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| Business Intelligence Series |
Announced at the end of the last year, Microsoft Fabric (MF) become a reality for the data professional, even if there are still many gaps in the overall architecture and some things don't work as they should. The Delta Lake and the various data consumption experiences seem to bring more flexibility but also raise questions on how one can use them adequately in building solutions for Data Analytics and/or Data Science.
Currently, as it happens with new technologies, data professionals seem to try to explore the functionality, see what's possible, what's missing, and that's a considerable effort as everybody is more or less on his own. The material released by Microsoft and other professionals should facilitate in theory this effort, though the considerable number of features and the effort needed to review them do the opposite. Some professionals do this as part of their jobs, and exploring the feature seems to be a full job in each area, while others, like myself, do it in their own time.
There are organizations that demand from their employees to regularly actualize their knowledge in their field of activity, respectively explore how new technologies can be integrated in organization's architecture. Having a few hours or even a day a weak for this can go a long way! Occasionally, I could take 1-2 hours a week during the program and take maybe a few many more hours from my own time. Unfortunately, most of the significant progress I made in a certain area (SQL Server, Dynamics 365, Software Engineering, Power BI, and now MF) it was done in my own time, which became in time more and more challenging to do given the pace with which new features and technologies develop.
By comparison, it was relatively easy to locally install SQL Server in its various CTP or community versions, deploy one of the readily-available databases, and start learning. I'm still doing it, playing with a SQL Server 2022 instance whenever I find the time. Similarly, I can use Power BI and a few other tools, depending again on the time available to make progress. However, with MF things start slowly to get blurry. The 60 days of trial won't cut it anymore as there are so many things to learn - Spark SQL, PySpark, Delta Lake, KQL, Dataflows, etc. Probably, there will be ways for learning any of this standalone, though not together in an integrated manner.
The complexity of the tools demands more time, a proper infrastructure and a good project to accommodate them. This doesn't mean that the complexity of the solutions need to increase as well! Azure Synapse allowed me to reuse many of the techniques I used in the past to build a modern Data Analytics solution, while in other areas I had to accommodate the new. The solution wasn't perfect (only time will tell), though it provided the minimum of what was needed. I expect the same to happen in Microsoft Fabric, even if the number of choices is bigger.
There's a considerable difference between building a minimal viable solution and exploring, respectively harnessing MF's capabilities. The challenge for many organizations is to determine what that minimum is about, how to build that knowledge into the team, especially when starting from zero.
Conversely, this doesn't mean that the skillset and effort can't be covered by one person. It might be more challenging though achievable if the foundation is there, respectively if certain conditions are met. This depends also on organization's expectations, infrastructure and other characteristics. A whole team is more likely to succeed than one person, but not certainty!
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