SysAdmin

Last month I was debugging a dashboard that showed a sudden spike in customer age. Nothing in the app changed, but the chart was clearly wrong. The culprit was a tiny SQL mistake: someone used MAX(age) on a column that occasionally contained NULL, and a separate join duplicated rows. The number was technically correct for […]

I’ve lost count of how many times I’ve seen a perfectly good dataset turn confusing the moment it hits a dashboard: timestamps sorted as strings, monthly rollups that don’t align with business weeks, and “human-friendly” dates that quietly break filters. Most of those problems aren’t caused by bad data—they’re caused by mismatched expectations between how

I’ve inherited enough ASP.NET MVC apps to recognize the same pattern: the view starts as a small Razor file with a couple of inputs, then grows into a form-heavy page with conditional fields, validation messages, and a few “just one more” dropdowns. A year later, someone changes a property name in the model and half

Production bugs around time almost never come from “not knowing SQL Server has dates.” They come from subtle mismatches: local time vs UTC, datetime rounding, daylight-saving jumps, non-sargable filters, or mixing app clocks with database clocks. I’ve watched teams lose hours chasing “missing” records that were actually filtered out by a time boundary that shifted

Last year I watched a perfectly reasonable SELECT turn into a minor incident—nothing was “wrong” with the query, but it ran against the wrong database. The engineer had multiple environments open (local, staging, production), their SQL client reused a session, and the default database context quietly pointed somewhere unexpected. The query itself was harmless, but

I’ve watched perfectly capable teams ship features fast, then lose weeks to “mysterious” data bugs: duplicate customer records that disagree, invoices whose totals don’t match line items, permissions that flip when someone edits a profile. The root cause is often boring and predictable: we stored facts in places where the database can’t reliably enforce them.\n\nFunctional

Last year I walked into a post-incident review where the "database" behind a revenue-critical workflow was a shared spreadsheet, emailed around as a "temporary fix." It worked–right up until two teams edited the same row, a payment got refunded twice, and nobody could prove which version was correct. That‘s the moment most organizations stop treating

A library database looks simple until you ship the first version and reality shows up: the same title exists in multiple editions, readers change phone numbers, staff need accountability, and “return date” isn’t known at checkout time. When I design a Library Management System, I start with an ER diagram because it forces the hard

You feel this choice most when something goes wrong. The first time your app hangs because a test suite spawned 40 parallel workers, or a laptop went offline mid-write, or a customer asked for a point-in-time restore, the database stops being a checkbox and starts being a behavior. That’s why I treat “Derby vs MySQL”

I still meet teams who think “Hadoop” means one thing: a batch job that runs overnight and writes a giant file somewhere. In practice, the Hadoop ecosystem is a set of cooperating systems for storing data across many machines, scheduling work fairly, running multiple processing engines, and keeping the whole cluster consistent while dozens of

I still see the same failure mode in analytics teams: a warehouse starts simple, then “region”, “country”, “product category”, “department”, and “channel” show up, and suddenly every dashboard has a slightly different definition of geography or product hierarchy. Someone duplicates columns in three places to make BI “easy”, and within a quarter you’re debating which

Last year I watched a pricing pipeline “randomly” drift by a few percent every week. Nothing was crashing, dashboards looked healthy, and every single row-level rule seemed correct. The root cause was almost embarrassing: the team was averaging percentage changes arithmetically when the metric was multiplicative (compounding). The fix was a one-line change—move the math