Tag
8 posts
January 21, 2026
Mike and Tommy unpack a viral LinkedIn article where a consultant saved a client $57,000 per year by moving from Fabric F64 to Premium Per User — sparking a bigger conversation about when Fabric actually makes sense, licensing confusion, and the tipping points that justify the investment.
July 25, 2025
Mike and Tommy tackle a mailbag question about the optimal Power BI architecture in the Fabric era versus the pre-Fabric world of dataflows and shared datasets. They explore how the lakehouse, notebooks, and AI agents are reshaping what 'better' really means for data teams.
February 19, 2025
Mike and Tommy compare two practical ways to manage small-but-critical reference tables: Power BI Dataflows Gen1 versus a Fabric SQL database. They break down tradeoffs around refresh, governance, CI/CD, and downstream consumption so you can pick the simplest option that still scales.
October 9, 2024
Mike and Tommy compare Gen 1 vs Gen 2 dataflows, why the Gen 2 experience still falls short in key areas, and when Gen 1 remains the practical choice for Power BI and Fabric teams. They also call out what improvements would make Gen 2 a true replacement.
November 8, 2023
A rapid-fire mailbag: incremental refresh vs full reloads, migrating off Report Server, pragmatic workspace governance, and how Fabric adds new compute choices without changing the core tables→model→report pattern.
June 14, 2023
In Ep. 223, Mike, Tommy, and Seth continue the data ingestion series with a pragmatic Fabric lens: when to use pipelines vs. dataflows vs. Spark, what tradeoffs actually matter, and how to standardize ingestion so it’s scalable, governable, and maintainable.
June 9, 2023
In Ep. 222, Mike, Tommy, and Seth use Microsoft’s Fabric Decision Guides to pick practical defaults for ingestion and storage—and explain how to govern those choices so teams stop reinventing the platform on every project.
May 24, 2023
Ep. 217 is a practical conversation about Power Query functions—how to build a small library of repeatable helpers, where reuse breaks down inside PBIX files, and when it’s time to move transformations into shared dataflows or upstream pipelines.