What role will Microsoft Fabric play in your future careeer?

hey everyone and welcome back to the channel in today's video we've got something a little bit different for you now to finish off this series what I'm going to be doing is stepping away from the technical side of fabric you know understanding about pipelines and lake houses and all this kind of stuff and really I want to focus on you and what fabric could mean for your career now I think it's normal for a lot of people out there especially powerbi developers to look at the news around fabric start learning about Fabric and then suddenly think hm this could really represent a bit of a change in direction for my career a lot of the world of data engineering data science analytics engineering suddenly becomes a lot more accessible so you got a lot of ideas around H should I be focusing on this for the next few years in my career so in this video we're going to go through a few things we're going to start by looking at some of the options available to you in a fabric World in terms of your career we're going to talk about why I think that could be a beneficial move for you and we're also going to talk about some of the mindset shifts that you might have to go through some things you might have to think about differently when you're moving into different roles like analytics engineering data engineering data science now this is a topic I'm particularly passionate about because this is a transition that I made in my own career so about six or seven years ago I was building powerbi reports within a company and then I moved more into data science roles and more recently data engineering data strategy roles so this is something I've got a lot of views on on and hopefully I can kind of share some of the lessons learn and hope that it helps you make good decisions over what you're going to be doing in the next few years of your career so let's get into it so to kick us off I wanted to go through some of the options that are available to you so the first option really is to continue what you're doing with powerbi like if you love building reports and you're happy with that that's absolutely fine I'm not going to stop you from doing that that's absolutely great now I do think it will still be very beneficial for you even if you want to continue you down that powerbi report development route to learn more about fabric because in the future I believe that a lot of the reports that you're going to be building is going to be built from data in fabric as more and more companies migrate over they start doing a lot of their storage and modeling inside fabric it's really going to help you in your career if you know more about Fabric and I think the more you learn about fabric the more valuable it's going to make you as a powerbi developer for example if if you can take control over you know creating sequel views in a data warehouse for example molding the data that you need that you need for your reports on your own if you can take some of that autonomy it's definitely going to help out your company and it's going to make you a lot more valuable take the pressure of data engineering teams takes the pressure of analytics engineers and it makes you a more valuable employee for your company if you can do more of that work yourself and one thing to bear in mind is that power is a very powerful tool it's kind of like an allinone Swiss army knife it can do lots of different things right you can do data ingestion data transformation modeling and data visualization all in one tour and that's absolutely fantastic but just because you can do something in one tour doesn't necessarily mean that it's the best place to do these things with the introduction of fabric a lot of that data ingestion data storage data transformation piece is going to be moved Upstream right you probably heard of Ro's maxim of data transformation data should be transformed as far Upstream as possible and as far Downstream as necessary right and what that means in a fabric world really is that a lot of the ingestion transformation stuff is likely going to be done in Microsoft Fabric and for me anyway I see the future of powerbi being a lot more focused just around the visualization piece semantic modeling defining relationships anything that you have to do in powerbi like filter context and uh that kind of stuff that's where I think the future of powerbi be a lot of the transformation cleaning all of that stuff should be done in Fabric and this has the benefit that your data sets can be validated your data set can be shared more easily they're not just confined to one semantic model you're kind of building Enterprise data assets that you can share amongst your teams more easily so if you do want to continue down this route of developing powerbi dashboards and reports absolutely go for it I think it will still be worth your while to learn little bits of fabric as much as you feel comfortable with so if you're a little bit bored of just churning out reports you could shift into analytics engineering so what is analytics engineering well the way that I kind of think about it is if you're a powerbi developer your product is your report right that's the thing that you're you're building and you're focusing on building beautiful reports and the end user is your your business user right the consumer of that report well in analytics engineering that Focus shifts right your your data becomes the product itself and your users are the people that are going to consume that data so it could be the powerbi developers could be data scientists data analyst anyone that needs like high quality data and when you make data the product itself really have to shift focus in your mindset really you need to think about how you can deliver high quality data that's complete that's validated timely well modeled and also documented as well these are all slightly different things to what you think about when you're building a powerbi report could mean things like Source controlling your ETL jobs could be testing your code validating your data segregating different development test and production workloads and data so that you're really sure that you're delivering a high quality product at the end of each pipeline right your product being the data ultimately your goal is to build robust pipelines that reliably produce high quality data sets that can easily be consumed by the user now personally I find this work a lot more interesting and I would definitely argue that it's a more valuable skill in businesses because businesses are crying out for high quality data I think we've gone through the phase of bi where it's nice to just build lots of reports now I see the big kind of industry shift towards the demand for high quality tested validated data sets on which you can build your reporting your machine learning models all that kind of stuff more Downstream the focus in investment and the focus in data strategies becomes how do we make sure that our data sets are of really high quality by the time they reach that gold layer now if becoming an analytics engineer is the goal for your career then a good obvious first Target is the dp600 exam to become a certified fabric analytics engineer it's going to introduce you to a lot of new technologies ways of thinking ways of work and it covers quite a wide range of the fabric Technologies right lake houses data warehouses pipelines data flows all of these things and how we can use them together git integration Advanced semantic modeling building large models and that kind of thing as well so I definitely recommend if that's your goal that's a really good first step to you to learn what it takes to pass that exam and on this channel in the future in the next few weeks I'm going to be starting producing more content specifically around DP P600 to help you pass that exam so watch out for the announcements if you're not subscribed already then make sure you are subscribed to get these videos when they launch so next up we have the option to become a data engineer now the distinction between a data engineer and an analytics engineer those lines are quite blurry and they can mean different things to different organizations so I don't want to get too hung up on the syntax terminology but for me a dat engineer is doing a lot more work in spark in the data engineering experience right so they're probably going to be better python programmers they're going to be doing more of the complex Transformations working more around parameterization and automation right so how can you do ETL jobs across all of the tables in your bronze layer for example working out how to pass really thorny like Json structures doing a lot of more complex stuff as well as more like system optimization how do we make sure that this engineering system as a whole is working reliably so things like setting up testing for for your code making sure that that whole data pipeline is robust now as well as that I would expect data Engineers to be really proficient in the data warehouse know how to set up a data warehouse structure a data warehouse set up a lake housee optimize a lake housee and kind of the underlying Delta tables as well so they're a little bit more technical a bit more advanced skill set so if this is your goal then a good route into that OT is via the analytics engineering route now next up we got the data scientists so a lot of you might want to move into data science you want to be building machine learning models that predict things about your data in your organization right so the distinction here really for me at least anyway is data science focused a lot more around what's going to happen in the future so powerbi developer you're doing more descriptive analysis about what's happened in the past data scientist is looking about what's going to happen in the future what's your revenue of your company going to be what's going to impact your Revenue in the future those kind of problems and they're actually really interesting problems and I've worked as a data scientist in two companies and I absolutely loved it what I would say is for people that want to transition from kind of powerbi to data science it's going to be a bit more challenging there's a lot more skills that you have to learn you have to learn about the data science process you have to learn programming to quite a good level in python or R I'd recommend Python and alongside the programming side of things you also going have to learn about a lot more theory around machine learning models how do they work how do you choose the right ones for different scenarios depending on your data and your problem but the one thing you have as a powerbi developer or at least hopefully is a really good understanding of the business right so the data scientist is a lot more business focused most of the time so you're solving problems for the business and often you're communicating your results to people in the business as well so your skills as a powerbi developer in that respect are going to be quite transferable and useful right so if you got experience presenting a dashboard to the business and kind of selling that idea selling your analysis to the business that's going to be a really useful skill if you want to move into data science but there is going to be quite a few steps for you to make to become kind of a useful data scientist I would argue now for me how I did that was I did a master's degree in data science took two years part-time so that's where I built up a lot of the theoretical knowledge and also practical knowledge of how to like Implement data science and do data science in a business now one thing I would say that if this is your goal then I would argue it's still beneficial to go through the route of analytics engineering because what I find is that a lot of data scientists especially in kind of the early career data scientists a lot of your work is going to be similar or more similar to data engineering or analytics engineering right you're going to have to learn how to clean data sets very effectively very quickly building some sort of data infrastructure and so from my experience at least the role of the data scientist especially in smaller companies can actually look a lot more similar to a data engineer right because if you don't have a team of data Engineers to help you productionize your code or build views and extract data from databases you're going to have to learn all these skills anyway right so it helps to be really good at SQL to get some data in a format or to be really good at python to learn how to clean data transform data model the data that you need in the shape that you need it to do some sort of machine learning some sort of predictive analysis so if your goal is to be a data scientist just beware that there's quite a few steps you need to go through and I think it's still worthwhile for you to go through the rout of analytics engineering data engineering learn those skills first because they're going to be really valuable if you then decide to go into a role in data science so that's some of the options that are available to you in the world of fabric there's probably a lot more these are just some of the main ones that I wanted to touch on now one thing that I think not many people understand or think about when they're making this kind of transition or thinking about whether it's the right thing for them and I think it's really important is that fabric is built on top of technologies that are very very widely used in the industry right so you're talking tsql python spark Delta all of these things are very very transferable skills a lot more I would argue than the skills that you're building up in the world of powerbi so this opens up a lot of options to you in your career and learning these Technologies is actually quite painful right so you need to choose your pain wisely I would argue so for me it makes a lot more sense to direct your energy your learning energy into the technologies that are going to give you the biggest leverage in your career so for me those are things like tsql and python because every company uses tcq and Python and by going really deep on techn Oles like Dax and power query in the powerbi world that's great if you want to spend your career working in powerbi but if you invest that time and the energy into learning things like SQL and python that's going to open up a huge amount of opportunities to you because every company uses these Technologies so let's just round up this video with a few next steps for you I would argue that a good first step for anyone really is to get a thorough understanding of analytics engineering and how that works in Microsoft fabric so understanding more about that engineering mindset because it underpins a lot of the other roles right if you want to become a data engineer a data scientist a lot of these things the Technologies and the skill sets that you build up in the role of an analytics engineer they're going to be really really useful so how do you get going as an analytics engineer well we've already mentioned it I think a really good Target for you is the dp600 certification might require you to learn quite a lot of new technologies new skills but it gives you focus in your learning now that's not to say that if you become a certified fabric analytics engineer that's the Pinnacle of your career now you can rest and relax now for me it's a bit like passing your driving test right it shows that you're competent enough to be trusted to do certain things but a lot of people say that once you've got your certification that's when you really start to learn the technology and how actually apply it in the real world so that would be my initial Focus if I was working in powerbi today cuz it's very adjacent in terms of skill set right so you can leverage a lot of the knowledge you already have terms of semantic modeling I think semantic modeling is about 25% of that exam 75% might be new stuff but it's definitely worthwhile learning that the final point I want to make is around how you can become valuable very quickly because you might have built up a lot of skills in the world of powerbi but then if you want to transition into the world of fac really you need to learn how to provide value to your company very quickly right because you might be starting at a lower level in the world of analytics engineering CU it's quite new to you and the best route that I see for powerbi developers to become valuable in the world of fabric if you want to become an analytics engineer is to focus really on a few Technologies and learn them really really well fabric is a really vast platform and it can be easy to get overwhelmed you know you got real-time analytics data science data engineering data warehousing and so you really need to focus on the areas that are going to give you the most value as quickly as possible and for me as an analytics engineer I would be focusing on the data Factory experience so really learning data flows which hopefully you know pretty well already if you're a powerbi developer and data pipelines so these are kind of the two orchestration ETL tools that are used right across the data stack and so it would be really worthwhile learning how to get really good at these now I used to interview quite a few people at my last job and I was actually surprised how many people have full-time jobs just creating managing and maintaining ADF pipelines aure data Factory pipeline so can be your full-time job in some companies just to focus on the data pipelines part and it's quite a simple tool but there's layers and layers of complexity so it's easy to get started but then there's a lot of complexities and strategies for building more complex pipelines that you can learn in time but I think it's a really good place to focus your energy as well as the data Factory experience I would definitely recommend really understanding the data warehouse so this is more than likely going to be your gold layer in a company so it's the nearest really to the world of powerbi so here you're going to be able to transfer a lot of the skills that you built up in modeling building star schemas doing all that kind of stuff you're just going to be doing it in slightly different Technologies right so you're going to have to learn how to apply what you've learned in pobi to a different technology stack to the world of SQL right how can you produce that stuff using SQL views SQL tables stored procedures all that kind of stuff and SQL is a really really valuable skill set for you to learn and it will make you employable by basically any company in the world that does dat not just those that use powerbi so I definitely recommend that as an area to focus on thank you very much for watching that's what we got time for today and I hope you found that valuable my kind of ranting on different career options for you let me know if you are considering a career in Microsoft fabric which of these different options are most interesting to you this is the last video in the series where I have been helping you transition from the world of powerbi to the world of Microsoft fabc thank you so much for joining me hope you found the content really valuable and if you have I would be really really grateful if you can leave a like let me know in the comments share the series the playlist with people in your organization that you think might find it valuable thank you so much for watching and I'll see you on the channel soon

Channel: Learn Microsoft Fabric with Will

Views: 4,454

Keywords: power bi, microsoft fabric, fabric, powerbi, power bi fabric, career advice, lakehouse, data warehouse, data engineer, data scientist, analytics engineer, dp-600, career path, data engineering, analytics engineering, data science

Id: ahPDdHcvJ8E

Length: 19min 25sec (1165 seconds)

Published: Wed Apr 03 2024