A Fine Slice Of SQL Server
Reza Rad has a video and an article:
Microsoft Fabric is a complete set of technologies that provide Analytics as a service. Fabric uses a logical storage layer named OneLake. In this article, we will explore what OneLake is, what it is important, its essential features, and how it works with the rest of Fabric objects.
Check out the video or read through the article.
Comments closedJose Mendes performs a data migration:
It’s fair to say there have been some considerable changes in the Azure landscape over recent years.
This blog will show you how to configure Synapse Link to export D365 data in the Delta Lake format – an open-source data and transaction storage file format used in Lakehouse implementations.
Before you start considering using this approach, you will need to ensure you meet the following prerequisites (Microsoft documentation).
Read on for those prerequisites as well as a step-by-step guide on how to do it.
Comments closedDennes Torres asks and answers a question:
I explained in a previous article how the Tables in a lakehouse are V-Order optimized. We noticed this configuration depends on our settings, which can be enabled or not.
One question remains: How could we check if the tables are V-Order optimized or not?
Read on for the answer, as well as a link containing more information on V-Order optimization.
Comments closedGilbert Quevauvilliers needs a place to store this data:
Following on in my series, in this blog post I am going to use the dataflow Gen2 in Microsoft Fabric to load the data into a lake house table.
By doing this, it will allow me to store the data in a delta lake table.
In this series I am going to show you all the steps I did to have the successful outcome I had with my client.
Click through for links to the first two parts of the series, as well as a step-by-step guide for part 3.
Comments closedAdam Saxton explains the power of shortcuts:
We feel shortcuts are one of the most power capabilities within OneLake in Microsoft Fabric! Adam walks through what these are and how you can use them.
Click through for a video and a couple of Microsoft Learn links on the topic of shortcuts.
Comments closedFabric content is all over the place by now. In Fabric, as a SaaS platform, most (if not all) services have interconnectivity. In a few clicks you connect your web-developed Power BI dataset to a lakehouse, or warehouse to fetch data from OneLake. But what about Power BI Desktop? You might have uploaded some files to OneLake which you cannot access from Power BI Desktop.
In this blog I’ll explain on how you can connect to OneLake data using Power BI Desktop!
This turns out to be a bit trickier than I would have expected. Hopefully the experience gets better over time.
Comments closedJames Serra has layers, like an onion:
Data lakes typically have three layers: raw, cleaned, and presentation (also called bronze, silver, and gold if using the medallion architecture popularized by Databricks). I talk about this is my prior blog post on Data lake architecture. Many times, companies will create a fourth layer outside of the data lake that I call the relational serving layer. I’ve been having conversations recently with companies about the need for another type of fourth layer, which I will call the physical serving layer. In this blog post I’ll discuss the relational serving layer and the physical serving layer.
Read on to learn more about these.
Comments closedNikola Ilic needs more than a lakehouse:
In the previous parts of the Data Modeling for mere mortals series, we examined traditional approaches to data modeling, with focus on dimensional modeling and Star schema importance for business intelligence scenarios. Now, it’s time to introduce the concept of the modern data platform.
As usual, let’s take a more tool-agnostic approach and learn about some of the key characteristics of the modern data estate. Please, don’t mind if I use some of the latest buzzwords related to this topic, but I promise to reduce their usage as much as possible.
Lakehouses are getting closer to being good enough, but the performance needs to be there, especially if you eventually have virtual data warehouses sitting on top of lakehouse data to deal with the need for structured fact-dimensional data for reporting tools.
Comments closedThe Microsoft’s Lakehouse definition is less ambitious and exclusive. “Microsoft Fabric Lakehouse is a data architecture platform for storing, managing, and analyzing structured and unstructured data in a single location. It is a flexible and scalable solution that allows organizations to handle large volumes of data using a variety of tools and frameworks to process and analyze that data. It integrates with other data management and analytics tools to provide a comprehensive solution for data engineering and analytics”. In other words, a lakehouse is whatever you want it to be if you want something better than a data lake.
Read on for Teo’s classic The Good, The Bad, and The Ugly format.
Comments closedReza 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.
Comments closed