Microsoft Fabric for those who know nothing about Fabric

Copper Contributor

Jan 31, 2024

You are indeed curious and up to this challenge. You have done your part to click the link to get to this page, now it's my turn to get you convinced and fall in love with Microsoft Fabric.

At the end of this article,

You will learn how Microsoft Fabric can make you a better data professional and create new growth opportunities for you.
You will write your first SQL in Microsoft Fabric Notebook
You will write your first Python Code in Microsoft Fabric Notebook
You will create your first Power BI Report from the Microsoft Fabric Semantic Model

In summary, you will move from someone with zero knowledge or experience to a confident beginner.

Data is the new oil and we live in a world where only a few companies are drilling - Adapted from Olanrewaju Oyinbooke's 2019 quote. If you take a moment to process this quote, you will wonder why only a few companies are drilling when they all have access to this valuable resource - DATA.

So far, most value from data is at the descriptive level today - the building of dashboards and reports meanwhile, it is a continuum.

Spoiler

Spoiler Alert: My Short Story

2018 was the year when I got fully into Business Intelligence Analysis. After 3 weeks on the job, I was moved to the Analytics Dashboard team. That's pretty exciting however, I have no idea about the tools - Power BI and SQL Programming skills needed to be successful on the project.

This isn't the challenge, the main challenge was that every data professional across the value delivery chain was working in silo. I had no idea what the database/data engineer team was up to, nor knowledge of any data asset that was already available for me to use, nor idea of the challenges I could face doing something the way I had initially thought about it just because someone had gone through that same route in the past.

Information and wealth of experience were in silos and sitting in the head of an individual or at most in teams which are disjointed from others. While I was able to deliver results in that role, I do not wish any upcoming professional to face a similar challenge that I experienced.

In another role as a Data Scientist, after taking months to build a machine learning model, I realized we did not have Cloud Infrastructure to deploy the model. This is a very serious challenge as there is no way to derive value from the model until it is deployed.

In another role, I built a Dashboard on a database of a transactional system because we did not have data infrastructure or architecture for analytics - this is still coming in some organizations today but it's wrong. Your reporting solution should not be resting on your transactional systems.

Let's not talk about duplicate data across different systems.

What do you see? Challenge, Challenge, Challenge
- Data teams work in silos
- Duplicate data across systems
- Lack of data infrastructure to support model deployment
- Lack of data Infrastructure for Data Warehousing
- Lack of data Dictionary
- Lack of data Governance
- and many more

Your argument could be, what if the company embarks on a project to solve this challenge, to solve this challenge, it will cost money, time, and human resources, and can easily be poorly implemented. This is because a lot is required to solve this problem.

Spoiler Alert: My Short Story 2018 was the year when I got fully into Business Intelligence Analysis. After 3 weeks on the job, I was moved to the Analytics Dashboard team. That's pretty exciting however, I have no idea about the tools - Power BI and SQL Programming skills needed to be successful on the project. This isn't the challenge, the main challenge was that every data professional across the value delivery chain was working in silo. I had no idea what the database/data engineer team was up to, nor knowledge of any data asset that was already available for me to use, nor idea of the challenges I could face doing something the way I had initially thought about it just because someone had gone through that same route in the past. Information and wealth of experience were in silos and sitting in the head of an individual or at most in teams which are disjointed from others. While I was able to deliver results in that role, I do not wish any upcoming professional to face a similar challenge that I experienced.In another role as a Data Scientist, after taking months to build a machine learning model, I realized we did not have Cloud Infrastructure to deploy the model. This is a very serious challenge as there is no way to derive value from the model until it is deployed.In another role, I built a Dashboard on a database of a transactional system because we did not have data infrastructure or architecture for analytics - this is still coming in some organizations today but it's wrong. Your reporting solution should not be resting on your transactional systems. Let's not talk about duplicate data across different systems.What do you see? Challenge, Challenge, Challenge- Data teams work in silos- Duplicate data across systems- Lack of data infrastructure to support model deployment- Lack of data Infrastructure for Data Warehousing- Lack of data Dictionary- Lack of data Governance- and many moreYour argument could be, what if the company embarks on a project to solve this challenge, to solve this challenge, it will cost money, time, and human resources, and can easily be poorly implemented. This is because a lot is required to solve this problem.

Back to Microsoft Fabric

Did you check the spoiler alert? I think you should. this will help us establish the challenges organizations are facing today. Not just organizations, Data professionals are also facing this same challenge and it affects the quality of solutions they build or even limits the value they bring to the company.

What Should a Solution Look Like?

I only listed a few challenges that I have faced in my career but there are many more. I believe you can resonate with some of these challenges. If so, let us describe what a manageable solution should look like.

- Teams should be able to work together in a single platform

- Data Integration time should be significantly reduced

- Ability to reduce duplicate data across the organization

- Availability of modern data architecture and infrastructure that supports advanced analytics

- Ability to govern and manage your data asset seamlessly

Since you understand the problem, you can add to this list, right?

What if I tell you that everything we just discussed listed right here and much more are available in Microsoft Fabric? Did you just exclaim? As someone who has experienced these challenges firsthand, I was filled with so much excitement when Microsoft Fabric was announced and it was that very moment I knew it would be a game changer in the industry.

What then is Microsoft Fabric?

Microsoft Fabric is an end-to-end analytics platform that provides a single, integrated environment for data professionals and businesses to collaborate on data projects. Fabric provides a set of integrated services that enable you to ingest, store, process, and analyze data in a single environment.

Workloads on Microsoft Fabric

Microsoft Fabric provides tools for both citizen and professional data practitioners and integrates with the tools the business needs to make decisions. Fabric includes the following services:

Data Engineering
Data integration
Data warehousing
Real-time analytics
Data Science
Business intelligence

Let's Get Practical

I want to keep away from too many stories and texts. I will proceed from here with practical illustrations.

To follow along, you need to have a Microsoft 365 Work account with a Fabric License or Trial enabled.

If you don't have any, we've got you covered, check the guide below to set up yours for FREE and for Learning purposes only

- Setup Microsoft 365 Developer Account: Click Here to Setup

- How to enable Microsoft Fabric Trial: Click Here to Enable Fabric Trial

The Scenario:

Let us start from known to unknown.

The Journey always begins with Data Ingestion. Your data resides somewhere and you need to get it into Microsoft Fabric. There are multiple options to achieve this in Fabric e.g. Using Data Pipeline, Notebook, Shortcut, and Dataflow Gen2. Your project needs will determine which of the tools or methods is most appropriate.

In our case, we will be ingesting data from Kaggle. We will ingest this data via Notebook. We will connect directly using Kaggle API, unzip the data, and load it into Fabric Lakehouse.

I chose this data ingestion option because there are a lot of examples and resources that used Dataflow Gen2 and the Copy Activity of Data Pipeline. So, get ready to learn something new.

Our goal will be to explore the data in the Lakehouse using the Fabric SQL Visual Query and the Data Wrangler in the Fabric Notebook. We will then use Power BI to build a report based on the transformation that we've carried out on the data through the Visual Query activity and the Data wrangling exercise in the Fabric Notebook.

I want you to get every step so, here is a complete video guide for you.

Make sure you share this blog and the video with your network, they will thank you!

Build Your First Fabric Project

Additional Learning Resources

Microsoft Fabric Learn Together: Expert-led live walk-throughs covering all the Learn modules to prepare you for the the upcoming DP-600 exam leading to the Fabric Analytics Engineer Associate certification. 9 episodes delivered in both India and Americas timezones. Register now for this exclusive live learning experience.
Also, sign up for the Fabric Cloud Skills Challenge at and complete all the modules to become eligible for a 50% discount on the DP-600 exam.
Learn how to use copilot in Microsoft Fabric, your data insights AI assistant
Join the Fabric Community to stay updated on the latest About Microsoft Fabric
Consider joining the Fabric Career Hub so you won’t miss out on any Careers in Microsoft Fabric