Nicholas Meyer

I don't usually write post on LinkedIn, but I'm very exited about the latest update in Power BI desktop, in particular, User-defined Functions. For me, this feature will make writing and debugging DAX so much easier, it has a lot of potential to make developer's speed to delivery a lot faster, creating a standardise library of functions (check out https://daxlib.org/ by SQLBI) and code snippets that can be leverage by all developers in an organisation. Taking this further, the ability to write DAX closer to a functional programming paradigm would make the AI coding agents even more efficient at generating complex semantic models. This is exiting! I did an initial test (see some tips below) and can't wait to test it a bit more. I found the video by SQLBI very helpful and highly recommend it to all Power BI devs. #PowerBI

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The winners of the Fabric Notebooks for Power BI was just announced. The winner is linked below. Pretty impressive, they created a note book application (didn't know that was a thing!) that can run your refreshes to any semantic model you have permissions to. Very useful for things that are not automated or when you need to do off cycle loads. We will be running this! #PowerBI #MicrosoftFabric #Analytics #Notebooks https://lnkd.in/gPNMnp7u

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🧵 Hearing "GraphQL" for the first time years ago at a conference made me think I needed to dust off my old TI-85 and remember how to graph equations. Not really, but I did realize I was way in over my head. I was still early in my career as an Analyst learning SQL, and now there's this new Query Language? How do you use it? Why do we have it? What is the purpose and function? What does graphing have to do with any of it? Spoiler: nothing. The "graph" refers to graph theory. Now, I'm going back to basics to learn more about GraphQL: It's a query language for APIs that lets clients ask for the data they need, nothing more and nothing less. One endpoint, strongly typed, and self-documenting. Why dust this off now? - It's everywhere, with big tech companies running GraphQL in production - Microsoft Fabric now has native GraphQL support. You can spin up a fully typed API endpoint directly from your Lakehouse or Warehouse with zero code - It changes how you think about data modeling. Designing a schema forces clarity about relationships and what consumers actually need - It's strongly typed by default, which means better tooling, autocomplete, and catching errors before they reach production Looking forward to sharing what I learn, and hearing from others on this journey. #GraphQL #APIDevelopment #DataEngineering #MicrosoftFabric #LearningInPublic

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I made a post on Sunday that upset a lot of people and I'd like to clarify... Or maybe double down. Your first data role doesn’t need two years of data experience, but in my opinion, it does need some business experience, even if it’s unrelated. Here’s why: When you’ve worked a front-line job - frankly, almost any job - you’ve dealt with rules, policies, workflows, and probably a decision support system. And a lot of those systems are shaped by decisions made with data. ...Through dashboards and models supported by data teams. ...By people like us. Spending time in the business gives you a very real sense of what those decisions actually feel like. Sure, you know customer service can be frustrating, but there’s a big difference between “Yeah, that must be tough...” and “OMG, I remember a call where…” Data work impacts people. First-hand experience with that impact makes you a better analyst.

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Lately as I've transitioned into a new role I've been able to lean in to designing web application's instead of traditional BI dashboards (Looker, Power BI, Excel). I'm really beginning to see the value in this kind of approach. Short term this will likely give many organizations some initial growing pains (essentially another vector for developing technical debt) but long term I think we are going to enter a new age of Analytics where we aren't so limited by the capabilities of some large platform and are instead only limited by the skillset of the analytics teams. #data #analytics

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