A Fine Slice Of SQL Server
Manish Mishra shows off some of the query capabilities with delta tables:
Delta Time Travel is a feature that is provided by Delta Lake. Delta time travel allows the user to switch to the previous version of the delta table.
Some of the benefits of Delta Time Travel are:
- Historical Data Analysis
- Rollback to the previous version in case of new data quality is not valid
- Supports Schema Evolution
Click through for examples of each of these.
Comments closedI have published videos and articles before about Lakehouse maintenance. In this article I want to address a missing point for a lot of Fabric administrators: How to do maintenance on multiple lakehouses that are located in different workspaces.
One of the videos I have published explains the maintenance of multiple lakehouses, but only addresses maintenance in a single workspace. Is it a good idea to keep multiple lakehouses in the same workspace? Probably not.
Click through for the process.
Comments closedA few days ago, while preparing materials for the customer training on Microsoft Fabric, I stumbled upon a very interesting article at Microsoft Learn. The article describes how to integrate Power BI semantic models (aka datasets) into OneLake.
At first glance, this doesn’t sound like something epic, but when I started thinking more and more about it, I realized that this really might be a huge thing in many different scenarios. First of all, at the moment of writing, this feature is still in preview – this means, it can change to some extent in the coming months, before eventually becoming GA. Nevertheless, I decided to take a shot and explore what can be done with OneLake integration for semantic models.
Read on to learn more about what this is doing and what you can do with it.
Comments closedChris Webb shows off a bit of functionality:
One of the coolest features in Fabric is Direct Lake mode, which allows you to build Power BI reports directly on top of Delta tables in your data lake without having to wait for a semantic model to refresh. However not everyone is ready for Fabric yet so there’s also a lot of interest in the new DeltaLake.Table M function which allows Power Query (in semantic models or dataflows) to read data from Delta tables. If you currently have a serving layer – for example Synapse Serverless or Databricks SQL Warehouse – in between your existing lake house and your import mode Power BI semantic models then this new function could allow you to remove it, to reduce complexity and cut costs. This will only be a good idea, though, if refresh performance isn’t impacted and incremental refresh can be made to work well.
Click through to learn more about the performance of this operation and how it all works.
Comments closedEugene Meidinger continues learning about Azure Fabric:
I want to preface that a lot of the issues I run into below are because of my own ignorance around the tooling, and a lot of the detail I include is to show what that ignorance looks like, since many people reading this might be used to Fabric or at least data engineering.
Read on to learn about Eugene’s mistakes so you don’t make them yourself.
Comments closedKevin Chant invites you to a swank lakhouse:
By the end of this post, you will have a good overview of Microsoft Fabric Data Lakehouses, including CI/CD options. In addition, where your SQL Server background can prove to be useful and where some Power BI knowledge can come in handy.
Plus, I share plenty of links in this post. For instance, there are a couple of useful resources to help you get started towards the bottom of this post.
Click through for the article.
Comments closedTomaz Kastrun gives us the skinny with multiple posts in his Advent of Microsoft Fabric. Day 3 introduces the lakehouse:
Lakehouse is cost-effective and optimised storage, supporting all types of data and file formats, structured and unstructured data, and helps you govern the data, giving you better data governance. With optimised and concurrent reads and writes, it gives outstanding performance by also reducing data movement and minimising redundant copy operations. Furthermore, it gives you a user-friendly multitasking experience in UI with retaining your context, not losing your running operations and working on multiple things, without accidentally stopping others.
Yesterday we looked into lakehouse and learned that Delta tables are the storing format. So, let’s explore what and how we can go around understanding and working with delta tables. But first we must understand delta lake.
We have learned about delta lake and delta tables. But since we have uploaded the file directly, let’s explore, how we can also get the data into lakehouse.
Click through for all three posts.
Comments closedSandeep Pawar scripts out some lakehouse work:
At MS Ignite, Microsoft unveiled a variety of new APIs designed for working with Fabric items, such as workspaces, Spark jobs, lakehouses, warehouses, ML items, and more. You can find detailed information about these APIs here. These APIs will be critical in the automation and CI/CD of Fabric workloads.
With the release of these APIs, a new method has been added to the
mssparkutilslibrary to simplify working with lakehouses. In this blog, I will explore the available options and provide examples. Please note that at the time of writing this blog, the information has not been published on the official documentation page, so keep an eye on the documentation for changes.
This looks to be quite useful for CI/CD work.
Comments closedNikola Ilic dives into tables:
Probably the biggest confusion is: should I use a lakehouse or warehouse in Fabric? Or, what is the difference between Direct Lake and DirectQuery mode for Power BI reports?
And, while these two points mentioned above are of paramount importance to clarify, in this article I’ll focus on explaining another potential caveat, which is relevant when working with the lakehouse in Microsoft Fabric.
If only Nikola dove onto tables, I could make him an honorary Buffalo Bills fan.
Comments closedReza 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 closed