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Category Microsoft Fabric

A split blue-and-orange technology illustration comparing Microsoft Fabric Mirroring and Eventhouse. Blue database cylinders and data pipelines appear on the left, while orange event streams flow into a stacked analytics platform on the right. Centered text reads “Mirroring or Eventhouse?” with “J. Michael Smith” below. A split blue-and-orange technology illustration comparing Microsoft Fabric Mirroring and Eventhouse. Blue database cylinders and data pipelines appear on the left, while orange event streams flow into a stacked analytics platform on the right. Centered text reads “Mirroring or Eventhouse?” with “J. Michael Smith” below.
02/10/ 2026
Mirroring or Eventhouse?

A team I was talking with recently had a familiar mid-market reporting problem. Their nightly refresh took eight hours. The data lived in on-premises SQL Server, stitched together with linked servers, and almost none of the tables had indexes. Reports were stale by the time anyone opened them, and if a job failed overnight, they were stale for a full day. They wanted to move to Fabric and build semantic models on top. The first question on the table was whether to connect the linked server to Eventhouse…

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11/09/ 2026
We Built the Grader Before We Built the Agent

Anyone can build a chat window that answers questions about invoices. Connect a language model to the data, ask, “What is our outstanding AR for this company?” and it’ll give you something. In a demonstration, it can look like magic. The problem is the word something. In accounts receivable, an answer that is close isn’t a smaller version of the right answer. It’s a wrong answer wearing a suit. If someone carries that number into a collections call, financial report, or board presentation, “approximately right” isn’t helpful. It’s…

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24/07/ 2026
The Dedupe Key That Doesn’t Drift

This article was originally published on LinkedIn and is archived here on texlytics.com. There is a reflex every data engineer has when they need to deduplicate rows: hash the row, compare the hashes, keep one of each. It is fast, it is simple, and on a well-behaved source it is correct. I reached for it without thinking when I built the ingestion for one of our CMiC AP voucher endpoints. It was the wrong call, and the way it failed is worth writing down, because the lesson holds…

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10/07/ 2026
The Most Important Table in My Microsoft Fabric Environment

This article was originally published on LinkedIn and is archived here on texlytics.com. Most data engineers love talking about pipelines. Others love talking about notebooks, Spark, semantic models, or dashboards. But after building a near-real-time Microsoft Fabric integration framework that ingests dozens of CMiC endpoints every fifteen minutes, I’ve come to a different conclusion: The most important object in my entire environment isn’t a pipeline. It’s a table. Not a fact table.Not a dimension table.A control table. What started as a simple metadata repository evolved into the operational…

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26/06/ 2026
Stop Writing One Pipeline Per Source

This article was originally published on LinkedIn and is archived here on texlytics.com. Every data engineer hits this moment. You’ve integrated three systems, you’re proud of it, and then a fourth source shows up. So you do what you did the last three times: copy the previous notebook, find-and-replace the table names, tweak the date logic, adjust the pagination, ship it. It works. You now have four notebooks that are 80% identical and 20% subtly different — and that 20% is where every future bug will live. Then…

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