lakehouse lab short-form video show has formally launched; a new episode will be published each week and tool, process, and topic suggestions are encouraged
Category Archives: AI
well, shoot… (i guess i do like writing queries via chat)
i actually like writing code and did not imagine i would enjoy just asking my business data analysis questions with natural language, but i’m nothing if not flexible and open to reevaluating my opinions
what is driving the semantic layer revival? (ai can’t live without it)
decades later semantic layers are still a good idea. will the value they provide agentic ai finally be the reason enterprises build & maintain these valuable business context dictionaries?
don’t lead your chat-based llm (it wants to please)
ai tools want to please us, but their overly-agreeable responses are tweaked to make use happy, not necessarily provide the right, or best, response — don’t trust the response at face value!
yarp: yet another rag post (this time using sql)
you don’t have to know python or bother your data scientists to start exploring genai concepts like rag; you just need a tool that offers these features in a familiar sql interface
unstructured docs in ai (the wild west)
rag ai apps can only be as good as the parsed and chunked data that fuels them – testing, testing, and more testing the outputs of all the various available libraries with the front-end apps is critical
the effect of ai on intelligence (behold the idiocracy)
the long-term benefits of sunscreen have been proved by scientists whereas my advice on ai has no basis more reliable than my own meandering experience; i will dispense this advice now, but trust me on the sunscreen
develop, deploy, execute & monitor in one tool (welcome to apache nifi)
for those not familiar with apache nifi, come on a short overview of how this framework rather uniquely spans so many of the phases of the typical software development lifecycle
exploring ai data pipelines (hands-on with datavolo)
after explaining what rag ai apps are all about & showing what a typical ai data engineering pipeline looks like, i wanted to offer a hands-on lab exercise actually building a simple pipeline use datavolo cloud
understanding rag ai apps (and the pipelines that feed them)
i’m learning all about rag ai apps and wanted to try to explain, at a high-level, what these are all about plus do the same for the etl pipelines that are key to their success