Building Maintainable Web Apps: Lessons from 20+ Years in Tech

Building software is easy. Keeping it maintainable over time? Not so much. Over the years, I’ve seen simple applications turn into unmanageable disasters, all because developers overcomplicate things or follow trends that don’t actually solve real-world problems. If you want to build software that is easy to update, scale, and debug, here are some lessons I’ve learned that will save you from future headaches.

1. Keep It Simple, Stupid (KISS)

The biggest mistake developers make is over-engineering. Everyone wants to build something impressive, but complexity is the enemy of maintainability.

You don’t need microservices for every project. You don’t need the latest and greatest tech stack. If you’re building a typical web application, you should be using a framework that allows you to move fast and keep things simple. Grails, Rails, or Laravel are all excellent full-stack frameworks that handle backend and frontend seamlessly. They come with built-in solutions for common problems and let you focus on solving business needs rather than reinventing the wheel.

2. JavaScript Frameworks Have Their Place, But Shouldn’t Be the Default

Every year, there’s a new JavaScript framework that people claim will change everything. These technologies have their place, but they shouldn’t always be the default stack for every project. The reality? Most apps don’t need React, Vue, or Next.js. These frameworks add unnecessary complexity for projects that don’t truly require them.

If you’re building a typical business application, you don’t need a full-blown SPA. HTMX is a fantastic alternative that lets you create dynamic, interactive UIs without overloading your app with unnecessary JavaScript. Pair that with vanilla JavaScript when needed, and you’ll have a frontend that is easy to maintain and doesn’t require an entire team to manage.

Over the years, I’ve seen projects default to some form of React and MongoDB without much thought. All problems are different, and the technology used to solve those problems should be chosen based on actual needs, not just industry trends. Many of the projects I’ve inherited using these stacks were clearly written by developers who followed a tutorial or watched a YouTube talking head instead of truly understanding the project’s needs. Choosing technology should be about solving real problems, not just following the latest hype cycle.

3. Write Code for the Future You (or Someone Else)

If you’ve ever had to revisit old code and wondered, “What idiot wrote this?” only to realize it was you… you know why maintainability matters. Code should be written as if someone else will take over the project tomorrow.

• Use clear naming conventions.
• Document why something was done, not just what it does.
• Avoid clever one-liners that make debugging a nightmare.
• Follow consistent patterns so that new developers don’t have to guess how things work.

Good code isn’t about being impressive. It’s about being understandable six months from now when you’ve forgotten everything.

4. Test-Driven Development (TDD) Is Not Always Best

TDD is often treated as the “gold standard” of software development, but let’s be real: it’s not always the best approach for every project.

When TDD Makes Sense:

• You’re building something mission-critical where failures have serious consequences (e.g., medical, banking, security-related software).
• The project is large, complex, and requires long-term maintainability.
• You’re working in a team where test coverage is essential to prevent regressions.

When TDD Slows You Down:

• You’re building a quick MVP to validate an idea.
• The project scope is small and changes rapidly.
• You’re working solo on a project that won’t require long-term maintenance.

Instead of blindly following TDD, focus on pragmatic testing. Write tests where they add value—especially for critical logic—but don’t waste time writing tests for trivial things that are likely to change anyway.

5. Scalability Without Over-Engineering

A lot of developers build for scale before they even have users. That’s a mistake.

Scalability matters, but premature optimization is a waste of time. Most applications don’t need Kubernetes, distributed databases, or an elaborate microservices architecture. They need:

• A solid database design.
• Efficient queries.
• A caching strategy.
• A good deployment pipeline.

Start simple. If your app grows and actually needs to scale, you can optimize when necessary. Until then, don’t overcomplicate things for the sake of “future-proofing.”

6. Choose the Right Database for the Job

If your project needs a database, choosing the right one is critical. Too often, developers default to what they know rather than what fits the workload. The wrong choice can lead to scaling issues, performance bottlenecks, or unnecessary complexity.

Relational Databases (SQL) – Best for Structured, Transactional Data

If your data is highly structured and requires ACID compliance, a relational database like PostgreSQL or MySQL is likely your best bet. SQL databases excel when you need:

• Strong consistency and integrity constraints.
• Complex queries and relationships between entities.
• Transactions that need to be atomic, consistent, isolated, and durable (ACID properties).
• A well-defined schema that won’t change frequently.

Use SQL databases when you’re building financial systems, inventory management, traditional CRUD applications, or anything where data consistency is crucial.

NoSQL Databases – Best for Scalability and Flexibility

Not all data fits well into relational models. If you need flexibility, high availability, or horizontal scalability, a NoSQL database might be a better choice. NoSQL databases come in different flavors:

• Key-Value Stores (e.g., Redis, DynamoDB) – Best for caching or high-speed lookups where you don’t need complex querying.
• Document Stores (e.g., MongoDB, CouchDB) – Ideal for semi-structured data where documents may have varying fields.
• Column-Family Stores (e.g., Cassandra, HBase) – Built for distributed, write-heavy workloads that require horizontal scaling.

Use NoSQL when your schema is flexible, your data structure is evolving, or you need to handle large-scale, distributed workloads. Common use cases include real-time analytics, session storage, and event logging.

Graph Databases – Best for Complex Relationships

When your data is deeply interconnected, a Graph Database like Neo4j or ArangoDB can be the best choice. Graph databases shine when:

• Your queries involve complex, multi-hop relationships.
• You need efficient graph traversals (e.g., social networks, recommendation engines, fraud detection).
• Relationships between entities are as important as the entities themselves.

Graph databases are particularly useful in fraud detection, social media platforms, and recommendation engines where relationships define the value of the data.

Final Thought on Databases

Choosing a database is not just about preference; it’s about understanding the data model, query patterns, and scalability needs of your application. As highlighted in Designing Data-Intensive Applications, databases are designed for different trade-offs. Understanding OLTP vs. OLAP, event sourcing, and data replication models will help you make informed decisions.

Don’t just default to what you know. Choose the tool that aligns with your project’s actual needs.

Final Takeaway

Maintainable software isn’t about using the latest trends or following strict methodologies. It’s about keeping things simple, choosing the right tools, and writing code that future developers (including yourself) can actually work with.

If you focus on practical solutions instead of hype, you’ll build applications that last and are easy to improve over time.