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Category: Data Lake

Generating Fabric Delta Tables from Power BI Semantic Models

Nikola Ilic is excited:

A 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.

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Delta Table Incremental Refresh in Power BI

Chris 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.

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An Overview of Lakehouses in Microsoft Fabric

Kevin 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.

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All about Lakehouses in Microsoft Fabric

Tomaz 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.

Day 4 covers Delta format:

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.

Day 5 covers data ingest:

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.

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Lakehouse Management in Fabric via mssparkutils

Sandeep 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 mssparkutils library 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.

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Fun with Tables in the Microsoft Fabric Lakehouse

Nikola 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.

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Exporting Dynamics 365 Data into Delta Lake via Synapse Link

Jose 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.

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