Hello everyone! Welcome to another Interesting Tech Update on Analytics! Recently, I came across an interesting update about  chDB in the  TDLR Newsletter, which motivated me to explore it further. Until now, I’ve mostly worked with server‑based  SQL engines such as  PostgreSQL, MySQL, and SQL Server. I’ve also used serverless  SQL engines on many challenge platforms like DataLemur, LeetCode, and HackerRank. These platforms typically use in‑process  SQL engines, which don’t

Healthcare organizations generate huge amounts of patient and provider data daily. In this blog, we use Python to analyze the HC Dataset from Kaggle, focusing on patient discharge trends, provider specialties, and ambulatory visit patterns. Key insights include variations in length of stay (LOS), discharge dispositions, and visit volumes, which help hospitals optimize staffing, improve operational efficiency, and improve patient care.

I. The Goal: Testing the Hype In my last post, I documented how I built a custom dataset by pulling live data directly from Wikipedia. It was a process of turning messy web tables into a structured CSV file. Today, I’m putting that data to work to answer a question many of us have: Are record-breaking box office numbers a consistent new trend or are we just seeing a few lucky hits? By looking at the last 30 years of cinema, I wanted to see if the "Billion Dollar Club" is actu

Introduction: Challenging the Traditional Analytics Workflow Usually, the roadmap for building a data portfolio follows a very specific, linear path. The formula is familiar to almost everyone entering the field: Find  a pre-cleaned dataset on a portal like Kaggle. Download  the static CSV file. Clean  any minor remaining errors using Excel, SQL or Power BI. Visualize  the results in a standard dashboard. It’s a safe and effective process for learning the basics. However, as