BluePes Blog: Insights & Trends

2025 Cybersecurity Trends: Expert Predictions

Cybersecurity in 2025 is no longer just about defense —it’s about resilience. As attack surfaces expand and threats become more sophisticated, organizations must move beyond traditional security measures. The focus is now on proactive strategies that enable detection, mitigation, and rapid recovery from cyber incidents. This article explores key cybersecurity trends for 2025, analyzing emerging threats and new technologies shaping the future of digital security.

• 2025-03-13
• 6 min

Scaling EV Charging Networks: From Infrastructure to Intelligence

Expanding an EV charging network is not just about installing more chargers—it’s about creating a scalable, resilient, and user-centric system. The real challenge? Ensuring that as demand grows, charging stations remain efficient, grid impact stays minimal, and customer experience improves. Let’s dive into key lessons from scaling EV networks effectively.

• 2025-02-27
• 9 min

Building Systems That Scale: Lessons from Hypothetical Challenges

Scaling isn’t just about handling more traffic or adding new features — it’s about building systems that adapt to growth, complexity, and ever-changing user needs. Reflecting on previous hypothetical challenges, let’s explore essential lessons and how they align with current industry practices.

• 2025-02-13
• 9 min

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.

• 2025-01-30
• 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.

• 2025-01-13
• 4 min

Unlocking the Power of Data: ETL and Real-Time Architectures

Turning raw data into actionable insights is a cornerstone of modern business strategy. But achieving this requires more than one-size-fits-all solutions. ETL pipelines remain indispensable for historical data processing and analytics. However, when low-latency decisions and real-time responsiveness are critical, event-driven architectures and streaming data approaches take center stage.

• 2025-01-02
• 4 min

Future Cybersecurity Threats and How Businesses Can Prepare

Cyberattacks are no longer a question of "if" but "when". From AI-powered phishing to insider threats, businesses in every industry are grappling with increasingly complex challenges. In 2025, staying ahead means understanding the evolving threat landscape and preparing for the unexpected. In this article, we’ll explore the most pressing cybersecurity risks, real-world examples, and actionable steps your business can take to protect itself.

• 2024-11-25
• 5 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
• 2020-05-11
• 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
• 2020-04-20
• 9 min