Data Engineering

Knowing when an AI agent has failed sounds straightforward — until it isn't. Agents have a frustrating habit of finishing confidently while quietly doing the wrong thing, or looping endlessly without ever crashing in an obvious way. This episode tackles one of the thorniest probl ...  Show more

Despite what the marketing hype might suggest, AI agents are far from infallible — and if you've ever actually used one, you already know this. Today's episode dives deep into the many, varied, and sometimes surprising ways AI agents can fail, from subtle reasoning errors to casc ...  Show more

<div class="wp-block-jetpack-markdown"><h2>Summary</h2>

Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Sometimes, however, one of those transformation ...

Poor data engineering is like building a shaky foundation for a house—it leads to unreliable information, wasted time and money, and even legal problems, making everything less dependable and more troublesome in our digital world. In the retail industry specifically, data enginee ...  Show more

<h2>Summary</h2>

Data pipelines are the core of every data product, ML model, and business intelligence dashboard. If you're not careful you will end up spending all of your time on maintenance and fire-fighting. The folks at Rivery distilled the seven principles of mod ...

<div class="wp-block-jetpack-markdown"><h2>Summary</h2>

The most important gauge of success for a data platform is the level of trust in the accuracy of the information that it provides. In order to build and maintain that trust it is necessary to invest in defining, monitori ...