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Category: Microsoft Fabric

Building a Data Warehouse in Microsoft Fabric

Reza Rad continues a video series on Microsoft Fabric:

Microsoft Fabric Data Warehouse is a database system that stores data in OneLake and provides a medium to interact with the database using SQL commands. The Fabric Data Warehouse, which is also called Data Warehouse, or in short, Warehouse, also provides a powerful computing engine behind the scene to account for large volumes of data and support a fast-performing database system. The term Data Warehouse comes from the fact that this is not usually a place to store transactional data for an operational system (for that, you can use Azure SQL Database). A Data Warehouse, in generic Business Intelligence terminology, is a place where you would store the data that needs to be analyzed.

Reza also explains how the warehouse differs from a lakehouse.

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Data Governance and Microsoft Fabric

Matthew Roche digs deeper into data governance in Microsoft Fabric:

One of the most underappreciated benefits of Power BI as a managed SaaS data platform has been the “managed” part. When you create a report, dataset, dataflow, or other item in Power BI, the Power BI service knows everything about it. Power BI is the authoritative system for all items it contains, which means that Power BI can answer questions related to lineage (where does the data used by this report come from?) and impact analysis (where is the data in this dataset used?) and compliance (who has permissions to access this report?) and more.

If you’ve ever tried to authoritatively answer questions like these for a system of any non-trivial scope, you know how hard it is. Power BI has made this information increasingly available to administrators, through logs and APIs, and the community has built a wide range of free and paid solutions to help admins turn this information into insights and action. Even more excitingly, Power BI keeps getting better and better even as the newer parts of Fabric seem to be getting all of the attention.

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First Look at Loading Data into Fabric

Reitse Eskens digs into Microsoft Fabric:

In my previous blog, I wrote about some first impression working with Fabric and mostly following the Lakehouse tutorial provided by Microsoft. Well structured as this one may be, the sizes are not like the sizes I’m seeing in the wild. So I decided to give Fabric a bit more of a challenge by letting it loose on my TPC-H dataset.

Click through for Reitse’s early analysis.

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Data Pipelines and Dataflows in Fabric Data Factory

Reza Rad has two videos and posts for us. First up is a primer on data pipelines in Microsoft Fabric Data Factory:

The Pipeline comes from Azure Data Factory. A Pipeline is a group of activities bundled together into a workflow. For example, a Pipeline can generate a process around the Dataflow. For example, you may want to run a Dataflow in a loop until something happens, and with the failure or success of each execution, you want to perform a task such as sending out an email, copying data somewhere, running a stored procedure, etc.

Reza then gets into Dataflows:

Through the years, the Data Transformation engines evolved. In the past, much coding was involved, and the user interface was not the best experience. These days, most actions can be done through pre-built transformations; less coding is needed, and a hardcore developer is not needed for preliminary tasks. This enables citizen data engineers to work with these tools.

Power Query is the data transformation engine of the new generation of Microsoft Data Integration tools and services. Power Query is the data transformation engine used in Power BI. However, Power Query can be used as a standalone cloud-based data transformation service when it is used as Dataflow. Dataflow is the ETL in the cloud offered by Microsoft, which uses the Power Query engine.

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Microsoft Fabric vs Synapse

Nikola Ilic shares some thoughts:

I’ve already introduced Microsoft Fabric in the previous article, so if you’re still not sure what is it all about and why you can think of Fabric as your “data football team”, I strongly encourage you to check that article. Additionally, there are many great articles and videos, both from Microsoft and the community, where you can find out more about Fabric and its various scenarios and components.

In the above-mentioned article, I scratched the surface of the inevitable topic that now comes into focus: “What now for Azure Synapse Analytics?” Since I’ve been asked this exact question multiple times in the previous days, I’ve decided to put down my thoughts and share them in this article.

Read the whole thing. My thoughts, which are generally similar to Nikola’s:

  • There are no plans (at this time) to remove Synapse, and even if there were, prior history—like with Azure SQL DW—says that the deprecation timeframe is something we can measure in years rather than months
  • Fabric is intended to replace Synapse one of these days, and new customers should start with Fabric
  • Current Synapse customers should stay on Synapse for now, especially given that there is currently no easy migration plan. Give partners and Microsoft some time to sort that out, though, and I expect you’ll see tools and products for this by the time Fabric goes GA
  • PaaS and SaaS are quite different and that can be an influential factor. My personal preference is for SaaS, especially knowing how difficult it can be to secure Synapse while still enabling developer functionality
  • We’re on day 4 of Fabric being a thing (at least in public), and it’ll probably be in a public preview for a while, so there’s still plenty of baking left to do
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Microsoft Fabric for the Power BI Practitioner

Kurt Buhler provides a nice graphic:

I’m just writing this quick article to share a visual overview I made of the newly announced Power BI-related features. I hope it helps you get an at-a-glance overview of some of the big changes relevant to Power BI. More importantly, I hope it doesn’t make you feel overwhelmed! For more information, check out the documentation and learning paths.

There is a lot in store for the platform, but you can already see a slew of new changes and opportunities for Power BI developers.

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Building a Lakehouse in Microsoft Fabric

Reza Rad builds a warehouse down by the river:

The term Lakehouse is derived from two other words; Data Lake and Data Warehouse. A Lakehouse is a place to store structured data (such as Data Warehouse) and unstructured data (such as a Data lake) in a single location. Lakehouse is capable of scaling up to handle large amounts of data. Other tools and services can be used to interact with the lakehouse, for example, to load or read data into it.

Click through for instructions on how to build one and how to access it from SSMS and Power BI.

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Microsoft Fabric Compendium

This one’s going to be a little different from your average Curated SQL post, because there are a whole bunch of Microsoft Fabric-related blog posts. Consider this more a round-up than highlighting any single post.

Overviews

Trying It Out

Size and Scope

Direct Lake

The Name

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