When API calls on a client’s data platform surged by 500% in an hour, the monitor alarm blared, and the entire team had to sift through logs all night. It was then that I realized, “too many things at once” isn’t just a meme; it’s a real operational disaster. This experience compelled me to question the claim that the tech industry is becoming increasingly complex and to delve into the root causes.
Is the tech industry truly complex?
Complexity doesn’t automatically arise with an increase in the number of system components; the core issue is how these components are interconnected. For instance, when data flow in a manufacturing ERP is routed through multiple event buses simultaneously with a microservice architecture, monitoring points become scarce, and errors become “invisible.” A similar situation was observed in a bank’s internal platform when token refresh errors during the integration of different OAuth2 providers cascaded into a chain of delays.
What factors fuel complexity?
1. Mix of multiple providers and protocols
In one of my side projects, when I tried to back up data to three different cloud providers simultaneously, it turned out each had different API throttling limits. Ignoring these limits led to the system eventually throwing a “rate limit exceeded” error and halting the entire pipeline. Thus, managing multiple providers simultaneously adds an extra “layer of complexity,” especially in the automation layer.
2. Organizational processes intertwined with code
In an ERP project, embedding both business rules and data models into the same codebase to model the “purchase → produce → ship” flow made version management almost impossible. This situation led to “idempotency” problems after an update and emergency situations requiring manual intervention. This fusion between business processes and technical infrastructure transforms complexity from merely a technical issue into an organizational one.
What can we do to reduce complexity?
1. Simplified data flow design
In a client’s data platform, routing all data flow through a single “event hub” simplified monitoring and debugging by 70%. This change not only added a single line of code but also eliminated “duplicate processing” errors in the system. A simplified flow makes it possible to detect problems early and intervene quickly.
2. Adopting a “Fail Fast” and “Observability” culture
In one project, I activated reliability reporting for systemd timers, logging service failures instantly. As a result, when a service crashed, an alarm was sent to the team within just one minute, and the intervention time dropped from 15 minutes to 3 minutes. The “fail fast” approach doesn’t reduce complexity but minimizes the impact of errors in a complex environment.
How should the future balance be struck?
Completely eliminating complexity might not be possible; however, pruning “unnecessary” complexity is always an option. My recommendation is that before adding a new technology or tool, measure how much “load” the existing infrastructure can handle and integrate only components that truly add value. This approach can reduce maintenance costs by up to 40% in the long run and shifts teams’ focus towards innovation.
graph TD; A["Users"] --> B["Application"] --> C["Data Layer"] --> D["Analytics"] --> E["Report"]
Conclusion
Complexity is a natural byproduct of technological innovation, but it doesn’t become a problem as long as we manage it. The biggest lesson I’ve learned in my 20 years of field experience is to approach things with a “better process” mindset, not “more tools.” If you encounter a similar situation in your projects, try simplifying the system first, then adding new features. What do you think? Is the tech industry truly too complex, or are we just adding too many layers?