21h ago · 6 min read · I got paged at 6am twice in three months because of the same class of problem Upstream API changed a field type. My pipeline ingested the bad data without complaint. Six hours later, a dashboard was s
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Mar 25 · 3 min read · GoldenCheck profilers are purely statistical. They look at patterns, distributions, and formats. They do not understand what the data means. LLM Boost adds semantic understanding on top of the profiler pipeline — catching issues that statistics alone...
Join discussionMar 25 · 3 min read · The hardest part of automated data validation is not finding issues — it is deciding which ones matter. Run 14 profilers across a 50-column dataset and you get hundreds of findings. Most are noise. GoldenCheck solves this by classifying every finding...
Join discussionMar 25 · 3 min read · GoldenCheck ships with 10 column-level profilers and 4 cross-column profilers. Each one targets a specific category of data quality issue. Together, they achieve 100% recall on planted issues in the DQBench benchmark — without any configuration. The ...
Join discussionMar 25 · 3 min read · Every data validation tool I have used makes you write the rules first. Define your schema. Specify your constraints. Configure your expectations. Then run the tool to check if your data conforms. GoldenCheck flips that entirely: validate first, keep...
Join discussionMar 20 · 3 min read · 5 Data Engineering Techniques That Increased Our LLM Efficiency by 70% Introduction: Why Data Engineering Is the Overlooked Engine Behind LLM Performance We boosted our LLM's efficiency by 70% — not by touching the model architecture, but by fixing w...
Join discussionMar 4 · 10 min read · A customer types their perfectly valid email address into your signup form. Your validation rejects it. They don't contact support—they just leave. You never know it happened. This is more common than most teams realize. Overly strict email validatio...
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Feb 26 · 8 min read · "Validate that it's a phone number. How hard can it be?" I've heard this sentence start many projects that ended in frustration. Phone numbers look simple — we use them every day, we know what they look like, how hard could validation be? Very hard, ...
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