[Live] - FabricCast #1 - Dirceu Resende: Career, Power BI Embedded, Market and Microsoft Fabric

Hey guys!

Recently, I had the pleasure of participating in a sensational chat on Fabric Cast, my great friend's podcast Sidney Cirqueira. We talked for more than two hours about careers, the challenges of working abroad, behind the scenes of the Microsoft product team and, of course, what everyone wants to know: Microsoft Fabric vs. Databricks.

As the content was extremely rich, I decided to summarize this conversation for anyone who wants to understand the current scenario in the data area, avoid career pitfalls and master cutting-edge technologies.

It was a really cool, open and very honest chat about careers, technology, data and the market. The idea was never to make a formal presentation or sell any specific technology, but rather to talk about the trajectory, decisions and real learnings over the years, including mistakes made.

Those who couldn't watch it live can watch our chat, which was recorded. This podcast was very TOP!! I'm sure you'll like it!

Stream link:

Beginnings in technology and the first paths

I started programming very early, when I was still a teenager, at 12 years old. My first professional contact with technology was far from databases or BI.

I worked with support, maintenance of computers, printers, cabling, then desktop development and web development. I went through PHP, ASP, WebForms and other scenarios that, at the time, made perfect sense.

I even developed code generators that reduced huge projects to a few days of work.

Technically it was interesting, but over time I started to notice something that bothered me: the pace of change in web development.

Frameworks emerged and died quickly, standards changed constantly and accumulated knowledge seemed to lose value very quickly.

This perception was not a judgment on the area, but a personal understanding that I wanted something more stable, where it would be possible to delve deeper and reuse knowledge for many years.

The migration to data and the search for technical stability

It was in this context that I migrated to the data area. At the time, BI was still undervalued, Power BI did not exist and many companies focused everything around the database.

I worked with SQL Server, Oracle, procedures, functions, modeling and administration.

What caught my attention was precisely the longevity of the knowledge. SQL was still SQL. Modeling continued to be essential.

Understanding data, business and performance made a difference regardless of the trendy tool.

That decision ended up shaping my entire career after that.

The current scenario for those just starting out

During the live, we talked a lot about how the scenario has changed for those starting today.

If before the challenge was access to information, today the problem is the opposite: there is too much information.

I see a lot of people lost, trying to study a little bit of everything at the same time: cloud, BI, data engineering, data science, Python, Spark, new tools every week, and without being able to delve into anything.

I reinforced a lot that it is essential to choose a macro area first and, from there, narrow it down.

Nobody builds real depth in six months. A generalist is not someone who superficially knows several things, but someone who has built a solid foundation over time.

Community as a career turning point

I joined the community more actively relatively late, around 2016 or 2017, and I can confidently say that this completely changed my professional trajectory.

Before the community, my growth was much slower and more isolated. Afterwards, I started to exchange experiences with people who were experiencing similar problems, I learned much faster and had access to opportunities that would never have arisen individually.

Community is not just an event or lecture. It's a technical group, daily exchange, mutual help and often solving critical problems together.

I even made it clear that, in selection processes, active participation in the community always weighs positively for me.

The emergence of the technical blog

The blog came about very simply. I saved my scripts and notes in Evernote and decided to centralize them in one place. In the beginning, it was something completely personal.

Over time, I realized that writing helped me learn better and organize my thinking.

The texts became larger, more detailed and deeper. I also started treating the blog as a technical portfolio, where I not only show the “how”, but the “why” of decisions.

I've always believed that a well-written article shows much more about a professional than just a repository of loose code.

Stronger entry into BI and new challenges

When I started working more directly with BI, I had no formal experience in the area.

I was very transparent about this and made it clear that I would study intensely. I delved into dimensional modeling, Kimball, SSIS, SSAS and heavy SQL.

This pattern was repeated throughout my career: accepting challenges greater than my current experience, but always with a real commitment to study and deliver.

I never believed in “fixing it later”.

Power Tuning, Power Embedded and Fabric

I joined Power Tuning when the company was still small and helped structure the BI team.

In 2023, Power Embedded appeared, practically along with the launch of Microsoft Fabric.

The product was created to solve a real market problem: Power BI cost and licensing.

Today it is used by hundreds of companies, many with Fabric in production, large environments, dozens or hundreds of reports, real concern with governance, capacity and performance.

None of this was born out of theory or hype. It was real customer demand.

Experience working outside Brazil

I also shared my experience working in Barbados. I made a point of demystifying the idea that working abroad is just glamour.

There is a lot of learning, especially in English, DevOps, CI/CD and cloud, but there are also big challenges: Isolation, limited infrastructure, pandemic, severe lockdown, visa dependency and emotional impact are real factors that need to be considered.

Working abroad is a decision that goes far beyond salary.

Passage through Microsoft and change of route

Working on the Power BI product team was an important experience, but it also made it clear that not every environment suits every profile.

The corporate pace, bureaucracy and long delivery cycles didn't align with what I like to do.

Recognizing this and changing course was a conscious decision. I don't see it as a mistake, but as learning about myself.

Fabric, Databricks and technical choices

We talk a lot about Fabric and Databricks. My view is simple: there is no absolute best tool. There is a tool more suited to the context.

Databricks is more mature and offers more control. Fabric is newer, but it evolves quickly and integrates very well with the Microsoft ecosystem, especially with Direct Lake.

Shallow comparisons of cost or performance don't help anyone.

The role of a more experienced professional is to understand the pros, cons and impact on the business before deciding.

Technical basis and business vision

When talking about data engineering, I made clear what I consider essential: SQL, data modeling and Spark. Python comes later, as do CI/CD and DevOps.

Ignoring modeling creates serious problems: slow reporting, excessive capacity consumption and fragile solutions. Code is not a solution by itself. Solution is impact on the business.

Technology is a means, not an end. Those who grow are those who understand what generates money, what generates losses and what is critical for the company.

Microsoft Fabric vs. Databricks: Clash of the Titans

That's the million dollar question. Are they competitors? Yes.

Although both run on Spark and use open formats such as Delta Lake, the philosophies are different:

1. Databricks (The Mature Expert)

Databricks is an extremely mature PaaS (Platform as a Service) platform. It gives you “full control of the engine”. You choose the type of machine, tune the cluster, change the Photon and adjusts every detail of the computation.

• Pros: Granular control, maturity and excellent for heavy engineering teams (Python/Scala).

• Cons: Complex setup and cost management that requires a specialist to avoid “going over” the budget.

2. Microsoft Fabric (The Integrated Ecosystem)

Fabric is a SaaS (Software as a Service) solution. The idea here is simplicity and integration. With the OneLake, you have a single repository for the entire company.

• Pros: Direct Lake (access to data without needing Import or Direct Query), friendly interface for those coming from Power BI and native integration with the Microsoft ecosystem.

• Cons: Because it is SaaS, you have fewer “screws” to tighten in the infrastructure compared to Databricks.

Technical Comparison Table

The Modern Data Engineer's Survival Guide

If you want to stand out in 2026, don't just focus on the trendy tool. Focus on the fundamentals.

Below, I list the order of priority that I recommend to anyone who asks me:

1. SQL Server (T-SQL): It's the basis of everything. If you don't know how to perform a JOIN or understand an execution plan, the tool won't save you.

2. Data Modeling: The book of Kimball It's still the law. If your modeling is poor, your reporting in Power BI will be slow, no matter whether you use Fabric or Databricks.

3. Python/PySpark: Essential for complex transformations where SQL doesn't shine as much.

4. DevOps and CI/CD: Learn how to version your code (Git) and automate deployment.

5. Soft Skills (Main): Nobody wants to work with the “genius in the cave”. You need to know how to explain the value of your project to the director, talk about cost reduction and impact on the business.

Soft skills and final advice

We also talk about soft skills. English is not a difference, it is a requirement.

Communication, relationships and the desire to learn make a lot of difference.

Profiles that are arrogant, difficult to deal with or that only point out problems without offering solutions are not sustainable.

I also left some direct advice: never resign without having another job, always do your homework before proposing changes and always think about the impact of decisions.

In the end, the message was simple: technology is an excellent area, but it requires continuous study, focus and maturity.

Those who understand this and build their career with consistency always find space.

Conclusion and Future Vision

The data market has never been so hot, but the demand for complete professionals (technical + business) has increased drastically.

Whether you are a Databricks advocate or a Fabric enthusiast, the important thing is to deliver value and solve your company's bottlenecks.

Study English, participate in the community and never stop learning. Technology is just the means; your knowledge is the real asset.