The Most Expensive Mistake of My Career Wasn’t a Line of Code, It Was a “Yes”
I’ve spent 20 years in system architecture, networking, and enterprise software development. Over this time, we’ve said “hello” to countless technologies and had to bid farewell to some. We’ve all reached a point in our careers where we’ve chased a technology only to later say, “this was a waste of time.” For me, this meant discarding some approaches I once had high hopes for, even investing significantly in them. In this post, drawing directly from my experiences, I’ll explain why some popular trends don’t actually work or have lost their relevance over time. My goal isn’t to belittle anyone, but rather to pragmatically evaluate this technological evolution that we all go through.
On this journey, I’ve encountered many things that made me think, “I wish I had never gotten involved.” Sometimes a technology failed to deliver on its promised potential; other times, my understanding or implementation of it was insufficient. The important thing is to learn from these mistakes and keep moving forward. Now, let’s take a look at the technologies I’ve discarded and the realities behind those decisions.
The “Single Database for Everything” Myth and Its Realities
In the early years of my career, especially in small and medium-sized businesses, one of the most common approaches was to connect all applications to a single, massive database. This seemed simple and easy to manage at first. However, over time, the problems caused by different workloads sharing the same database began to surface. One application performing intensive operations directly impacted the performance of others.
For example, while working on a production ERP, the excessive load from an inventory tracking module would slow down the invoicing module that used the same database. This situation disrupted workflows and led to significant time losses. Even worse, a schema change in one module could unexpectedly cause errors in other modules without us realizing it. This experience taught me that the “monolithic database” approach has serious weaknesses in terms of scalability and isolation.
While struggling with these issues, the idea that each application should have its own database began to make more sense to me. Of course, this brought its own set of challenges; data consistency, distributed transactions, and complex queries, among others. However, these challenges were often more manageable and isolatable. Ultimately, I evolved from an approach that “kept everything together” to one that “kept everything in its place.” This brought a significant improvement in both the overall health of the system and the speed of development processes.
The “Magic Wand” of Distributed Systems: It Doesn’t Always Work
Microservices architecture has become the darling of the tech world in recent years. Its promises of scalability, flexibility, and independent deployment sound very appealing. I, too, became very interested in this approach at certain points in my career and tried to implement it. However, especially in my initial attempts, I realized I had underestimated the complexity this architecture brings. With each service having its own database, its own CI/CD pipeline, its own monitoring, the total system management overhead became immense.
Once, when I tried this approach for a side project, I started with just a few services. But as things grew, the number of services increased, and managing their communication, error handling, distributed transactions, and other aspects turned into a real headache. The dependencies between services became so complex that a small change in one service could cause a chain reaction. The debugging process turned into a nightmare; I had to sift through logs for hours to find the root cause of an error.
This experience showed me that microservices are not a silver bullet. Especially if your team is small or your project is in its early stages, starting with a simpler, monolithic architecture can be much more sensible. Over time, as needs grow and the system becomes more complex, breaking down this monolithic structure into smaller services in a controlled manner often offers a smoother transition. The principle of “get it working, then make it beautiful” becomes even more important in this context.
Event-Driven Architecture and Over-Engineering
Event-driven architecture (EDA) is a very attractive concept for promoting loose coupling between systems and its ability to respond in real-time. It can provide great benefits, especially in large-scale and dynamic systems. I also recognized the potential of this concept and tried to implement it in some of my projects. However, as always, experiencing the gap between the ideal and reality was inevitable.
In an enterprise software development project, we decided to structure all business workflows around an event-driven approach. This was quite exciting at first. Every time a process was completed, an event would be published, and other relevant services would listen to this event to trigger their own operations. However, we soon began to experience serious problems with event management, ordering, and consistency. Issues like which event should occur first, tracking whether an event was successfully processed, and rollback in error situations became incredibly complex.
This situation made me understand the concept of “over-engineering” more deeply. We don’t always have to choose the most complex and “modern” solution. Sometimes, a simple REST API call or a direct database operation can be much more efficient and manageable than the complexity brought by an event-driven system. While I appreciate the power of EDA, these experiences also taught me the importance of using it where it’s truly needed and in the right way.
Conclusion: The Reality That Learning Is a Continuous Cycle
Throughout this journey, I’ve seen that technology is constantly changing, and what was “best” yesterday may not be “good enough” today. But more importantly, I’ve understood that it’s not so much the technology itself, but how we implement it and the actual needs of the project that are decisive. Sometimes, the most brilliant-looking trends may not be the right answer for our specific situation. Being pragmatic, clearly seeing the trade-offs, and sometimes being able to say “this doesn’t work” allows us to build more robust and sustainable systems in the long run.
What about you? What technologies have you discarded in your career, things you’ve said, “I wish I had never gotten involved”? I’d love to hear the stories behind those decisions.