Blog
Curating DuckDB Datasets Leveraging AI
In Curated MySQL Data Sets for Realistic Testing I described datasets assembled the manual way — download, schema, load, validate, document — over hours or days per source. This post is the follow-up I promised: what changes when AI assists the same workflow, using DuckDB as the target engine and GeoNames as the first example.
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Why INSERT IGNORE should not be used
Let’s say you’re building a reference table from source data, e.g. a silver medallion table from a primary source. In this example I am using a file of random locations on the globe extracted from OpenStreetMap (OSM) as my primary source.
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What Constitutes Clean Data?
Clean data is one of those terms everyone uses and few define precisely. Row counts match? No nulls in key columns? Values within expected ranges? All useful checks — and all insufficient once you move from a table view to a visualization view.
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Curated MySQL Data Sets for Realistic Testing
Synthetic benchmarks have their place, but I have always preferred working with real data. Not client production data — that stays private — but publicly available datasets that reflect the messy shapes, skewed distributions, and indexing challenges you encounter in the wild.
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COSCUP 2026: Planning your upgrade to MySQL 9.7
I recently had the pleasure of presenting Planning your upgrade to MySQL 9.7 at COSCUP 2026 in Taipei, Taiwan. COSCUP (Conference for Open Source Coders, Users and Promoters) is one of the largest open source conferences in Asia, and the community there is always engaged and technically sharp.
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DataEngBytes Sydney Overview
I have just returned from my first DataEngBytes conference in Sydney. Here is a quick summary on my thoughts for the event. I plan to go into more detail on great points from presentations in later posts.
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MITRE ATLAS - A 2025 Review
The MITRE ATLAS Matrix provides a working knowledge base of adversary tactics, techniques, and real-world case studies specifically for Artificial Intelligence (AI) and Machine Learning (ML) systems. It adapts the established MITRE ATT&CK adversary tactics and techniques matrix which is the standard reference for traditional IT cybersecurity.
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Foundational Frameworks for GenAI Security Analysis
Three frameworks form the analytical foundation of this series’ threat analysis, selected for their complementary coverage of the GenAI security landscape and their broad adoption across enterprise security practices. The MITRE Adversarial Threat Landscape for AI Systems (MITRE ATLAS) maps tactics and techniques specific to AI systems.
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A first look at MySQL 26.7 Early Access
MySQL has dropped its newest release , categorized as “Early Access” and available at https://labs.mysql.com/ . While this post is not going to go into depth, I wanted to at least validate the management changes you verify between normal MySQL upgrades.
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Where is the technology breakdown? Can AI help?
On a major financial institution website I was asked to complete a contact form. This organization has millions of existing customers. This is not a startup, yet the quality of work is something a junior developer would fail at an interview if they provided the answer.
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Why My Mac Was Not Using Post-Quantum SSH With GitHub (And How I Fixed It)
In my previous post I made the case that the only post-quantum protection that counts is the algorithm your connection actually negotiates. This post is what happened when I checked my own laptop.
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Q Day Is Coming: A Plain-English Guide to Post-Quantum Cryptography
Every time you see the padlock in your browser, push code over SSH, or your application connects to a database, TLS or SSH is quietly doing two jobs. First, it proves you are talking to the real server and not an impostor.
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VillageSQL Extensions with versioning
VillageSQL has just released it’s latest version 0.0.5 , describing this as stable release for installations with full MySQL 8.4.10 compatibility. With my primary work around extensions, specifically vsql-statistics , I took the new versioning features for a test.
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Producing IQR and Outlier statistics with SQL
The interquartile range (IQR) measures the spread of the middle 50% of a distribution — the distance between the first quartile (Q1) and the third quartile (Q3). Combined with Tukey’s 1.
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