty of memory but slow disks.

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Refreshing a Direct Lake Power BI Dataset in Microsoft Fabric

Published 2023-10-12 by Kevin Feasel

Chris Webb refreshes our memories:

If you’ve heard about the new Direct Lake mode for Power BI datasets in Fabric you’ll know that it gives you the query performance of Import mode (well, almost) without the need to actually import any data. Direct Lake datasets can be refreshed though – in fact, they refresh automatically by default – and if you look at the dataset’s Refresh History you’ll see there’s a Direct Lake section which sometimes shows errors:

Chris goes on to ask and answer the question, what does it mean to refresh a Direct Lake dataset if you’re not actually importing the data into Power BI?

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A Path to Avoid Getting Overwhelmed with Microsoft Fabric

Published 2023-10-10 by Kevin Feasel

Kurt Buhler tries to limit information overload:

It’s just too much; I don’t have time for all this stuff.

I think this is a big problem. It’s a problem not just because people shouldn’t feel overwhelmed, but also because it says something about how effectively these new features, tools, and resources are being communicated, understood, and used. But what is the problem, exactly? And if you’re in the minority of people not feeling overwhelmed, why should you care?

Perhaps most importantly, how can we approach these new features, tools, and resources to ensure we understand them and can find value without feeling overwhelmed?

Read on for several tips on how to tackle learning about a product with a large surface area. And I’d also note that anybody who is comfortable working in SQL Server had to go through the same process.

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Workaround for Primary Keys in Fabric Data Warehouses

Published 2023-10-09 by Kevin Feasel

Gilbert Quevauvilliers needs a key:

When I started looking into using the data warehouses feature in Fabric, I did see that there were limitations on Primary Key columns.

Below is my blog post on how I still use keys in my data warehouse, instead of using GUID’s which to me are long and hard to use.

In my example I am going to create a simple data warehouse which is going to consist of two-dimension tables (Date and Country) and a fact table with the Sales amounts.

This seems sub-optimal, though at least Gilbert shows us a workaround.

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Data Temperature in Microsoft Fabric

Published 2023-09-29 by Kevin Feasel

Marc Lelijveld breaks out the thermometer:

As part of Microsoft Fabric, a new storage mode to connect from Power BI to data in OneLake has been introduced. Direct Lake it makes to possible to use your data from OneLake in Power BI without taking an additional copy of the data. Where Direct Lake promises to deliver the performance of Import-mode with the real-time capabilities of Direct query, it is time to have a closer look how data gets loaded into memory and delving into the concept of data dictionary temperature.

In this blog I will explain when data gets loaded into memory, elaborate on how you can measure the dictionary temperature of your data and the effect of queries on the temperature.

Click through to see what affects this measure and how.

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Shortcuts Beat Duplication in Microsoft Fabric

Published 2023-09-27 by Kevin Feasel

Reza Rad makes a recommendation:

Microsoft Fabric uses OneLake as the single logical layer for storage management across the Fabric. OneLake offers Domains and workspaces to work with Fabric Items, and amid all those structures, you may require a table or file from one Domain to be used in another. OneLake’s Shortcut feature will give you this option without duplicating the data. In this article, I will explain what Shortcuts are and their importance in building an analytics solution.

Click through for a video and an article.

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Setting a Spark Compute Pool Size in Microsoft Fabric

Published 2023-09-26 by Kevin Feasel

Reitse Eskens manages compute pools:

This next blog won’t be a long one and will probably serve most as a reminder for myself where to find the settings for the Spark compute pool.

When you create a workspace, you get the default starter pool and it has taken me way longer than I care to admit to find where to find the setting and, more importantly, how to change it.

Read on to learn more about how to create a Spark pool of the size you desire. The sizing method is essentially the same as with Azure Synapse Analytics.

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Maintaining Existing Power BI Data while Loading More with Fabric

Published 2023-09-25 by Kevin Feasel

Chris Webb looks back on an older post:

To be honest I’m slightly ashamed of this fact because, as I say in the post, the solution I describe is a bit of a hack – but at the same time, the post is popular because a lot of people have the problem of needing to add new data to the data that’s already there in their Power BI dataset and there’s no obvious way of doing that. As I also say in that post, the best solution is to stage the data in a relational database or some other store outside Power BI so you have a copy of all the data if you ever need to do a full refresh of your Power BI dataset.

Why revisit this subject? Well, with Fabric it’s now much easier for you as a Power BI developer to build that place to store a full copy of your data outside your Power BI dataset and solve this problem properly.

Read on for an example of the new solution.

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