“We have failover.” That sounds reassuring. But when real failure hits… many systems still go down — hard. Why? Because failover is easy to configure — but extremely hard to make reliable at global scale. Here are the most common ways failover fails in production: RDS Multi-AZ enabled Kubernetes failover configured Looks good on paper. Reality: Takes minutes instead of seconds Gets stuc
SOFTWARE ARCHITECTURE & REFACTORING 3 Domain-Centric Architectures Every Software Architect Should Know The first concern of the architect is to make sure that the house is usable; it is not to ensure that the house is made of brick. — Uncle Bob The expression domain is occurring in software bibles for a very long time now and is heavily discussed in the book Domain-Driven
How we moved from a fragile loop-based payout system to a reliable, idempotent, and traceable architecture. On paper, payouts sound simple: Customer places an order Platform collects payment Platform pays the seller That's it. Until you try to do it at scale. In any marketplace or fintech system, money flows across multiple parties: Sellers / vendors Delivery partners Platform fees Discounts, vouc
Is your website throwing 502 errors whenever an external API starts lagging? It is a common engineering grind where slow dependencies choke your server and kill your response times. The fix is not adding more resources. It is about changing how you handle work. Stop making users wait for external processes to finish. Offload heavy tasks to background jobs and queues. Distinguish between workers
I’m going on a short vacation this week, so this post is coming out a bit earlier than usual. I actually had a different, more “useful” topic in mind — something educational, something responsible. But then I came across this fascinating article: I don’t like Tailwind. Sorry not sorry written by @freshcaffeine , and I couldn’t get it out of my head. So I decided to write a response instead. I actu
In modern data-driven organizations, managing and analyzing data efficiently is critical. OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are both integral parts of data management, but they have different functionalities. Understanding how they differ, and how they complement each other is essential for anyone working with data systems. Online Transaction Processing (
Every distributed system you build is already taking a side in the CAP trade-off. The question is whether you made that choice deliberately or discover it during an incident. CAP states that a distributed system can guarantee at most two of three properties: Consistency, Availability, and Partition Tolerance. The critical insight most teams miss — P is not optional. Networks fail. Pods crash. AZs
We tried to make everything perfect. Strict validation Looked good on paper. In reality: System was correct. But unusable. So we changed approach. Allowed partial data System became less perfect. But it started working better. In real systems, perfection creates friction. This shows up often in BrainPack deployments. When multiple systems are connected, trying to make everything perfect upfront us