Artificial IntelliTools

So, I'm sure you've heard of Google's Notebook LM. If you haven't, go listen to this podcast first, then come back. Seriously, I'll wait. Alright, now that you're back, let me give you a brief background on why and how this  started. For months, my teammates Andrea Lacche ,  Pradeep, and I have been working on analyzing New York City's noise landscape as part of a class project. The goal was to analyze noise complaints across New York City to better understand the urban noise landscape. We wanted to identify key hotspots and trends, ultimately providing insights into how noise pollution affects different neighborhoods and what measures might be effective in addressing these issues. We uncovered some pretty interesting insights, such as the Bronx having the highest number of noise complaints, particularly related to loud music, and identifying significant spikes in complaints during the COVID-19 pandemic. If you're curious about those and deeper insights

Understanding Factless Fact Tables I want to discuss Factless Fact Tables because, to be honest, the name caught my attention—it sounds funny, a "Factless fact?"   lol. And since am a simple person to please, I figured there must be an something interesting here or at least a reason behind it. Plus I figured that by understanding what "factless" means, I could work backward to understand what a regular fact table is… hehe. So, I’ll try to explain this in a way that's easy to understand. So What is a Fact Table?   S o much for working backwards am I right? lol. But bare with me for a sec In data warehousing, a fact table stores measurable data—things that can be counted or summed. For example, a "Sales" fact table might store: Product sold Number of units sold (a measure, or "fact") Sale date Sale amount (another fact) Each row in a fact table represents a measurable event, like a sale, and includes numbers that help us analyze performance. Wh