In the last article, I covered the data warehouse architecture. In order to leverage data warehouse to its full potential and get most out of the data, data modeling becomes a crucial part. Dimensional modeling is a very popular choice for analytical data loads. It is widely popular due to performance improvements over transactional/normalized data and also it makes it very easy to understand the data. Due to the simplicity, it also helps to deliver business value quickly. Essentially…
Data is the lifeblood of any modern business, and as data volume and complexity grow, so does the need for efficient, scalable data management solutions. This is where a well-architected data warehouse comes into play. In this article, I’m going to break down the key components of a data warehouse architecture, including how data flows from raw sources through ETL processes, into structured presentation areas, and ultimately powers business intelligence applications.
It has been almost two years since the introduction of ChatGPT, and it has taken the world and startup community by storm. Since then, every business is trying to find an opportunity to leverage LLM capabilities to optimize their work or decision-making in some way. There are many groundbreaking developments emerging over time. My overall focus here will be on the basics of LLMs and prompt engineering. I am still learning and exploring this world, and through articles like this, I hope to share…
