data science

As we've already seen with the essential components (Part 1, Part 2), the Hadoop ecosystem is continuously evolving and being optimized for brand new applications. Consequently, various tools and technologies have developed over time...

Introduction Let’s speak about Kubernetes probes and why they matter in your deployments. When managing production-facing containerized applications, even small optimizations can have enormous advantages. Aiming to cut back deployment times, making your applications higher react...

What's synthetic data? Data created by a pc intended to duplicate or augment existing data. Why is it useful? We've all experienced the success of ChatGPT, Llama, and more recently, DeepSeek. These language models are getting used...

Working in Data Science, it will probably be hard to share insights from complex datasets using only static figures. All of the facets that describe the form and meaning of interesting data should not...

Machine learning and AI are amongst the preferred topics nowadays, especially inside the tech space. I'm fortunate enough to work and develop with these technologies on daily basis as a machine learning engineer! In this...

As data continues to grow in importance and turn out to be more complex, the necessity for expert data engineers has never been greater. But what's data engineering, and why is it so necessary?...

Popularity of RAG Over the past two years while working with financial firms, I’ve observed firsthand how they discover and prioritize Generative AI use cases, balancing complexity with potential value. Retrieval-Augmented Generation (RAG) often stands out as...

The “interdisciplinary approach” is something that has been lauded for many years...