The 95-Minute Notebook
This article was originally published on LinkedIn and is archived here on texlytics.com. The symptom Our Bronze ingestion notebook on Microsoft Fabric had a runtime problem. The notebook pulls incremental changes from CMiC’s REST APIs into a lakehouse on a 15-minute cadence — dozens of endpoints, all driven from a single control table. The kind of thing that should finish in a few minutes. It was taking 95. Here’s what made it interesting: the Python wasn’t slow. The API calls came back quickly. The filtering and dedupe logic…
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Article II – Mission
The texlytics Charter Series Technology isn’t our mission. Understanding is. At texlytics, we don’t start with software. We start by understanding the business. Every organization has its own goals, processes, challenges, and people. Technology should reflect those differences, not force businesses into someone else’s way of working. That’s why we ask questions before we recommend solutions. The technology is never the destination. It’s simply the vehicle. That philosophy is what led to the creation of Origo. Not because the world needed another platform, but because we saw too…
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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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Article I – Purpose
The texlytics Charter Series The purpose of texlytics is to help organizations better understand themselves. We transform complexity into clarity by connecting information, people, systems, and intelligence so better decisions become possible. Those two sentences define everything we build. Businesses rarely struggle because they lack information. They struggle because information exists in too many places, disconnected from the context needed to make confident decisions. Information is often scattered across: Our job isn’t simply to connect systems. Our job is to connect understanding. When understanding improves: This philosophy shapes…
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We spent a year building a data platform. Here’s what AI can finally do with it.
This article was originally published on LinkedIn and is archived here on texlytics.com. Most construction firms that say they want “AI” don’t have data that AI can use. The accounting system doesn’t talk to the project management tool, half the operational truth lives in spreadsheets, and the same vendor is spelled three ways across three systems. Point the fanciest model on the market at that, and it will confidently produce nonsense — because it’s learning from a mess. So before any of what follows, the boring part. We…
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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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The Bug That Reported Success
This article was originally published on LinkedIn and is archived here on texlytics.com. One of our customers didn’t exist. Not deleted, not archived — simply absent. A real company, with real invoices, that should have been sitting in our analytics layer like everyone else. It wasn’t. And the system responsible for bringing it in had run cleanly every night for weeks. Green checkmark after green checkmark. No errors, no warnings, no failed jobs. The whole time, it was quietly wrong. This is a story about why the bugs…
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Our ERP’s API returned different results for the same query. Here’s how we dealt with it.
This article was originally published on LinkedIn and is archived here on texlytics.com. Run the query at 9:00 AM. You get 4,812 rows. Run the exact same query at 9:01 AM. You get 4,797. Run it a third time and you get 4,812 again — the exact count you started with. Relief. Except that run is the most dangerous of the three, and I’ll come back to why. No data changed. No filter changed. Same endpoint, same parameters, same everything. Fifteen rows just evaporated, and they were different…
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How I Solved CMiC’s 429 Rate Limit Challenge
This article was originally published on LinkedIn and is archived here on texlytics.com. If you have ever built an integration against the CMiC Cloud APIs, you already know where this story is going. HTTP 429. Too Many Requests. Over and over. I pull CMiC ERP data into our Microsoft Fabric Lakehouse on a schedule, Bronze through Gold. The pipeline ran clean for months. Then one morning I walked in to find it already in full meltdown, runs failing on throttling across the board. Nothing had changed on our…
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