📅 Week 49
Operation: Cloud Bait · Azure honeypot field notes in Four minutes and forty‑eight seconds. Azure · Cowrie · Azure Monitor Agent · Log Analytics · KQL · DCR · DCE
[cloud security, azure, honeypot, security]
I built a deliberately tempting, deliberately harmless server in Azure, told nobody its address, and wired every keystroke into Log Analytics. This is how it was built, what broke along the way, and what the internet did to it.
📅 Week 46
Low cost does not mean low security: A Small Azure Storage Lab With Real Security Lessons
[azure, storage, identity, cloud security]
A team needs a small Azure Storage account for IT training. They want to prototype storage scenarios, upload a few files, and let new engineers practise without paying for a production-grade disaster recovery design.
The Cyber Resilience Architecture Behind Serving Public Facing Content from Azure Storage
[azure, storage, architecture, cloud security]
A marketing team needs to serve product images, videos, and customer success stories to a global audience or maybe any public facing content. Traffic is growing fast, latency matters, and the content itself isn’t secret — it’s meant to be public. So why does this scenario still trip up so many teams?
Designing for Data Redundancy in Microsoft Azure
[cloud security, data storage, azure]
I was a victim of the Iranian drone strikes that hit three AWS data centers in the UAE and Bahrain targeting Amazon cloud infrastructure affecting my databases in MongoDB Atlas. I lost production data and I haven’t been able to recover it to date. It hit me hard and I stopped ignoring to also design for availability and safety in my applications.
📅 Week 45
Design Data Storage Solution for Non-Relational Data
[cloud security, data storage, azure]
Data storage is how different data is stored and managed in your organization. The type of data storage that you implement is based on two things: structure of your data and how your data is accessed.
📅 Week 40
Performance Testing and Its Types With Practical Examples
[software testing]
A system can work perfectly for one person and still fail when hundreds of people use it at the same time. Performance testing helps us discover these problems before users do.
📅 Week 26
Database Replication Concept Explained
[backend, system design, interviews, databases]
M-Pesa handles the equivalent of Kenya’s entire GDP multiple times over, its system cannot afford to lose a single record or go offline.
Tools to Know for Backend Engineers
[backend, tools]
As a backend engineer, it’s essential to familiarize yourself with the key tools that are commonly used in the industry. Here’s a comprehensive list of tools you should know:
📅 Week 25
Authentication and Authorization: Knowing Who They Are and What They Can Do
[backend, system design, interviews, security]
Most developers mix these up. Authentication and authorization are two separate steps, and confusing them leads to insecure systems and confused interview answers.
System Foundations: From One Server to Millions of Users
[backend, system design, interviews]
Every complex system you admire today (Netflix, Uber, Stripe, Mpesa) started as a single server running everything. Understanding that journey from one box to a distributed fleet is the foundation of system design thinking.
System Design Interview Questions and Answers
[backend, system design, interviews]
These questions reflect real system design rounds at companies like Google, Meta, Amazon, Microsoft, Stripe, and Uber. Each answer follows the structure interviewers expect: clarify requirements, outline the high-level design, then drill into components with tradeoff analysis.
Multi-Factor Authentication: Passkeys, TOTP, Authenticator Apps, and Two-Step Verification
[backend, security, interviews]
Passwords alone are not enough. They get phished, leaked in breaches, reused across sites, and brute-forced. Multi-factor authentication (MFA) adds layers beyond the password so that stealing one credential is not enough to compromise an account. This post covers every modern MFA method, how they work under the hood, and the tradeoffs that determine which ones you should implement.
RESTful APIs: CRUD Operations, Status Codes, and Production Best Practices
[backend, system design, interviews]
REST is not a protocol or a library. It is a set of architectural constraints that, when followed, produce APIs that are predictable, cacheable, and easy to reason about. This guide covers the practical mechanics of building REST APIs that other developers actually want to use.
Databases: Choosing Between SQL, NoSQL, and Graph Stores
[backend, system design, interviews, databases]
The database you pick shapes everything: your query patterns, your scaling strategy, your consistency guarantees, and the kinds of bugs that will wake you up at 3 AM. This is not a “use whatever you know” decision. It is a design decision with real engineering tradeoffs.
Load Balancing, Health Checks, and Eliminating Single Points of Failure
[backend, system design, interviews]
Once you move to horizontal scaling, you need something to sit between your users and your servers to distribute traffic intelligently. That something is a load balancer. But choosing the right distribution algorithm, handling server failures, and avoiding single points of failure are where the real engineering decisions live.
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