Tag: Power BI

The Principle of Least Privilege Edition

January 2nd, 2024

By Audrey Gerred

Happy New Year! In the spirit of resolutions, new beginnings, etc., lets dive into this concept that’s all about keeping our Power BI world safe and sound—the Principle of Least Privilege. Now, before your eyes glaze over thinking it’s some tech jargon or naptime, trust me, this principle is like the guardian angel of our data, ensuring things stay secure and snug. Sit back, relax, and find out about why it matters and how it plays out in the world of Power BI access!

The Lowdown on the Principle of Least Privilege

So, what’s this “least privilege” fuss all about? Well, imagine you’re putting together an elaborate event/concert… everyone’s invited, but not everyone gets a backstage pass. Least privilege follows that rule—giving users only the access they need, nothing more, nothing less. It’s like handing out keys: no master key for everyone; just the right key for the right door.

Why Does it Matter?

Now, you might ask, “Why bother with this whole access control thing?” Ah, here’s the kicker: it’s all about security and control. By limiting access to only what’s necessary, we minimize the risks of data breaches, unauthorized changes, and mischief-makers wreaking havoc. Plus, it ensures data integrity (think, semantic modeling and a single source of truth) and compliance, keeping the ‘Big Brother’ regulations happy! We are ensuring users have access to the data they need to create robust and efficient analysis and reporting.

Applying Least Privilege in Power BI: Let’s Break it Down

Workspaces: Imagine you’re managing a workspace—Least Privilege means giving access only to those who need it for specific projects – the access should be given to collaborators. You wouldn’t hand out backstage passes to everyone if they’re not working on the show, right?

Apps: Picture Apps as the VIP section—Least Privilege here means making sure only the right eyes see the exclusive content. Share it with those who need those dazzling dashboards, not the entire guest list!

RLS (Row-Level Security): Think of RLS as bouncers at the door—Least Privilege sets rules so users only see the rows of data they’re supposed to. It’s like giving access to certain rooms at the party based on VIP status.

OLS (Object-Level Security): Now, OLS is like secret compartments—Least Privilege ensures only specific users can access or alter certain reports or datasets. It’s the key to hidden rooms only for authorized guests.

Wrapping it Up So, there you have it—the Principle of Least Privilege in a nutshell. Remember, in this digital bash, not everyone needs an all-access pass; sometimes, a peek from the balcony is just enough!
The Semantic Model Edition

December 1st, 2023
By Audrey Gerred

Let’s talk about something that often gets overshadowed by the glitz and glamour of flashy dashboards and eye-catching charts—Semantic Modeling in Power BI.

You see, Power BI isn’t just your run-of-the-mill visualization tool; it’s a power-house in the realm of data modeling, and Semantic Modeling is at the heart of its prowess. But what on earth is Semantic Modeling, and why should you care? Let’s dive in and demystify this essential aspect of Power BI!

What is Semantic Modeling?

Alright, let’s keep things simple. Semantic Modeling is like the architectural blueprint of your data in Power BI. It’s the behind-the-scenes wizardry that organizes and structures your data in a way that makes sense to both humans and machines.

In essence, it’s not just about plopping your data onto a canvas and hoping for the best. Semantic Modeling is the art of creating relationships, defining hierarchies, and adding meaningful metadata to your data model. It’s about shaping your raw data into a coherent, understandable structure that fuels your reports and dashboards.

Why Does Semantic Modeling Matter?

Now, you might wonder, “Why bother with all this modeling mumbo-jumbo? Can’t I just throw data onto a chart and call it a day?” Well, hold your horses—here’s why Semantic Modeling is a game-changer:
1. Clarity and Consistency: Semantic Modeling brings order to the chaos. It ensures that everyone in your team speaks the same data language, making collaboration and understanding a breeze.
2. Enhanced Analysis: By establishing relationships between different data elements, Semantic Modeling empowers you to perform deeper, more insightful analysis. It allows for seamless drill-downs and slicing and dicing of data without losing context.
3. Data Reusability: Once you’ve crafted a solid Semantic Model, it becomes a reusable asset. You can use it across multiple reports and dashboards without reinventing the wheel, saving time and effort.
4. Future-Proofing Your Analytics: A well-designed Semantic Model anticipates future needs. It accommodates changes and additions to your data sources, ensuring scalability and adaptability as your data ecosystem evolves.
In short, it’s your single source of truth!

Power BI: More Than Visuals, It’s Semantic Modeling Magic

Here’s the kicker: Power BI isn’t just about creating visually appealing charts and graphs. It’s about laying a robust foundation through Semantic Modeling that drives those stunning visualizations.

Think of it as crafting a story—your data is the plot, but Semantic Modeling shapes the narrative. It’s about creating a compelling storyline that captivates your audience and provides meaningful insights, not just a series of pretty pictures.

So, don’t overlook the power of Semantic Modeling in Power BI. Embrace it, nurture it, and watch as your data transforms from a jumbled mess into a well-structured, insightful masterpiece.

Remember, it’s not just about what you see on the surface; it’s about the solid foundation beneath that makes all the difference. So, roll up your sleeves, dive into Semantic Modeling, and unlock the true potential of your data in Power BI!
The Ode to Import Mode Edition

November 9th, 2023
By Audrey Gerred

Time to grab a seat and settle in… You know how we deep dive into Power BI and all its (many) wonders? Well, today, I want to talk about the champion of data loading modes – “Import Mode.” Yep, in most cases, it should be your mode of choice when you are creating your semantic model, and I can’t wait to tell you why.

Why Import Mode Rocks:
1. Speed, Speed, Speed: Picture this – you’re whipping up a dashboard, and boom, your visuals load at the speed of light. That’s the magic of Import Mode. It brings your data into Power BI, and once it’s there, everything happens in a snap. Your reports are snappy, your visuals are instant, and your users? They’re impressed.
2. Offline Accessibility: What if I told you that you can take your reports on the road without worrying about an internet connection? Import Mode caches your data, making it available offline. It’s like having your data on standby, ready to impress anytime, anywhere.
3. Transformations Galore: Import Mode isn’t just about speed; it’s also a wizard when it comes to data transformations. You can clean, shape, and mold your data to perfection during the import process. No need to wait for the data to hit the screen – it’s ready to roll from the get-go.
4. Aggregations and Calculations: Need to create complex calculations or aggregations? Import Mode is your go-to. It pre-calculates and stores those values, saving precious time when users interact with your reports. Less wait time, more happy users.
When to Use Direct Query (Because There’s Always a Catch):

Now, let’s talk about Direct Query. It has its time and place, like that niche coffee shop you visit for a specific brew.
1. Live, Real-Time Data: If your data is changing every millisecond, and you need your reports to reflect those changes instantly, Direct Query might be your buddy. It connects directly to your data source, ensuring your reports are always up-to-the-minute.
2. Large Datasets: Sometimes, your data is so massive that importing it into Power BI might slow things down. Direct Query lets you keep your data where it is and fetch what you need when you need it – a win for massive datasets. Even if this is the case, I would suggest at least using aggregate tables to help speed some things up.
But Wait, There’s More:

The thing is, Import Mode isn’t just a one-size-fits-all solution. It’s tailored for most scenarios, offering speed, accessibility, and a playground for transformations. Direct Query has its perks, but it’s like that specialty dish you order once in a while.

So, there you have it – the Import Mode love letter. It’s the unsung hero making our Power BI experiences smoother, faster, and downright enjoyable. Next time you’re building that killer report, remember the magic of Import Mode.
The Iterator Edition

November 1st, 2023

By Audrey Gerred

Let’s talk about some DAX functions! More specifically, I want to unravel the difference between iterator DAX functions and their regular counterparts. Admittedly, this is something that I didn’t get the importance of right away. It’s a bit like comparing apples to oranges, but both are delicious in their own way.

First Things First: DAX Functions

DAX (Data Analysis Expressions) is the language used in Power BI (Power Query uses M code, i.e. Mash-Up). It has a wide array of functions to perform calculations, aggregations, and transformations. Some functions are pretty straightforward – you give them a column of data, and they return a single value.

What the Heck are Iterator Functions?

Then, there are these special ones called iterator functions. They’re like wizards in the world of DAX. Instead of dealing with the whole column at once, they work their magic row by row, giving you the power to iterate (i.e. doing it again and again – see, row by row) through your data.

AVERAGEX vs. AVERAGE: The Showdown

Let’s take an example to understand the difference between iterator functions and regular functions, and when to use which.

Scenario: Daily Sales Data

You have a dataset with daily sales data, and you want to calculate the average sales amount. Now, you could use the AVERAGE function, and it would work just fine. It takes the whole column of sales amounts and gives you the average.

Average Sales = AVERAGE(SalesTable[SalesAmount])

The Twist: Monthly Average

But here’s the twist: What if you want to calculate the average monthly sales, and your data is in a detailed daily format? AVERAGE alone won’t cut it because it would give you an average of all the daily values. You need a monthly breakdown.

AVERAGEX to the Rescue!

This is where AVERAGEX comes into play. It’s an iterator function. Instead of taking the entire column in one go, it works through each row and then calculates the average. So, it goes through each row to do the summing, then averaging the sums.

Average Monthly Sales = AVERAGEX(SalesTable, SalesTable[SalesAmount])

Here’s the key: by using AVERAGEX, you’re able to iterate through the daily data and calculate the monthly average. It’s like having a mini AVERAGE function for each month, and then you’re getting the average of those monthly averages. This makes your calculation precise and granular, exactly what you need for monthly insights.

Conclusion: Choose the Right Tool for the Job

The takeaway here is simple: choose the right tool for the job. When you need a quick, straightforward average, use AVERAGE. But when you need to dive into the details, perform calculations row by row, and get more granular results, that’s where AVERAGEX and its iterator friends come in handy.

The next time you’re faced with a data challenge, remember that DAX has a toolkit with functions that cater to your specific needs. Choose wisely, and you’ll be the magician of your data analysis!

Here’s a Microsoft Learning path that can help you dig in further: Use DAX iterator functions in Power BI Desktop models – Training | Microsoft Learn
My ADHD Edition

October 26th, 2023

By Audrey Gerred

I’ve been thinking about writing this blog for quite a while and going back and forth on whether I should take the plunge. Why? Because there’s a very personal aspect to it. I finally decided to just do it, I’m sharing my ‘dirty laundry’.

Today, let’s dive into a conversation that’s close to my heart – learning Power BI when you’re not only passionate about analytics but also navigating the ADHD highway/roller-coaster. If you’re juggling between the thrilling world of data and the constant dance of distraction, I’ve got some tips to make your Power BI learning adventure not only manageable but truly exciting.

1. Ride the Wave of Your Passion

First things first – let your passion be your guiding star. When you’re genuinely excited about something, it becomes easier to stay engaged. Think of Power BI as a gateway to transforming your data-driven dreams into reality.

2. Harness Hyperfocus for Deep Dives

ADHD often comes with the gift of hyperfocus. Use this to your advantage. When you’re in the zone, dive deep into a specific Power BI topic or project. It’s amazing how much you can accomplish when you’re on fire!

3. Interactive Learning is the Key

Sitting through lengthy lectures or reading dense manuals might not be your jam. So, make learning interactive. Power BI is all about interactivity, so get your hands dirty! Build reports, create dashboards, and see your data come to life. There’s nothing like immediate results to keep you hooked.

4. Embrace Creative Visualization

Your analytical mind probably thrives on creativity. Power BI is a playground for creative data visualization. Use it as an opportunity to express yourself. Turn your data into stunning visuals that not only convey insights but also ignite your passion.

5. Chunk Your Learning

ADHD often prefers bite-sized chunks. Break your Power BI learning journey into manageable pieces. Spend a day on data modeling, another on DAX calculations, and so on. It’s like savoring your favorite meal one bite at a time.

6. Practice Mindful Learning

Mindfulness techniques can help. Before you start learning, take a moment to clear your mind, focus on the task at hand, and breathe. This helps reduce distractions and improve concentration.

7. Leverage Online Communities

Power BI has a vibrant community. Join forums, participate in webinars, and explore Power BI-themed subreddits. Connecting with like-minded folks not only fosters your passion but can also be a source of motivation.

8. Set Realistic Goals

Set achievable learning goals. It’s better to master one aspect of Power BI at a time than to overwhelm yourself with too many goals. Celebrate your achievements along the way.

9. Organize Your Learning Environment

An organized learning environment is your ADHD brain’s best friend. Keep a dedicated space for your Power BI adventures and use tools to stay organized. Digital apps can be a lifesaver.

10. Never Underestimate the Power of Patience

Lastly, remember that your learning journey is your own. Don’t rush it. Power BI is an amazing tool (not just a viz tool – it’s so much more than that), and mastering it takes time. Your passion will drive you forward, and the process itself can be incredibly rewarding.

So there you have it, my friends. Learning Power BI with ADHD is not just a challenge; it’s an opportunity to leverage your unique strengths and channel your passion into the world of data. Let’s keep the conversation going, share our experiences, and celebrate our analytical adventures together.

Keep fueling that passion and keep those insights coming!
The Explicit/Implicit Measure Edition

October 25th, 2023
By Audrey Gerred

Have you ever delved into the world of Power BI and found yourself standing at the crossroads of a crucial decision: explicit measures vs. implicit measures? If you’re scratching your head thinking, “What’s the difference, and why does it even matter?” – well, you’re not alone. But today, I’m here to break it down for you.

Explicit Measures: The Clarity Champions

Imagine explicit measures as the clear and detailed storytellers of your data. They’re like friends who explain everything step by step. When you create an explicit measure, you explicitly define the formula it uses. You get to name it, specify how it should behave, format it, add descriptions to make it understandable for everyone, and utilize them in Analyze in Excel and or other viz tools when you connect to a Power BI semantic model with XMLA endpoints.

The beauty of explicit measures is that they offer precision. If you need a calculated field that counts something specific or performs a unique calculation, you can create it as an explicit measure. This helps you ensure consistency and reliability across your reports. It’s like having a well-annotated map for your data journey.

Implicit Measures: The Quick & Dirty Hack

Now, let’s chat about implicit measures. These are more like your speedy friends who can give you a quick answer but might not explain how they got there. In Power BI, implicit measures are created automatically when you drag and drop fields onto visuals. They save you time by providing basic aggregation (like sum or count) without you writing a single line of code.

Implicit measures are fantastic for speedy report creation or quick exploratory data analysis. However, they come with a trade-off – they might not always give you the exact results you desire, especially for complex calculations and you can’t use them in Analyze in Excel or when visualizing in another tool through XMLA endpoints. They can also become confusing when dealing with multiple visuals and changing requirements.

So, What’s the Best Choice?

Here’s the deal: there’s no one-size-fits-all answer. Both explicit and implicit measures have their place in the Power BI toolkit.
- Use explicit measures when:
  - You need precise and custom calculations
  - Clarity and documentation are vital for your reports
  - You want to create KPIs or unique metrics
  - Ease of use for users with Build Access
  - Utilizing in Analyze in Excel and or other viz tools through XMLA endpoints
- Use implicit measures when:
  - You’re in the early stages of report development
  - You need a quick answer for basic analysis
  - You’re exploring data on the fly
In an ideal world, a balanced mix of both can work wonders. You can start with implicit measures for initial data exploration and report sketching. Then, as your report matures and specific requirements arise, shift to explicit measures for those custom calculations and crystal-clear explanations.

Think of it like a conversation – sometimes you need a quick answer, and sometimes you need a detailed discussion. In Power BI, explicit and implicit measures give you the flexibility to do just that. So, go ahead, use both wisely, and tell your data story with confidence!

What’s your take on the explicit vs. implicit measures debate in Power BI? Share your thoughts in the comments below!
The DAX Variables Edition

October 4th, 2023

By Audrey Gerred

Data Analysis Expressions (DAX) is the backbone of Power BI’s analytical capabilities. It empowers us to create complex calculations and metrics, transforming raw data into actionable insights. One of the lesser-known gems within the world of DAX is the use of variables. In this blog post, we’ll explore how harnessing the power of variables can significantly enhance your Power BI modeling and analysis.

What Are DAX Variables?

Before we dive into the benefits, let’s understand what DAX variables are. Basically, variables in DAX allow you to store intermediate results or values within a formula. These stored values can be reused multiple times within the same calculation, making your expressions more efficient, readable, and maintainable.

Improved Readability

One of the immediate advantages of using variables in DAX is improved readability. DAX formulas can become intricate and convoluted, especially when dealing with complex calculations. By using variables, you can break down these complex expressions into smaller, more manageable parts. This makes your formulas more intuitive and easier to understand, not only for you but also for others who may need to work with your Power BI model.

Consider a scenario where you’re calculating a complicated financial metric involving multiple sub-calculations. Without variables, your formula might resemble a labyrinthine equation. With variables, you can assign names to these sub-calculations, making it crystal clear what each part of the formula does.

Enhanced Performance

Efficiency is another compelling reason to embrace DAX variables. When you use variables, DAX evaluates the expression assigned to the variable only once, storing the result for subsequent use. This can lead to significant performance improvements, especially when dealing with large datasets or complex calculations.

Imagine a situation where you’re repeatedly calculating the same subtotals or aggregations in various parts of your report. Without variables, DAX would calculate these values multiple times, potentially slowing down your report. With variables, you compute the value once and reuse it wherever needed, reducing unnecessary recalculations and improving report responsiveness.

Debugging Made Easier

Debugging DAX formulas can be challenging, especially when you have long and intricate expressions. Variables come to the rescue once again. When you encounter an issue in your formula, you can inspect the values of intermediate variables to pinpoint the problem area. This can save you a considerable amount of time in identifying and resolving errors.

By using variables effectively, you create a modular structure within your DAX calculations. This modular approach simplifies the troubleshooting process, as you can isolate and test individual parts of the formula without altering the entire expression.

Enhanced Reusability

DAX variables promote reusability. Once you’ve defined a variable, you can use it across multiple measures or calculations within your Power BI report. This not only saves you time but also ensures consistency in your analysis. If a business requirement changes, you can update the variable in one place, and the change will automatically propagate throughout your report, reducing the risk of errors.

Conclusion

In the world of Power BI and DAX, variables are a powerful tool that can streamline your modeling and analysis processes. They enhance readability, improve performance, simplify debugging, and promote reusability. By incorporating variables into your DAX toolbox, you can take your Power BI reports and dashboards to the next level, providing clearer insights and more efficient data analysis. Don’t underestimate the potential of DAX variables—they might just become your secret weapon for unlocking the true power of Power BI.

Link to Microsoft Documentation: Use variables to improve your DAX formulas – DAX | Microsoft Learn
The Query Folding Edition

September 21st, 2023
By Audrey Gerred

When it comes to data transformation and manipulation in Power BI, the Power Query editor is your go-to tool. It offers a wide range of options and functions to shape your data into a format that’s ready for analysis and visualization. Among the many features that make Power Query a powerful ETL (Extract, Transform, Load) tool, one stands out as a game-changer for performance optimization: Query Folding.

Query Folding is a concept that can significantly enhance the efficiency and speed of your data transformations in Power Query. To truly understand its impact, let’s dive into what Query Folding is and why it’s essential in your Power BI workflow.

Query Folding is the art of delegating data transformation tasks back to the data source. Instead of Power Query laboriously processing every transformation step, it lets the data source do the heavy lifting. This means that your database or data repository carries out the transformations, significantly boosting performance and efficiency.

As an analogy, imagine you’re hosting a dinner party and need a delicious three-course meal. You could either do all the cooking yourself, from shopping for ingredients to chopping, seasoning, and cooking each dish, or you could hire a talented chef.

In the first scenario, you’d spend hours in the kitchen, working tirelessly to prepare the meal. This is similar to how Power Query without Query Folding operates—it loads all your data into its workspace and meticulously processes each transformation step locally, like you cooking each dish from scratch.

On the other hand, hiring a chef represents Query Folding. Instead of doing everything on your own, you delegate the complex cooking tasks to a professional. The chef (or your data source, in the case of Query Folding) knows exactly how to prepare each dish efficiently, with access to the best equipment and expertise. This allows you to focus on other aspects of your dinner party, much like your data source efficiently handling data transformations while you concentrate on data analysis and visualization in Power BI.

In other words, Query Folding is like having a skilled chef take care of the cooking, saving you time and effort, while ensuring a more efficient and optimized outcome. Similarly, it allows your data source to perform data transformations, resulting in faster query execution and smoother data analysis in Power BI.

Why Query Folding Matters
1. Turbocharged Performance: The most evident benefit of Query Folding is its power to supercharge your data transformation process. By tapping into the data source’s processing capabilities, your queries execute faster, making your reports and dashboards more responsive.
2. Minimal Data Transfer: Query Folding minimizes the amount of data transferred between Power Query and the data source. This reduction in data transfer is a lifesaver, especially when dealing with large datasets or working across slower network connections.
3. Optimized Query Plans: Data sources like databases are optimized for data manipulation. Query Folding ensures that these sources generate optimal query execution plans, leading to more efficient and faster transformations.
4. Scalability: As your data grows, Query Folding keeps you ahead of the game. By offloading work to the data source, you can handle larger datasets without straining your resources.
How to Leverage Query Folding

To leverage the power of Query Folding in Power BI Power Query, follow these best practices:
1. Use Native Queries: Whenever possible, opt for native queries when connecting to your data source. This allows Power Query to delegate as much of the work as possible to the source system.
2. Minimize Data Import: Avoid importing unnecessary data into Power Query. Filter and aggregate data at the source level to reduce the volume of data transferred.
3. Understand Query Dependencies: Be aware of the limitations and capabilities of your data source. Not all data sources support Query Folding for every transformation type, so understanding these limitations is crucial.
4. Check Query Execution Plans: Use tools like the Query Diagnostics feature in Power BI to analyze query execution plans. This can help you identify opportunities for Query Folding and performance optimization.
In conclusion, Query Folding is a powerful feature in Power BI Power Query that can significantly enhance your data transformation and query performance. By pushing as much of the data transformation process as possible back to the data source, you can enjoy faster refresh times, reduced data transfer, and improved scalability. To make the most of Query Folding, it’s essential to understand your data source, choose native queries, and regularly analyze query execution plans. With these practices in place, you’ll unlock the true potential of Power Query in Power BI.
The ‘Fabric for Power BI Users’ Edition

September 12th, 2023

By Audrey Gerred

In my previous blog post, I covered how you can get a free trial version of Fabric through a Microsoft Developer Account so that you can start doing some hands-on tutorials. Today, I completed the tutorial, “Fabric for Power BI Users” and, I am hooked! In this blog post, we’ll walk you through the key steps covered in this comprehensive tutorial and highlight how it can empower you to harness the full potential of your data… all in one workspace!

Creating Your Lakehouse Microsoft Fabric Lakehouse is a versatile data architecture platform designed for the centralized storage, management, and analysis of both structured and unstructured data. This scalable solution empowers organizations to efficiently handle extensive data volumes while leveraging a variety of tools and frameworks for data processing and analysis. It seamlessly integrates with other data management and analytics tools, offering a holistic solution for data engineering and analytics needs.

Data Transformation with Dataflows Gen2 Data preparation and transformation are essential for meaningful insights. Dataflows Gen2 is the new generation of dataflows which has new features and an improved experience such as shorter authoring flow, auto-save and background publishing, data destinations, improved monitoring and refresh history, integration with data pipelines, and high-scale compute.

Adding Data Destinations Data must find its home. In this section, you’ll add data destinations (nice new Gen2 feature), ensuring your transformed data has a place to reside. Configuration options are available to suit your specific needs, whether it’s replacing existing data or appending new records.

Orchestrating with Data Pipelines Automation is key! Pipelines are a powerful feature of Azure Data Factory that enable you to create and automate data-driven workflows in the cloud. Pipelines allow you to logically group and manage ETL activities instead of managing each individually.

Semantic Modeling At this point, you’re in the home stretch! Now, let’s make sense of your data. In this section you will utilize the SQL endpoint to create relationships between tables (you know Power BI loves those Star Schema models) and SQL views in the lakehouse. This allows you to easily access the data within the semantic model which is designed to present data for analysis according to the structure of the business.

Crafting DAX Measures Power BI’s DAX language empowers you to calculate meaningful metrics. In this section you’ll create a simple measure, but feel free to practice making more!

Creating SQL Views SQL views are your gateway to structured data access. You’ll craft SQL statements to calculate total sales amounts by month, saving them as views for future reference.

Auto-create a Report Time to visualize your data! Power BI’s auto-create report feature generates a dynamic report based on your data model. You’ll save, edit, and customize this report to meet your reporting needs.

By following these steps outlined in the tutorial, you’ll be one step closer to understanding and leveraging everything that Fabric has to offer. So, don’t hesitate – dive into the tutorial, create your data lakehouse, and let the data-driven journey begin.
Getting Started with Fabric Edition

September 7th, 2023

By Audrey Gerred

Fabric, Fabric, Fabric… unless you are living under a rock, you have undoubtedly heard a ton of news about Microsoft unveiling Fabric in Power BI. Fabric (still in preview) is an end-to-end analytics product that brings together the best of Power BI, Azure Data Factory, and Azure Synapse into a unified lake-centric SaaS platform. Such big changes can be overwhelming, and you may be wondering where to start or if/how you can test it out and get more comfortable with it. Fear not! Today, we’ll just dive on in and get you some access to start your learning journey!

Microsoft has several tutorials that allow you to jump in and start hands-on learning. They have both multi-experience and experience-specific tutorials. The most important piece of the puzzle when starting one of these tutorials is having a tenant with Fabric enabled. You may have access through your organization, but if you don’t, you can sign up for a Microsoft 365 Developer Account. With the Microsoft 365 Developer Program you will get access to a sandbox and a free, renewable 90-day Microsoft 365 E5 developer subscription which is pre-provisioned with M365 apps.

To sign up for an M365 Developer Account, you will need a personal Microsoft Account (i.e. @outlook.com or @hotmail.com) which you can easily set up here, if you don’t already have one.

Once you have your personal Microsoft account set up, you will need to set up your M365 Developer Account, which you can do here (I suggest using an InPrivate window), then clicking the ‘Join Now’ button.

When you see the below screen fill in the Country/Region, Company (enter your own name if you are not using this on behalf of your organization), language preference, accept the terms and conditions, then click Next.

Select Personal projects, then click Next.

Select which areas you are interested in, then click Save.

Select Configurable sandbox, then click Next.

Create a username, domain, and password, then click Continue.

Enter your phone number, then click Send Code.

Once you get the code you can enter it, then click Set up and you will be taken to a screen that shows how many days you have left and other information.

Now, swing on over to Power BI Service and login using the credentials for your Developer Account (in my example it was powerbiwithme@powerbiwithme.onmicrosoft.com). Power BI should appear as it always does… for now.

Click the settings icon in the upper right-hand of your screen.

Select Admin portal, then in Tenant settings, go to Microsoft Fabric (Preview), enable, and apply.

It will take about 15 minutes for the changes to take effect. Once they do, you will notice a Power BI icon in the lower left-hand side of your screen. If you click on it, you will see additional options available to you because of Fabric being enabled in the tenant.

When you need to create a Fabric Capacity Workspace, select Trial under the Advanced options.

Viola! Now, that you have enabled Fabric you can start utilizing the Microsoft tutorials! Happy learning!