Modern cloud-native systems often fall victim to their own scale. A single misconfigured deployment or localized infrastructure degradation can quickly cascade across an entire distributed system, compromising the service for all users simultaneously. When architectural boundaries fail to contain faults, engineering teams face catastrophic service level agreement breaches and prolonged recovery ti
🎓 Contexto acadêmico Universidade de Marília Disciplina: Projeto de Vida e Soft Skils Professor: Gustavo Comassi Autora: Jhenifer Gonçalves Januário Marília - SP | 2026 Com a evolução das aplicações para arquiteturas distribuídas, especialmente com o uso de microserviços, os sistemas deixaram de ser centralizados e passaram a ser compostos por diversos serviços independentes. Cada ser
The DataFrame class (from Pandas) is a work of art. Even if you never "do data", priceless lessons can be gleaned by studying this class. It starts simple enough. Usually you will create a DataFrame by ingesting from a CSV file or database table or something. But you can whip up a small one like this: import pandas as pd df = pd.DataFrame({ 'A': [-137, 22, -3, 4, 5], 'B': [10, 11,
Imagine you run a bustling coffee shop. In the beginning, you take orders, make the coffee, and serve pastries all by yourself. It works perfectly when you have a handful of customers. But as the crowd grows, you become the single point of failure. If you are stuck making a complex latte, the simple drip coffee line grinds to a halt. In software engineering, this "one-person shop" represents a mon
When we talk about Data Visualization and Dashboards, enterprise tools like Tableau or PowerBI often dominate the conversation. However, for Data Scientists and Developers, these GUI-based tools can feel restrictive. What if you need complex machine learning integration, custom UI logic, or automated CI/CD deployments? Enter the holy trinity of Python visualization tools: Streamlit, Dash, and Boke
[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada