Think AI Podcast - The Long Arc of Data and AI | Ep. 7 with Reza Rad (RADACAD)

Episode Date: May 5, 2026

The Long Arc of Data and AI with Reza RadReza Rad has watched every Microsoft data platform turn since SQL Server. 15-time MVP. Founder of RADACAD. Author of seven books. 10 million YouTube views. In this episode he tells Dave Goyal exactly which workforce AI is replacing, why your Copilot output is only as good as your star schema, and what a 22-year-old should spend the next two years learning if they want to survive the next decade.In this episode:00:00 Cold open: Who AI is actually replacing00:34 Welcome and intro01:48 What has actually changed and what has not in the AI era07:52 Why Copilot in Power BI fails on a bad data model10:39 The CFO who got the right Q3 number from stale data14:59 Why AI is replacing juniors, not seniors16:02 Real Fabric and AI customer outcomes20:39 Governance, F-SKUs, and the iceberg under every AI investment24:49 Mindset, energy, and surviving 20 years of platform turns29:36 What Reza would tell a 22-year-old today34:18 Power BI and Fabric Summit and why virtual beats in-person37:55 Music, pattern, and improvising inside structure42:13 What kept the content engine running for a decade44:01 One thing AI-curious, AI-enthusiast, and AI-skeptic should each learn this quarter47:02 New Zealand vs US adoption curves49:22 Closing wordIf this conversation helped you frame your own Power BI, Fabric, or AI strategy, share it with one teammate wrestling with the same questions. Subscribe for weekly conversations on building real AI in real companies.---🔗 Links and ResourcesRADACAD Academy: https://radacad.comReza on LinkedIn: https://www.linkedin.com/in/rezarad/Reza's books on Amazon: https://amzn.to/3QOdC4IPower BI and Fabric Summit: https://globalpowerbisummit.com/Think AI Podcast: https://bit.ly/ThinkAIPodcast#PowerBI #MicrosoftFabric #DataAndAI #Copilot #RADACAD

AI agents, LLM, CorePilot, ChatGPT, Cloud, all of these things are kind of replacing workforce. And that is a big concern for many people. Well, who does it replace? It actually replacing Junior workforce. So I would advise people to go on that route instead of saying, well, I don't need to learn this language. So I would reinvent junior, so at some point chat GPT would replace me. You know, that is not the route I would suggest people to go. Welcome to the Think AI podcast.

Each week, we talk about the most exciting AI research, tools, case studies and more. I'm your host, Dave Goir, and I've been working behind the scene in data and AI for over 30 years. Whether you are an AI expert, skeptic, or something in between, this podcast is for you. Today's guest is someone I've followed for years and personally learned fabric from when he came through L.A. Two years ago, I think. Resa Reda Redekhead, Microsoft Regional Director, a 15-time MVP for Data Platform and AI, and the author of seven books on PowerBi and SQL Server. He's based in Auckland, New Zealand, and his training reaches practitioners around the world.

What I wanted to talk to you today is the long arc. Racha has taught through SQL server, through PowerBI, and now through Fabric and AI. So we are going to ask what actually different this time, what has not changed, and what should a leader with the Power BI estate already running to do on Monday morning? Reza, welcome to the show. Thank you. Thanks, Dave. Thanks for the opportunity.

Hello, everyone. Very glad to be with you here. So my name is Rosarad. They've already introduced. So back to your questions that with the new era of AI and things like that, what has actually not changed. I think the processes that we use, the people, the culture, those are not changed. Technology changes.

Technology evolves as we go. We used to do things in SQL server using the stock procedures. then we did things in SSIS for transforming data, then data flow came in. There are all different technologies, but still there's a need for transforming the data. There's a need for integrating the data. There's a need for us to connect to a data source, get the data, prepare it in the shape that we want to analyze. And that is still part of the plan. Even with the AI, AI might speed up that process, like we tell AI that this is what we want,

so that it speeds off the process, but still that part of the work needed to be done. So I think once we look at it from that point of view of the processes, you still have the same processes as in the old ways, although we can do that more efficiently these days. Yeah, very well said. And we also saw the same thing in manufacturing and finance industry. Things stay stable. At the same time, there are a lot of innovations.

through, but the fundamentals are same, isn't it? Like SSIS may have transferred into ADF to fabric pipeline. The concept is still the same. You're still doing ETL. You're still applying the transformation logic, just doing it differently. One thing I also wanted to see, you know, with Microsoft, there is a love and hate relationship. I'll be honest. People love Microsoft, but at the same time, they complain about certain things. With this FAPCon this year, I saw something amazing that Microsoft has done, like a complete platform,

you know, as we used to see in MicroStrategy or Kognos, you know, back in our days, 30 years ago. Now things have been pretty stable with Microsoft. What do you see, customers or even people complain about Microsoft technology

and how Microsoft or us should be marketing it or explaining it in a correct way because there are a lot of complaints which may not be the complaint if that makes sense yeah that is right so one of the things about uh microsoft roles in the market is that microsoft is a market player like i've been in the data and analytics um let's say market for like over 20 years i've worked with like even technologies like ibn cognose back in the days with orichael not that many people are using it these days

but still we used that those times, SQL server, MySQL, all these different technologies that different companies provided, different vendors provided. And alongside with this, you also have big players. You have Microsoft, you have like other players in the market. These days we have a Google BigQuery. We have a snowflake. We have data breaks. The thing is that when you are a big player in the market, you try to expand your risk.

and that is what Microsoft is doing. So Microsoft started the new analytics aura with Power BI. Before that Microsoft had a lot of technologies for analyzing the data like SQL on-prem, analysis services, reporting services. But the one turning point, I would say, was Power BI. Power BI is a technology that many people are using it these days, even though they don't know how to go and build a database. It's a data analysis tool.

It's a self-service data analysis tool. then from Palauvia Microsoft expanded because Microsoft had these other tools and services such as

Azure Synaps such as your SQL database, Azure Data Factory so try to expand it to a bigger a bigger umbrella

clothed fabric which is a natural progress from Paral BIA when you look at it now when you look at it from someone's point of view

outside of this environment they say well if I I just compare, for example, SNAPS versus databases, I might get a better performance in databases. If I just compare, for example, the

visualization of Parabia, this particular visualization in the tablo, I might get a better visual experience in that. But then when you look at it from like a high level point of view, when you look at it as an umbrella product, instead of spreading with

a lot of different technology, is dealing with a lot of different vendors, and licenses, you could get one one licensing platform, one capacity structure that would cover everything. Always, there will be always pitfalls and there will be always bugs to fix, but

one thing that I would encourage people to look into is not to look for technology that doesn't have any bugs. You don't have such technology. There is always a bug in the technology, but look into So if the team behind that technology, if the product team behind that technology is taking time to improve that technology, is investing time to hear what their customers are saying, what their users are saying, and improve their products. And what we have seen from Microsoft, this is the case. They have the ideas from, they have the customer works, they have different channels that they get these inputs and that impact the way that they improve the products, which is also showing like the. fabric that we have now is totally different than the fabric we had two years ago. It's much more

allowable and it will go this way in the future. Very well said. And yeah, I think we used to say work with limitations, not work without limitations because that's nothing, such thing out there and available. So you very well explained it. So I want to pull on the PowerBi site again. And one of the thing Microsoft started doing is launching co-pilot. in everything. So PowerBi obviously have copilot. And now it's matured and now it's going towards the agents. Now agents that have arrived in fabric and also in Power BI. Where do you see customer actually seeing it wrong on the ground, not in theory? So they may be thinking, okay, agent can do this, but agent might be for something else. That reality versus the dream, where should they

keep their feet right now? Yeah. So, WAPE, so, I see that a lot of organizations are thinking at the moment or that is their, let's say, idea of co-pilot in Power BI or agents used in Power BI is that we ask the question, this gives us the whole response instantly. The whole thing is that this still relies on the good data model underneath. The good data model is following all the rules that we define what the good data model is, It is a star steam, it has fact table, dimensions table, one to many relationships between these. It has a roller security setup. It has all of those configurations that we want this to become a good data model.

On top of that data model, we could go and write our DAX calculation. If we don't have the right data model, our DAX calculation would be heavy, it would be low performance, even if COPilot writes it, right? But if I have a good data model, either me or COPilot going. and write that calculation, either me or co-pilot, and build the visualization, and that can be a good thing. So the agent used in power,

B.I, as of now, this, of course, would change in two years' time. But as of now, the agent use in power B.I. is good when it comes to letting something on top of a good data model. But you, as a developer, you as a B.I. team, data analytics, team data analyst,

you will still need to spend time to build that data model in a good way, to make sure that your relationships are well defined, you have good synonyms for the agent to understand that. So at the moment, you are building it to serve the agent, and then later on you expect agent to serve you to give you better visualization. In the future, two, three years, who knows,

this might entirely go and build a PowerBIS monthly model, but we are not there yet. No, very well said. And we had a similar story, so we had a CFO, co-pilot, what was the Q3 margin, and it returned the right number, but the stale data set, nobody refreshed. So now the number which was right in his mind wasn't right. And the story behind data before AI, and also the calculations have changed because they've acquired different businesses, and nobody pays attention to that. So they think AI can bring a magic

van and start building the calculation. It could, like you're saying, but I would think the data will always remain the foundation, the thinking behind how the data needs to be modeled, what the calculation needs to be, the human touch, the human control needs to be there. Copilot is only going to expand or explain the wrong thing in a bigger and better way. That's right. Yeah, co-pilot would emphasize what you have in your model. If you have a good model, you get good output. If you have a bad model, you probably would get even worse out. The other thing you just touched upon is Dex Calculations and DexNM. You know, I remember seeing Chris Webb and others from Europe

and then you writing and teaching on DexNM over a decade, I would say. Copilot can now write a whole lot, but then there are like these best practices that's been taught through. So what does a PowerBI consultant or a BI developer actually spend their day? I've got a lot of juniors and they say, why do I need to learn the concepts of Dex? I can just give a command

to copilot and I don't have a good answer. So I'm pushing you on to you. What would be the good answer for me to give it to them? Yeah, so the thing is this is not just Dex or M. This is like everywhere in programming. Like if I'm a developer,

why should I go and learn C-Sop, Copilot and write it for me? Why should I go and learn Node.js? What should I go and learn Java script, all of those things. But what I say is that, of course, covalet can go and write these for you, and it actually improves the covalet we use today. It would be much better in time. It would even write better calls. But still, this needs to be vetted. This needs to be checked by someone that is it

correct or not. Now, you may not go and check every single measure, Dax measure that this creates for you. But if it happens that that tax can't. that it generates for you has a low performance. Then you need to go and check it. It doesn't work with just telling Copilot that, well, this is low performance going to write it another day. You need to go and look up the code and see, for example, this use digital function, which is probably performing this slower

than or can't be part of that function which might perform much better. How do you know that? By knowing the language, by knowing the functions, the learning is are going to be different than the old days. You may not go and write the whole program yourself. You may not go and write the whole DAX, an M, C-sharp, JavaScript, all of that yourself. You get copilot to write most of this for you, but you have the capability in case it is needed to go and look at a code, know what this is not the right way to do with and change it.

And that is only happening when you know the language very well. No, very true. And we also talk to our consultants now that you need to know the subject area a lot more than before. You could get by just being a technologist before. But now, because technology is acting on your behalf, you need to come in front and ask the right questions, do the right testing, give the right architecture, give the right advice to AI so that AI can perform better for you. validate to that level. And that shift is, I think we are in that transition stage, especially I work with India, a huge team. They need to, I'm sure you're dealing with the same thing.

They need to have this mind shift towards, you know, having more thoughtful thinking, solution-oriented structure than just a technology-oriented structure, isn't it? Correct, correct. What I think will happen, and it is already happening in the, in many organizations is that AI agents, LLM, Corpilot, ChatGPT, Cloud, all of these things are kind of replacing workforce. And that is a big concern for many people.

Well, who does it replace? It actually replacing Junior workforce. Because, like, for example, the junior workforce that wrote this mediocre code, now ColPilot can write probably the same code or might be even writing it better. The workforce that this cannot replace at this stage is someone who has a good technical understanding,

is someone who has good knowledge more like a senior. So I would advise people to go on that route instead of saying, well, I don't need to learn this language, so I would be an engineer, so at some point chat GPT would replace me. No, that is not the route I would suggest people to go. Awesome. Now, that's a great advice. I also want to switch gear a little bit.

So we talked a lot about how to use the technology, where to use it, what to do and what not to do. But now the technology is out there, especially Fabric, which I'm big fond of. Do you have any examples like one or two real customer problems that fabric is solving today using AI or Power BI Plus AI in last six months maybe? Yeah, we do have actually both. So in general, we have customers who we had actually customers whom been previously working with some other technologies, combination of the costs for licensing they paid for doors, now has improved a lot that we migrated them to fabric.

That is just like pure fabric, not even using AI. Now, the use of AI, we have a little bit different story these days. The use of AI, so the general expectation is that this, should speed off the process. But the fact is that nowadays, we are not at that stage yet. Nowadays, we have to prepare our data well for the AI to consume. So we actually spend more time in building our model. So let's say the semantic model that I used to create, for example, previously in three weeks time. Now I might even spend one week more, like make it in four weeks time because I want to add all the synonyms. I want to make sure that this has all the logics

that the AI can leverage later. So when you look at it from a customer product view, the short-term view might be that this cost them more. But the long term you, like after some months, they get to see the results. They get to see the fact that now that we have built this model, now that we have prepared this data, all in fabric, all-in-power BI,

or I can just sit on top of it and give them really efficient visualization reports from what we already have. So they save a lot of budget, a lot of resources on that side. Any KPIs you had with your customer, no need to name the customer, but just the KPI is like 3X faster or from days to minutes or something like that. Nothing explicitly that we have measured it. But I remember, like, one of the projects that we have had this kind of things was

not necessarily implemented using fabric because this was a little bit older day. we used some analytical engine and AI engine with machine learning. We had a customer who have been using a lot of pre-flight booking, and they've been looking for finding out what is the right window to do the pre-flight booking. And they realized that the right window for the pre-flight booking is actually the window based on this decision three algorithm We calculated that this is, for example, like six, seven weeks before the ticket time for international flight and domestic flight was different.

And the revoke was different. In a six-month, they saved pretty much like half a million dollars just for that particular change in the time that they make their flight booking. Oh, that's a great example. In fact, we work a lot with manufacturing and we worked with one client. In fact, we just completed the project where we were dealing with manual POs and, you know, email or printed. And we started doing with like scanning OCR with Power Automate, AI builders, things like that. And also using fabric on top to use the intelligence of data from their ERP. And you taught us real-time AI or BI.

I wrote a book on it too. But this was near real-time AI, right? So they were able to get this multi-day approval into a near real-time approval cycle, and they were very happy about it. So those are some of the good examples that you have mentioned. I also want to touch upon, you know, how do you tell your customer whether AI investment in PowerBi or Fabric is actually working? How do you balance speed against the governance? So we talk about F8, F-16, F-64, and most of our customers are talking about it.

especially speed against, you know, what they're investing, but also the governance. So governance plays a big role over there as well. What do you have to say on that? Well, of course, I would first, like whenever a customer comes to us asking about utilizing AI in their environment, first I would challenge them to think about how much they are going to invest in the data underneath AI. Because if we consider AI, like tip of the iceberg, the underneath that is all the data that we have prepared.

That data needs a lot of work to be prepared for the AI to consume. Not only that we need clean tables, clean data warehouse, things like that. We also need to have clean processes. We need to make sure that workspace design is correct. We need to make sure that we have the Nadalian architecture. We have the enablement of self-service users. We have all of these processes defined because AI is just one part of all of these aspects. If I have business logics defined by the VIT, but there is no business logic input in that.

That's not going to work. How would I add my business logic by enabling self-service users? Self-service users, if I just do this 100% self-service, I wouldn't have governance, so I have to find that sweet spot between. so that I have good governance alongside with the software service. Now, when you consider all of these, if we start with looking at a trial project from this point of view, that let's first get all the foundation right, let's get all the architecture right, let's get the model right,

and everything else, then investment on AI makes sense. Then investment on AI, I would say the more the merrier, right? We would get the better outcome of this. Then I have a potential customer coming in saying, well, we want to spend much on AI, but we don't want to go and build data warehouse. We don't want to go and do data integration. We don't want to go and walk on the process that will bring data from other places, things like that.

That means that it doesn't end well. Like it might generate a couple of reports, a couple of dashboards for them. That is just good for now for like a short period of time. but after a while they realized that this is just a report on top of a chaos of a data that unless we go and fix that chaos of data, it doesn't really lead to a good outcome. Now, that's a great example. We have a parallel story. Most clients we work with obsess with speed.

So they can't articulate the success metric, but they can say, oh, what does the success looks like for 90 days? And we put that question back to them. I said, what Texas looks like for you in next 90 days? So we can support. Technology merely follows what your success looks like. If you don't have a metric, then your project will drift. So something like that is crucial.

If you don't have a definition of what is a definition of done, then you are always going. And the speed, no matter what you have, is not going to work out for you. That's right. Okay. I want to touch bases back onto your passion. which is teaching. I know you have been doing teaching for 20 plus years.

Amazing. I see your YouTube hits. I think you recently reached 10 million views, which is phenomenal. So congratulations. Thank you. And you have survived every platform turn, right? From SQL server to SSIS to PowerBI to Now Fabric.

And this changes everything. And it changes everything cycle as well. And life has thrown plenty of you at outside. of work too. So I'm not going to go into the details, but I'm mainly talking about mindset. What's the practice of mindset that keeps you teaching, shipping, meaning building products and showing with this positivity. I've always seen you very positive person, despite of everything that I know about you. So what do you have to say to someone listening who is in a really tough chapter in their life right now? Yeah. So I would say,

well, life happens. We have circumstances that does not always go in our favor. We have good days and bad days. Good days are not hard. When I have a good day, I'm productive, I go out with my friends, I go and build this piece of code, I write this code, I write this application, I am in a good mood.

The real thing is not how to handle our, Bad days. What I found myself that is working is that there are some tasks or activities during the day that I would consider them as the tasks and activities that would drain my energy. And there are some tasks and activities that would give me energy. If I do that, like for example, if I go for a hike, although this would drain my physical energy, but it would give me a good mental energy to do something. go for work with my friends, if I go and play pickleball or tennis with someone, these are giving me energy. If I go and go out to code, that also gives me energy for a lot of people that might not. If I go and work with particular technology, if I go and work with cloud and copilot to go and go to the application, that gives me energy. But if I go, for example, and do some account

teeth, I'll be like that. That's drain my entire energy. So then, then you and have to find the right balance of doing these things in a way that you don't have in one day all your energy-draining activity, right? If I start my day with things that I don't like and then do a two-tree of those, then by midday I wouldn't have energy. For many, the things that I really like to do, I like working with technology.

This technology could be like back in the day's exercise, now powered life, fabric, AI. I still love to work with technology. The other thing that I like is to teach others about what I learned. I started this like 15, 20 years ago with just a blog for myself. I wrote that blog just for myself to keep diary of, for example, I did this in SQL Silver, and then later on I found people that would get benefited from this blog. They'll come and read the blog and they realized that this is helpful for them, so this became more like a, a source for also teaching others.

So that way I can, I get some feedback from there that this is working for them, this is not working for them in a situation that I haven't worked before, right, because they are working in different environments. On the other side, this fight that I'm helping the others gives me energy. So one of the things that encourage me

to do a lot of these things, like blogging, video, videos on YouTube, going to different conferences, presenting, training, or left all of talk, is the fact that I actually like to help people, but the way that I help people is different than the way that, let's say, someone else might help people.

I help people with teaching them the technologies and the tools that I use. And that gives me energy, and I'm glad that that also helps people to get benefit out of it. Now that's amazing. Can I say teaching is a stabilizing force for you? So discipline versus inspiration, right?

So discipline to run the business and do things, but inspiration to do the teaching, become a provider. Is that correct? Yes, yeah, that is right, yes. Awesome. This is very inspiring, Reza. I love the answers we got here.

Thank you. I want to go back to the Microsoft front again. So you've been at the first. front of every Microsoft data platform. You and I both go to different conferences. You go in a lot more than I could afford. But looking forward for

five years, where do you actually see AI going and even data? I always say data and AI together. I never differentiate between the two. So where it is going for a practitioner like you and me, and it's not just a headline because a lot of people chase

AI as a headline and not as you know the electricity which will run the operation, the lights and everything. If you are advising a 22-year-old yourself, me or someone else, who just landed in their first BI data or even AI role, what would you tell them to spend the next two years and what to ignore? I would say that AI is going so fast, right? The AI that we know today is, let's say, combination of LLMs and agents,

things like that. A lot of us have seen like agents for doing different things. Now, in a few years, this would change completely. We would have like multi-agent programs, an agent that, like, for example, I would say, I want to have this project, this project manager agent would go and hire

two developer agents, one tester agent. They would go all together and build something entirely without any interaction for me, which is kind of neat. I'm steady because I would be more productive in the business. It's scary because then what the workforce we do. So this is quite critical. A lot of organizations these days are thinking about what would happen in the future.

Of course, we're not living in that future yet. So we don't know exactly what is going to happen. But there are some signs of it. At the moment, I would say the first sign of it is that this would save a lot of time for organizations, instead of hiring, let's say, 10 junior people, they might use a combination of agents to do that, right?

So my advice, based on that, to young generations, is that they need to work hard, utilize AI, to learn more about the subject that they are going to be expert on. To focus on a particular subject. I don't recommend them to go and do a little bit of everything,

right? It won't be a whole ocean with just one meter. Try to focus on a specific area, let's say data analytics. But then go and learn about that data analytics. A, I can help you actually to learn that. Like, for example, what are the top? Let's say things to learn. I'm going to follow.

Yeah, what are the top channels to follow if I'm going to learn updates about fabric things like that? Then utilize that and go and learn it. not to tell co-pilot that go and learn this and give me a summary. They actually need to go and spend time and learn things that will they become proficient. When you are proficient in your job, not only AI cannot replace you in a short time. The other thing is that you would find some areas of improvement that others who haven't been working with that, that much didn't find.

For example, just giving you an example, but one of the things that we did in robot ad was to come with a tool called Power BI HOF. A lot of organizations

are using it at the moment. The reason we came with that tool was that we worked with so many power bio solutions that we realized the first thing we did

when we woke with a customer is out we can find out what exactly is happening in the Power BI file. And he built a tool that connects

to the Paral BI file, give us all the documentation, which measure is coming from, which measure is not used at all, things like that. And that tool was a tool initially for us to use internally, then we got other people to use it, right? So when I work in that particular environment quite deep, I get to know that what other tools or services might be necessary. Then in the future, I'm not allowed to go on both those for me.

you would have that opportunity that you won't have if you don't go deep in that part of the technology, I would say. No, that's a great advice. And in fact, I tell young consultants the same thing. I tell my own son, the tool will change every two years, the way you think won't. Spend your time learning to ask the right questions and explain the answer in one sentence. That's a skill being succinct is going away. and I really hope and wish the youngsters now chase and acquire that skill more than anything. Yeah.

So thank you for that advice. I'm switching gears again. So you run the Power BI and Fabric Summit every year. I was fortunate to speak in that couple of times. It's a community-led events in the space. And I had the privilege of speaking not on the stage but online, but from a listener who has never heard. of the summit. What is it? Who is it for? And what do you want for them to know in the next

year's edition or even this year's edition? Yeah. Thank you for mentioning that. So the Parabri-Bi and Fabric Summit is a yearly conference that we run fully virtual. It's not in person. It is not running in a particular venue or a city or a country. It is fully online. We want it in two different time zones so that one in the world depends. It doesn't matter. world there are, like it might be in in the different parts of Asia, Europe, U.S., Australia, in Louisiana, there is

always a time frame that would work for them. We have sessions in different rooms, like eight sessions usually in parallel happening, sessions after each other, each of them is like about an hour, 40 minute session, the Q&A after that. The Q&A is live, the session itself is pre-recorded

or have the best quality of presentation for the attendees, and who should actually attend this. I would say anyone who work with data analytics with Microsoft data analytics specifically because this is focusing on Power BI, on Microsoft Fabric. So as one does you do anything with any of these technologies. For example, you might want to have, like your organization might want to

migrate to fabric. This is a perfect conference for you to come here because we have a range of sessions. It is from the dinner all the way to advance. We have sessions that are like deep dive for someone who have been working with PowerDIA for like 10 years and we have sessions for someone who hasn't worked with PowerDIA at all. If any

of these technologies is something that you would work, this is the right conference. The price of this conference compared to, let's say, in person conference is not comparable. Like you pay thousands of dollars for in-person conferences, plus the fact that you need accommodation,

you need travel plan. something like this, you sit behind your desk at your office or in your home and you watch the sessions. You can also interact with live with team's Q&A and you can watch these sessions as many as times you want. So I would say this is a great value for anyone, not just for the learning experience, but also for the connection, getting to all these different speeches, all the different people who are working with the technologies that you are. working with. The next one that we have is coming last week of February, 2027. We haven't had the call for speakers out yet, but soon we will have it and soon the website for selling the ticket will be also open. Now that's great and do they get access to the

content post-conference once it is done? Yeah, so we'll have like four-hour access to this content. We call us lifetime access. This means that even after after the conference is completed, you can watch decisions as many as times you want to be the online platform, but you have access to that online platform forever. Yeah, no, that's great. And speaking from my own experience last two years

when I was able to do it, I see more practitioners rather than more novice, meaning people who have already playing it, and that shows the value because they want to learn something bigger and better. While you do say that people who are, not into the state technology at all can come into and learn from it as well. But I see more advanced users also coming to the conference and I need some great

connection. So the density is unique and I have to commend you on to that. Yeah. Thank you. Thanks for mentioning that. So let's move on to something lighter. One of my passion is playing guitar. I see you are an amazing guitar player.

And I've always thought music and, technology, especially like teaching, materials, learning, structuring, they are very closer than they look, both about pattern, repetitions, listening, and improvising inside structure, right? You can draw a lot of parallels without going into those details. Does that lens show up in how you teach PowerBi or run RadCat? Or am I projecting? Yeah, yeah. I think that pattern is pretty much everywhere.

the music, also, like, if you want to learn language, with the language, it might be still a little different. But in music, it's kind of exactly like that. So you start by learning your technique first. Then once you learn the technique, you go and play that technique quite a lot to make sure that you master that technique. But once you learn that technique, that technique and that technique, then you start to improvise between these.

that how can I story in the written, how can I story in this structure but improvise in a way that I would be more creative so I might find some interesting things. That is exactly like the way that we do the music,

I would say the same way that I would teach Power BI or Maxo Fabric or any other subjects that I'm teaching. I think that is the way that I run myself better and that is the way that I try to teach others as well.

Yeah, I think see that and great explanation practice is generally an unsexy core of any craft it's whether it's music or decks so you would learn different keywords techniques but at the same time you need to be creative so i think technology requires a lot more art than the science even though science is very important so is music so that's a great parallel you draw thank you thank you thank you You've spent 15 years as an MVP and 20 years in this ecosystem. I was talking about the long arc, you know, which has been the throw line today. What does the version of Reza who started blogging at Raducat in like SSI's days?

In one sentence, what have you learned by surviving every platform that's done since? So like technology is changing, but you are surviving. What's the secret? Well, I would say start thinking out of the dogs of technology. Start thinking of what problem you are actually solving. Like if you are doing the BI project, you are helping organizations to make informed decisions. So then you would need data. You would need clean data.

You would need clean data to be modeled and analyzed in a way that can be consumed. This might be in the past with just SQL the script these days with Power BI in the future. It might do a different technology. If you are doing the programming, the programming itself is not what you do. You are helping other businesses to build applications that they use on a daily basis. This application might need a database to store some of the information, and it might also need some forms of entry into data, making sure that the amount of errors when entering the data is reduced.

So when you think out of the box, when you think that these are the problems that I'm solving, then you just try to find how you do that with the technology. Two years ago with SSIUS, if I want to do a slowly changing dimension, and it was a different process. Nowadays, if I want to do that a slowly changing dimension in the arcs of fabric using pipeline and data flow, it would different process.

but still it's the same thing. Still, we are solving that particular challenge. The very that I looking at it is that how we can help each other to get to solve that particular business problem. No, that's great. So building a content engine, so you have learning and being very passionate about technology and also teaching.

But what kept you going on blogging, writing books, and running an academy at the same time? for a decade, where people quit at six months, and everything requires dedication, hard work, discipline, consistency, and above all, inspiration. What keeps you going on these areas, which is more of an influencer area? Yeah, I would say just passion. I like technology. I like working with technology.

So every day, every reach that I work with technology, I start to record these, sometimes as a video, sometimes as like a blog post, so that I can refer to, later myself because I don't have like a memory of like 20 years of things. Like if I want to find something that I even I have done in the past, it's better to go and look at my blog in the past or my video in the past. So consider it as a passion and also as a little diary for myself to read later, but others can also get benefit from it. And that's inspiring, 100%.

I got inspired and I started doing this while I was thinking. I'm not good enough to do this, but you have to start somewhere, and we are in top 20 on Apple podcast. The listening side is working great. Thank you so much. You are the inspiration. So we run this podcast for three types of audiences, AI curious, enthusiast, and skeptic. What's the one power bi fabric thing each of them should learn this quarter? So if you are an AI curious, what you should learn, or enthusiast, what you should.

what you should learn, or skeptic even, what you should learn. Well, if you are, I would say, starting from Stockwick, I would say you would better to get to know with MCP servers. These are giving really interesting things about now that you can use AI agents to talk with these technologies. If you are like AI just enthusiastic, I would say start working with things such as co-piloting power. I see how this can help you. And if you are in the middle layer, I would say try some of the technologies in

markets of fabric, such as data agent, fabric agent, or even prep data for AI of Pararia, that would help you to understand how AI result can become even better when combined with these. Oh, no, that's very nice. Thank you for that suggestion. One of the thing I forgot to touch base on is RedCat. You're running a successful academy, and it's a paid training work in the market where so much free videos, including yours,

free documentation from Microsoft, certain free community events. What made paid training work for you? Yes, so as you mentioned, we have a lot of material, a lot of content, already free, available, like our blog, has like thousands of blog posts,

our YouTube channel has like almost a thousand YouTube videos, things like that. What makes it beneficial for someone to come and learn using a paid platform? The thing about YouTube or blog or things like that is that they are not structured in a way that we usually go and learn something. Right, for example, my blog one day might be about writing this tax time on intelligence calculation, in another day might be related to what is the governance principles in Microsoft Fabric. Whereas if I'm going to design a video course, I would have it laid out entirely, what is the syllabus of image, where this starts, where this stands,

with some practical examples to go through as well. And that is where the main value is for someone to start from Grand Zero in that particular area, to go through all of these aspects in a structured way. For example, the particular DAG's function that I explained in that YouTube video like the same function that I explained in this video course as well. But because now it is in the context of the course, it is in the right place, it is in the right order of elements, it makes much more sense. So people usually found this way of learning much better.

Whereas the free content usually is good for someone who is already for active, who already knows the basics, just want more tips and tricks here and there, whereas the actual structured learning is to actually go and learn that in a deep level. Yeah, better structure and sincere learning requires something to be paid on, and that would make sense to do and speed that up.

Last question before I close. You are from New Zealand. You have worked in New Zealand community in Microsoft, versus US and EU. What's different about how the community shows up, the people, the warmth, the organization, what's the difference or similarity that you draw? Yeah, so I would say in terms of the community,

we have a little good community in New Zealand. We have been working with this community since, like when I moved to New Zealand, 2012, like 14 years ago, like we had user group sessions that it was only three people in the community. like in that room, me, a speaker, and the attentive, right? Now, if we run a user group meeting, we have like 70, over 70 people easily in the room and we have to close the booking

because otherwise we don't have enough of space. So the community is growing, the community is nurtured, it's a good community around using Microsoft technologies. If we compare the overall usage of data analytics technologies in, let's say, in New Zealand compared to US, I would say we have

a slightly more progressive usage of AI in US and Europe compared to New Zealand, Australia, we have organizations who are using AI, but they are mostly still in the process of

leveraging what these AI functions can do for them, a lot of proof-off concept, whereas in our US customer, in our, let's say, in America, customer base or our customer base, we have companies using it already in production. Of course, we're going to get to that stage as well, that it's just, I think, a little bit lag in the market to get there.

In terms of community, however, I think they both are really a strong community. Great, great stuff. So, Reza, thank you so much. Before I close, anything you have to add? No, thank you, thanks for the opportunity to be here with you, and I hope everyone enjoyed working with the technology. Thank you. So that's Reseret, founder of Radicat, 15-time Microsoft MVP,

continuous since 2011. Wow, just talking about it gives me a goosebumps. And one of the most consistent teachers in Microsoft Data Platform community, find his blog and the Radicade Academy at Radicat, I'm going to leave a link here. His books on Amazon, his videos on YouTube, and you can obviously find him on LinkedIn and run into him in conferences.

If this video help you frame your own PowerBi, fabric, or AI strategy, share it with one person on your team who's wrestling with the same question. That's the highest compliment a podcast can get. Thank you, Razah. Thank you again. Thank you, Dave. Thanks, everyone.

You have been listening to Think Yeah, a podcast with Dave. Take one idea from this episode and turn it into action.