Tech Talk with Toni

  The formation of snowflakes is a complex and delicate process  This week, I added more tables to a data schema I created a few months ago.  To ensure optimal query performance when checking data quality, I took a deeper look at the differences between the two most popular schemas: star and snowflake. Now, if you are new to this, your first question may be: What is a data schema? To answer that question, let's start with the origin of the word.  "Schema" comes from the Greek word σχῆμα (skhēma), which means shape or plan. A data schema is the blueprint that defines the structure of the database, including tables, columns, data types, constraints, and entity relationships. Naturally, your next question may be: What is the difference between a star and a snowflake? Well, that's a good question! I hope we can both agree that a star may look like a snowflake, but it's not a snowflake. What makes a snowflake special is its tendency to branch out at the end.  Your fina

  Fun Fact: Luxembourg is the most productive country in the world (2023). In today's world, productivity can be defined as the rate at which value is created is generated per unit time.  This value can be in the form of products, services, or other contributions that positively impact individuals or society.  Coming from a background in Natural Science, I find it hard not to talk about the true producers: plants! As we all know, producers convert three raw materials—water, carbon dioxide, and sunlight—into glucose while releasing oxygen as a by-product of photosynthesis. Even though oxygen may not serve a direct purpose for the plant, especially during the day, there is always a demand for oxygen. Therefore, being a producer is more than just making things that are useful to yourself;  it's also about serving others.  CAPE Level Biology truly gave me an appreciation for plants, and just like them, humans also possess the ability to produce. However, unlike plants, we naturally

An inanimate object that takes the beating for us in simulated accidents. When I hear the word "dummy", a clip of an accident loops in the theatre of my imagination. These dummies, carefully crafted from materials like fiberglass, plastic, or silicone, strive to mimic our anatomy and go unnoticed despite their significant contribution to enhancing car safety. Analogous to mannequins, dummy data resembles real data while safeguarding the identities of the population being studied. This enables the study of human behavior without facing any legal repercussions. When creating your own data, you have the freedom to specify the levels of randomness, define field names, establish relationships, and assign data types that best suit your use case. In the context of the Caribbean, acquiring access to a reliable dataset is rare. As a result, I highly recommend generating your own dataset to start building applying what you have learnt.  In machine learning, dummy data helps researchers

  "In the midst of chaos, there is also opportunity." - Sun Tzu, The Art of War Yesterday, I had the opportunity to attend the Tech Hub Islands Summit (THIS), and it turned out to be one of the best decisions I have made this year. At the summit, I had the privilege of listening to some of the brightest minds in the industry, who shared their insights on the future, both at a regional and global level. One recurring theme stood out among all the speakers: the importance of continuous learning.   In this era where the status quo seems reminiscent of dystopian TV shows like Black Mirror, adaptability is crucial. Change has always been a constant in our lives, but the emergence of disruptive technologies has accelerated its pace. The days when a degree alone guaranteed a stable job for a decade or more are long gone. Put simply, those who are unwilling to embrace new knowledge and skills will inevitably be left behind in this rapidly evolving landscape. Instead of sitting down a