BluePes Blog: Insights & Trends

Failure Models and Monitoring for Resilient Distributed Systems

In distributed systems, resilience is not a feature—it’s a necessity. With increasing complexity and interdependence across components, failures are not just probable—they are inevitable. The challenge lies in how failures are detected, analyzed, and mitigated to maintain seamless functionality. This article explores the critical aspects of failure models, monitoring practices, and tools for ensuring distributed system reliability.

• Jan 30, 2025
• 10 min

Scalability and Security in a Hyper-Connected World

In today’s interconnected ecosystem, scalability and security form the bedrock of successful software systems. Beyond being technical imperatives, they serve as key drivers for business growth, ensuring adaptability and resilience in high-demand environments. This is especially vital for industries like EV charging networks, fintech platforms, and logistics systems, where operational consistency and user trust are paramount.

• Jan 13, 2025
• 4 min

ETL vs real-time data pipeline: choosing the right fit

Deciding how to move data from source to destination sounds like an infrastructure problem. But it is really a business decision — one that determines how fast your teams can act on what the data actually shows. This article is for CTOs and heads of data at mid-market companies who are under pressure to support both historical reporting and live operational decisions. Next — a structured comparison of ETL and real-time data pipeline architectures, with guidance on when to use each and when to run both together. ETL — Extract, Transform, Load — remains the standard approach for analytics and compliance workloads. Real-time pipelines, built around streaming platforms, handle event-driven scenarios where minutes or seconds of delay matter. The two approaches solve different problems, and most production systems end up needing both.

• Jan 02, 2025
• 15 min

Emerging cybersecurity threats and how businesses can prepare

Walk through the typical mid-market company's tech stack and you'll find cloud services, a handful of SaaS tools, some legacy databases, a dozen third-party API connections — and every single one of those connections is something an attacker can probe. The people doing the probing aren't sitting in a basement typing commands manually. They're running the same AI tools your engineering team uses, just pointed in the other direction. This article is written for IT Directors, CTOs, and security leads — particularly those in healthcare, fintech, and e-commerce — who need a grounded look at which emerging cybersecurity threats actually warrant attention right now, and what a realistic response looks like when you don't have a 20-person SOC team. No vendor pitches, no theoretical frameworks. Just a breakdown of the threat categories that have materially changed in the past two years, the defensive moves that work, and a 90-day starting point if you're trying to make progress with limited bandwidth. AI has shifted the offense/defense balance in ways that matter operationally, ransomware groups have gotten smarter about leverage, cloud environments keep getting breached through basic misconfigurations rather than sophisticated exploits, and supply chain attacks have proven that your security posture now depends partly on how well your vendors manage theirs. All of these are addressable — but not by buying more tools without knowing what you're actually protecting. Updated in March 2026.

• Nov 25, 2024
• 14 min

Deep Learning Platforms

Artificial neural networks (ANN) have become very popular among data scientists in recent years. Despite the fact that ANNs have existed since the 1940s, their current popularity is due to the emergence of algorithms with modern architecture, such as CNNs (Convolutional deep neural networks) and RNNs (Recurrent neural networks). CNNs and RNNs have shown their exceptional superiority over other Machine Learning algorithms in computer vision, speech recognition, acoustic modeling, language modeling, and natural language processing (NLP). Machine Learning algorithms based on ANNs are attributed to Deep Learning.

• Mykola Lavrskyi
• May 11, 2020
• 7 min

Data Science Usage in Natural Disasters Predictions

Millions of people are affected by natural disasters each year. Wildfires, floods, tornadoes, volcanic eruptions, are just the beginning of a long list of potential disasters. Some can last a few seconds, while others can last for weeks. However, their effects can be felt for decades or even longer, and impact the global economy, infrastructure, agriculture, and human health. The worst part is that the future impact of disasters will grow dramatically due to climate change. Some regions, which previously rarely suffered floods or wildfires, now regularly experience the effects of these natural disasters. Researchers have collected a large amount of data and developed models that predict disasters, but most of these models are far from perfect. For instance, the amount of data that is monitored by satellites and various ground sensors all over the world each minute is incredibly large, and therefore presents a major challenge for researchers. Having lots of information can be an asset, but data requires computational resources. As more data is collected, computational models become increasingly complex and slow. Furthermore, since just a few minutes’ notice in advance of a flood or wildfire can save people’s lives, predictive models must be able to work and do corrections in real time. Artificial Intelligence (AI) techniques and approaches, like data mining, machine learning, and deep learning, can assist in disaster prediction. It is possible for AI to find hidden dependencies in data, which can be a basis for better understanding the mechanism of disasters, and, as a result, making better predictions. Good predictions and warnings reduce economic losses and save lives. We can’t stop most disasters, like floods, hurricanes, volcano eruptions, but we can be prepared for them. In this article, we will illustrate how data science can help in predicting different natural disasters.

• Mykola Lavrskyi
• Apr 20, 2020
• 9 min

Chatbots in NLP

Chatbots or conversational agents are so widespread that the average person is no longer surprised to encounter them in their daily life. What is remarkable is how quickly chatbots are getting smarter, more responsive, and more useful. Sometimes, you don’t even realize immediately that you are having a conversation with a robot. So, what is a chatbot? Simply put, it is a communication interface which can interpret users’ questions and respond to them. Consequently, it simulates a conversation or interaction with a real person. This technology provides a low-friction, low-barrier method of accessing computational resources.

• Mykola Lavrskyi
• Apr 13, 2020
• 5 min

Sentiment Analysis in NLP

Sentiment analysis has become a new trend in social media monitoring, brand monitoring, product analytics, and market research. Like most areas that use artificial intelligence, sentiment analysis (also known as Opinion Mining) is an interdisciplinary field spanning computer science, psychology, social sciences, linguistics, and cognitive science. The goal of sentiment analysis is to identify and extract attitudes, emotions, and opinions from a text. In other words, sentiment analysis is a mining of subjective impressions, but not facts, from users’ tweets, reviews, posts, etc.

• Mykola Lavrskyi
• Apr 06, 2020
• 6 min

Why Businesses Choose Self-Hosted Jitsi for Secure Video Communication

With more and more people spending time at home in recent years, finding ways to organize work well and be in touch with work teams is a top priority. There are many specialized services like Skype, Google Hangout, or Microsoft teams here to help us. But there is an interesting alternative: Jitsi, a set of open-source projects that allows you to quickly build and deploy secure video conferencing solutions for your company.

• Mykola Lavrskyi
• Mar 16, 2020
• 4 min