Microsoft Fabric

How To Prepare And Load Data Into Fabric Lakehouse?

Step-by-step guide to Prepare and load data into your lakehouse

In the dynamic field of data management, the process of getting your data ready  is a critical step on your journey to data analysis. By using Microsoft Fabric analytics and Power BI data visualization, we make sure your data becomes a valuable asset. 

This guide provides a step-by-step approach to prepare and load data into Fabric Lakehouse. 

If you are not sure how to create a Lakehouse in Microsft Fabric, please refer to this guide here. 

Just like an adventure through a rich landscape, each step in this guide brings you closer to data excellence. Here, we are not only discussing how to prepare and load data in Lakehouse, but we will be testifying that by combining Microsoft Fabric analytics and Power BI data visualization, you’re not just preparing and loading data but are building a strong foundation for your organization’s data-driven success.

This synergy between Microsoft Power BI and Microsoft Fabric ensures your Lakehouse becomes a hub of analytical brilliance, where data transforms into actionable insights, driving strategic decisions and propelling your organization to new heights.

Now, without further ado, let’s move on to the steps needed to load data in the Lakehouse. p.s. if you are wondering what Lakehouse is and why it is important for Fabric, please scroll down to our FAQ section below.

Prepare and load data into Fabric Lakehouse: Step by Step​

Step 1: Open Your Lakehouse

Initiate the process by accessing your Lakehouse environment. If you’re new to this concept, refer to our previous blog post for a detailed walkthrough on getting started.

Step 2: Create a New Dataflow Gen2

Within your Lakehouse interface, locate and select the “New Dataflow Gen2” option. This step marks the inception of your data preparation and movement journey.

Creating a New Dataflow Gen2 in Microsoft Fabric

Step 3: Import from Power BI Query Template

Opt for “Import from Power BI Query Template” to streamline the data import process. This strategic approach simplifies data integration and paves the way for effective data management.

Importing from Power BI Query Template in Microsoft Fabric

Step 4: Choose the Query Template

A selection window will appear. Navigate to and choose the specific query template you intend to work with – for instance, “ConstosoSales.pqt.” Confirm your selection by clicking the “Open” button.

Choosing Query Template in Microsoft Fabric



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Step 5: Configure Authentication

In the subsequent interface, select “Anonymous” as the chosen “Authentication Kind.” This step ensures secure and controlled data access, a fundamental aspect of effective data handling.

Configuring Authentication for Lakehouse in Microsoft Fabric

Step 6: Establish Connection

Initiate the connection process by clicking the “Connect” button. This action establishes a secure and reliable link between your Lakehouse and the chosen query template.

Establishing a connection for Lakehouse in Microsoft Fabric

Step 7: Familiarize with the Interface

Once the connection is successfully established, the interface will present you with a mirror image of your data’s structure. This serves as the canvas upon which your data transformation will unfold.

Familiarizing with Lakehouse in Microsoft Fabric

Step 8: Preview and Select Data

Navigate to the “Data Preview Section” and identify the “DimDate Query.” Select this foundational dataset, which will undergo subsequent transformation processes.

Selecting Data in Lakehouse in Microsoft Fabric

Step 9: Customize DateKey Column

To initiate customization, click on the header of the “DateKey” column, opening the gateway to fine-tune this vital aspect of your data.

Step 10: Configure Date/Time

Within the customization options, proceed by clicking “Date/Time.” This action triggers a pop-up box, enabling you to further refine your DateKey column.

Configuring DateTime for a column in lakehouse of Microsoft Fabric

Step 11: Implement Column Replacement

Within the pop-up box, opt for the “Replace current” option. This decision ensures seamless integration of the modifications made to the DateKey column.

Implementing Column Replacement in Lakehouse in Microsoft Fabric

When you carefully follow each of these easy steps, you’re starting a special adventure – one that changes and improves your data. Think of each step like a puzzle piece that fits perfectly, creating a strong and neat Lakehouse space. This mix of getting things ready and loading them in is like a magical recipe. It’s the first step to looking at your data in smart ways, helping your organization make smarter choices using information.

As you walk through these steps, you’re not just doing tasks – you’re creating something amazing. Picture it like building a strong foundation for a house. This strong base, made by preparing and loading data, helps your organization understand things better. It’s like a superpower that helps your team see important things in the data and make wise decisions for the future. With every step, you’re building a path to a world where your decisions are driven by data, making your organization even better.

The perfect data duo: Microsoft Power BI & Fabric

Understanding Power BI is the key to unlocking the full potential of Fabric – together, they’re the perfect data duo.

If you’re not familiar with Power BI, don’t worry – we’ve got you covered. Our Exper Power BI Trainers have created this crash course that we reserve for those who truly appreciate its potential. It’s available to you for a free download below.

FAQ: Loading Data in Lakehouse | Microsoft Fabric

A data lakehouse is a comprehensive data platform that combines the strengths of data warehouses and data lakes into a single, versatile data management solution.

The Lakehouse within Microsoft Fabric is vital because it’s a data architecture platform that consolidates data, making it easily manageable and analyzable in one location. It simplifies data infrastructure, enhancing efficiency for analysts and scientists.

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