A Fine Slice Of SQL Server
Tomaz Kastrun starts a new series:
Microsoft Fabric is a next-gen platform, that brings all-in-one data and analytics solution for end users, small, medium and large enterprises. Services offer the complete data cycle movement (data ingestion, data engineering, data integration, data storing with warehouse using one lake), delivering data insights and building predictive models.
Read on for the overview and stay tuned for plenty more where that came from.
Comments closedGilbert Quevauvilliers rustles up some data:
In this blog post I am going to show you an easy way to clean your data (which is often fixing data issues or mis-spelt data) using the new feature Launch Data Wranger using DataFrames
I had previously blogged about using Pandas data frames but this required extra steps and details, if you are interested in that blog post you can find it here: Did you know that there is an easy way to shape your data in Fabric Notebooks using Data Wrangler?
In this blog post I am going to show you how I cleaned up the data in my location column.
Read on for a demonstration of what you can do.
Comments closedWhy the excitement on my part? Well to take advantage of all the great features in Tabular Editor, you really need to be able to connect and write via XMLA, be that for doing CI/CD pipelines or by making edits directly on the dataset.
What great new features does Tabular Editor unlock that you can’t just do in the online Power BI modelling experience in Fabric… tons!
Read on to see how Tabular Editor plays with Microsoft Fabric.
Comments closedBrian Bønk slips out of the constraints:
When working with data and building data models, I personally seldom use the constraints feature on a database. Call me lazy – but I think constraints are adding unnessesary complexity when building data models for reporting. Especially if you are working with the some of new platforms – like Microsoft Fabric, where you are using staleless compute, aka. data storage is seperated from the compute layer.
I understand the need for contraints on other database systems like OLTP systems.
In reporting models it can be somewhat usefull to have constraints between tables, as they help/force you to some level of governance in your datamodel.
But how can we use this in Microsoft Fabric and are they easy to work with?
Read on for those answers. I will note that I’m a stickler about constraints in transactional systems, though I agree that constraints in warehouses are not critical—assuming, at least, that you’re following the Kimball approach and have one and only one mechanism to write data, and that you have other mechanisms for vetting data quality.
Comments closedPrathy Kamasani wants a report, not a webpage:
Like many I am also playing with Fabric, many of my clients are also excited about Fabric and want to know more about it. Being a solution architect in the consulting world one of the most common questions I get asked is: “When certain features will be available, Where are they in the roadmap?”. That’s what sparked the idea of scraping the Microsoft Fabric Roadmap and creating this Power BI report. It is based on a Direct Lake connection, so it has been a bit temperamental.
So, what did I do it? If you are not interested in the whole story. Here is Python code you can run to get a road map. If you are interested in my process carry on reading
Click through for the process and explanation.
Comments closedSandeep Pawar talks about fallback options:
When you create a Direct Lake semantic model, by default it is in Direct Lake mode, i.e. you will directly query the delta table from the lakehouse/warehouse. This is what we want because the query performance will be very much comparable to the import mode. However, under certain circumstances, the DAX query can fallback to DirectQuery if Direct Lake limitations are hit.
Read on to learn more about circumstances in which this could happen and ways to change the default behavior.
Comments closedBrian Bønk gets in on the Microsoft Fabric fun:
Microsoft has released the final piece of the current puzzle around the OneLake as a one-stop-shopping service for dat in Fabric. Until now we had only access to the KQL data in the KQL database.
With this addition, we can now finally say that OneLake is the one place for your data in Fabric.
Read on to see how you can make data in an existing KQL database usable in OneLake.
Comments closedSoheil Bakhshi doesn’t want to forget to turn off the power at night:
In the previous blog post, I explained Microsoft Fabric capacities, shedding light on diverse capacity options and how they influence data projects. We delved into Capacity Units (CUs), pricing nuances, and practical cost control methods, including manually scaling and pausing Fabric capacity. Now, we’re taking the next step in our Microsoft Fabric journey by exploring the possibility of automating the pause and resume process. In this blog post, we’ll unlock the secrets to seamlessly managing your Fabric Capacity with automation that helps us save time and resources while optimising the usage of data and analytics workloads.
Right off the bat, this is a rather long blog, so I added a bonus section at the end for those who are reading from the beginning to the end. With that, let’s dive in!
To spoil the bonus a little bit, Soheil shows us not only how to turn things on and off on a schedule, but also how to ignore certain days of the week. Read the whole thing to get that.
Comments closedAndy Leonard kicks off a trial:
Are you interested in learning more about Microsoft Fabric?
One way to begin tinkering with the new platform is to start a free trial. At the time of this post, a free trial is available here:
Read on for instructions on how to try Fabric out. Now that Fabric is in GA, you’ll have to pay once the trial is over, but this does at least give you some time to check it out before then.
Comments closedSandeep Pawar wants readability:
JSON is ubiquitous, particularly when working with APIs and logs. Its unstructured nature makes it highly flexible for handling anything from a simple array to a complex nested structure. However, this can also make it challenging for data analysis. When parsing JSON, it’s crucial to understand its structure so you can flatten it and convert it into a tabular format for analysis. Once the structure is identified, you can use pandas or PySpark to explode or normalize it into the desired shape. In this article, I will explain the method I use. While this approach is applicable to any notebook, there is a specific trick to make it work in a Fabric notebook.
Read on for that trick.
Comments closed