System Logs

On Computation and Algorithms

Exploring Different Types of Databases

Just a couple days ago I was lucky enough to attend the after party for Microsoft Ignite 2025 event with my dear friend. The event hosted with CockroachDB about the limitation of the current stack of technologies that hold up AI, and the future prospect of architectural changes of AI-native solutions. Before attending, we wanted to be prepared for some questions and answers so we studied about different databases and how they are being used right now. I found out that there were much more variety of types of databases than I knew and found it very interesting, so this post will not be much so about the architecture, but rather an overview of different types of databases and how they are being used in different scenarios.

A Brief History on Data Engineering

Recently I finished reading 'Fundamentals of Data Engineering: Plan and Build Robust Data Systems' by Joe Reis and Matt Housley, and went upon an important topic; the history of how data engineering evolved. The more I learn in technical topics like Computer Science, Software Engineering, and Data Engineering, I get the inclination that learning the history of how/why the common pattern of problems in that field emerge, and the evolution of different approaches in solving those problems, are critical. The problem patterns don't change much despite how the field is always rapidly changing; it's rather the solutions that change rapidly. So today I want to share the things I've learnt from that book.

Diving into FFT, and the relation to Divide-and-Conquer

During my undergraduate course, I had the opportunity to do a presentation on the concept of FFT (Fast Fourier Transform), along with having to work with a DFT (Discrete Fourier Transform) price prediction algorithm during my internship experience. So today I'll be covering what the FFT is, how it works, and why FFT is a DAC (Divide-and-Conquer) algorithm.