Supply Chain Data Flow Management in Side Projects: Why the Overkill?

Working in the industry for twenty years, the habits ingrained by the corporate world inevitably seep into your side projects. Especially after spending a long time working on a production ERP system, I saw firsthand how complex managing the data flow of supply chain processes could be. From material requirements planning to shipment optimization, each step meant a separate data flow, a separate integration point.

However, when I turned to my own side projects, I realized this corporate mindset was leading me into unnecessary complexity. While side projects inherently require rapid prototyping and delivering value with minimal effort, I sometimes found myself unnecessarily contemplating enterprise architectures. In this post, I'll share my experiences on why we tend to overcomplicate supply chain data flow management in side projects and how simpler approaches can be more efficient.

The Fallacy of Transitioning from the Big Picture to Small Details

In corporate software development, we usually start with the big picture: business workflows, integration points, data consistency, scalability, and fault tolerance. When I worked on a production ERP, I thought about the entire process from order to invoice; I designed how each module—purchasing, production, inventory management, shipping—should have its own data flow. This naturally led to complex patterns like Event Sourcing, CQRS, and multiple microservices.

But when I embarked on my own side projects, like developing the backend for my personal financial calculators, with the same mindset, I hit a wall. I started questioning why I needed to set up a message queue for a simple calculation engine that related a few tables. While a corporate ERP has hundreds of tables and thousands of business rules, my own project might only deal with 10-15 tables. Applying the complexity of the former to a simple need in the latter only resulted in wasted time and energy. For me, this became a clear trade-off between the need for scalability and development speed. In a corporate system, milliseconds matter in high-volume transactions, whereas in a side project, situations where even seconds are acceptable are quite common.

ℹ️ The Corporate Complexity Fallacy

My experiences in corporate systems sometimes pushed me to consider unnecessarily complex solutions for my side projects. For example, thinking about the event-driven architecture we used to track stock movements in a production ERP for simply marking a task as "done" in my personal task management application. This situation not only extends the project completion time but also increases the operational overhead.

Last year, while designing the data flow for a side project's backend, a scenario I encountered in a past client project came to mind. For that client project, I had written an ETL process of over 1000 lines to pull data from multiple external APIs for supply chain integration, process it in a central data lake, and then distribute it to different departments. This process handled over 5 GB of data daily and provided integration with 12 different systems. In my side project, users just needed to create a simple list and add tasks. When I tried to proceed with a similar "data lake" logic for my side project, ignoring the difference between these two scenarios, I ended up spending a whole week on unnecessary infrastructure setup. In reality, a simple INSERT and SELECT operation in PostgreSQL met all my needs instantly. This showed me how critical features like PostgreSQL's B-tree indexes or GIN indexes are for high-performance production environments, whereas for side projects, more basic optimizations like connection pool tuning are often sufficient.

Data Flow in a Real Production ERP vs. a Side Project

Working within the ERP system of a manufacturing company, the data flow for the "buy-produce-ship-invoice" cycle was incredibly detailed. For instance, from the moment an order was received, to the raw material entering the warehouse from the supplier, its processing on the production line, passing quality control checks, and finally being shipped to the customer and invoiced—data flow was critical at every step. We had to ensure instantaneous and consistent information flow between stock levels, production schedules, quality control results, and financial records. This sometimes required simultaneous updates of hundreds of different data points and even mandated maintaining records compliant with international financial reporting standards like IFRS. In such a system, complex architectures like Event Sourcing, Saga Pattern, or distributed transactions were inevitable for data consistency and transactional integrity.

In my own side project, developing a simple inventory tracking system, the situation was entirely different. I was tracking perhaps 20-30 products, stock movements occurred only a few times a day, and I had no concerns about financial integration. Here, updating a product's stock quantity could be done with a single database transaction. While I wrote a 1500-line ETL script for IFRS integration in a production ERP, a simple UPDATE query in my side project met all my needs. For example, a query like UPDATE products SET stock_quantity = stock_quantity - 1 WHERE id = 123; represented the entire "supply chain data flow" management for me.

-- In a production ERP, part of a complex order processing scenario:
BEGIN;
    UPDATE stock_movements SET quantity = quantity - 5 WHERE product_id = 101 AND warehouse_id = 1;
    INSERT INTO production_orders (product_id, quantity, status) VALUES (101, 5, 'in_progress');
    INSERT INTO audit_logs (event_type, details) VALUES ('production_start', 'Product 101, quantity 5');
    -- ... Dozens of other insert/update operations on different tables ...
COMMIT;

-- In my own side project, a simple stock deduction:
UPDATE products SET stock_quantity = stock_quantity - 1 WHERE id = 123;

The fundamental difference between these two scenarios was the scale and complexity of the requirements. In a corporate ERP, a disruption in data flow could lead to millions of dollars in losses, whereas in my side projects, in the worst-case scenario, I could manually correct an error myself. This repeatedly showed me how much load even simple and well-designed tables in powerful databases like PostgreSQL could handle. For side projects, basic Linux tools like journald for log management and systemd units for starting simple services are often sufficient. [Related: Simple Linux Service Management]

Where "Overkill" Begins: Microservices and Asynchronous Communication

One of the first places where "overkill" begins in side projects, in my opinion, is the microservices architecture and the eagerness to adopt asynchronous communication. In the corporate world, microservices can become a necessity for large teams to focus on different business domains, enable independent deployments, and utilize different technology stacks. While working on an internal platform for a bank, different departments developing their own services and communicating via message queues was vital for scalability and flexibility. Here, a transaction could pass through 3-4 different services, and each service could have its own database. This highlighted the criticality of patterns like event-sourcing and the transaction outbox pattern.

However, in my own Android spam application, I started questioning why I needed to set up a separate "SMS processing service" and a "data saving service" with RabbitMQ or Kafka in between for a simple task like filtering incoming SMS messages and saving them to a database. A delay of 200 ms for each incoming SMS could directly impact user experience, yet setting up this complex structure only added 3 days of extra work. Ultimately, using the application's internal SQLite database directly to manage this data flow proved to be a much faster (less than 5 ms) and simpler solution.

⚠️ The Microservices Trap

Opting for microservices in side projects often increases operational complexity. Deployment, monitoring, and debugging processes grow exponentially. When I experimented with a microservice using Docker Compose on my own VPS, I remember having to examine the logs of 3 different containers for a simple error. This represents inefficient use of time for a side project.

In my experience, situations where asynchronous communication is truly necessary in side projects are very rare. Often, a simple threading mechanism or a cron job is sufficient to run a task in the background. For example, for a simple data collection tool I built for my website, I use a systemd timer that runs every hour and a Python script. This script pulls data from a specific website and saves it to my PostgreSQL database. Without needing any message queues or complex service architectures, this process runs smoothly. If an error occurs, I can see what happened in 5 minutes by checking journalctl -u my-service.service. This simplicity is one of the biggest advantages of side projects for me. After all, since I'm already struggling with configuring container memory limits and resolving build OOM errors in large systems, I prefer to stay away from such troubles in side projects.

Direct SQL and Simple APIs: The Forgotten Efficiency

Sometimes, due to habits from the corporate world, we tend to devise complex solutions even for the simplest problems. When I think about supply chain data flow management, API Gateways, data transformation layers, message queues, and orchestration engines immediately come to mind. Yet, in side projects, direct SQL queries and simple RESTful APIs can often meet most, if not all, of our needs.

While developing the backend for my personal financial calculators, I needed to take user inputs, perform a few mathematical operations, and then display the results. Instead of combining multiple services for this task, a single API endpoint written with FastAPI and simple SELECT and INSERT queries in a PostgreSQL database were sufficient. For example, a query joining three different tables (inputs, intermediate results, final results) ran in an average of 5 milliseconds. This speed and simplicity showed me how pointless it would be to set up a distributed messaging system like Kafka or design an event-driven architecture.

-- A simple data retrieval query for my personal financial calculator:
SELECT
    u.username,
    i.input_value,
    r.calculated_result
FROM
    users u
JOIN
    inputs i ON u.id = i.user_id
JOIN
    results r ON i.id = r.input_id
WHERE
    u.id = :user_id AND i.calculation_date = CURRENT_DATE;

The power of PostgreSQL, with the right indexing strategies (B-tree, GIN, BRIN) and connection pool tuning, can deliver surprisingly good performance even in simple systems. In my experience, avoiding ORM pitfalls like the N+1 query problem or eager-load explosions by being careful is much more efficient than building a complex architecture in most side projects. In a client project, we encountered a situation where a misconfigured ORM was executing more than 500 SQL queries for every user request. Optimizing the ORM settings to resolve this was much faster and more effective than designing a new data flow system.

💡 Hidden Power in Simplicity

In side projects, direct database interaction and simple APIs can be much more "get-it-done" than complex architectures. In my experience, correctly utilizing PostgreSQL's powerful features (JSONB, CTEs, Window Functions) has helped me solve many "data flow" problems without setting up extra services.

This also provides an advantage in terms of system security. Fewer moving parts mean less potential for security vulnerabilities. Protecting my Nginx reverse proxy with fail2ban patterns, implementing rate limiting, and using simple authentication mechanisms like JWT/OAuth2 are usually sufficient for a side project. While a complex data flow architecture might necessitate considering DDoS mitigation layers or kernel module blacklisting, I can avoid such worries with a simple CRUD API. [Related: Basic Security Measures with Nginx]

Observability and Realistic Error Management

In corporate systems, observability and error management are among the most critical parts of a project. It's almost impossible to keep a large system running without metrics, logs, traces, SLOs (Service Level Objectives), and error budgets. Finding out why a delayed shipment report was missing in a production ERP could take me days. This stemmed from the data flow passing through dozens of different services, each producing its own logs, and the necessity of consolidating and analyzing these logs in a central system. Even a WAL rotation alarm that dropped at 03:14 could affect the health of the entire system.

In side projects, this level of observability is often an unnecessary burden. When there's an error in my side project's backend, usually looking at a single log file or checking the journalctl output is sufficient. While debugging a chain reaction of errors between 3 microservices in a client project took 4 hours, I've had situations where I resolved the issue in 5 minutes with a simple try-except block and a print statement in my own side project. This demonstrates how useful journald rate limits or cgroup memory.high soft limits can be in side projects.

🔥 Victims of Excessive Observability

Trying to set up corporate-level observability tools (ELK Stack, Prometheus, Grafana) in side projects distracts from the project's main purpose. The installation, maintenance, and management of these tools require significant time and resources. While wrestling with Redis OOM eviction policy choices on my own VPS, I was reminded of how much information a simple top command provides me in real-time.

From my perspective, error management in side projects should be more pragmatic. If an error occurs infrequently and has a low impact—meaning a situation that happens once a day and doesn't significantly affect the user—it might be sufficient to occasionally check the logs rather than setting up a complex monitoring and alerting system. Last month, when I got an OOM-killed error in my side project by writing sleep 360, it only took me 10 minutes to see it immediately in journalctl and switch to a polling-wait mechanism. Such "it happens" errors are a natural part of side projects. In a corporate system, such an error would immediately mean paging and a critical alarm. Understanding this difference is critical for selecting the right tools in side projects. After all, deep security and monitoring mechanisms like file integrity monitoring with auditd or SELinux/AppArmor profiles are usually too detailed for side projects.

Time and Energy Management: The True Value of Side Projects

For me, side projects are not just about writing code; they also mean experimenting with new technologies, bringing different ideas to life, and contributing to my personal growth. The true value of these projects lies in the ability to iterate quickly, adopt experimental approaches, and deliver something in the shortest possible time. When we overcomplicate supply chain data flow management with a corporate lens, we are essentially disregarding these fundamental values.

In an AI-powered production planning project, I spent six months working on a complex data flow architecture and integrations. The resulting MVP was valuable, but the process was very long. On the other hand, I spent only two weeks adding a simple feature to my own Android spam application and received immediate user feedback. This rapid feedback loop not only boosted my motivation but also helped me more clearly define the project's direction. In side projects, instead of complex deployment strategies like feature flags or dark launches, simple blue-green or rolling deployments, or even manual rsync for deployment, can often suffice.

💡 Simple Solutions, Fast Results

In side projects, time and energy are the most valuable resources. Focusing on simple and direct solutions instead of complex data flow architectures reduces project completion time and keeps motivation high.

In my experience, adopting an "it happens" mentality in side projects has often led me to better results. Instead of designing a complex data flow architecture, I focused on using existing tools most efficiently. For example, in a scenario I encountered at a manufacturing firm's ERP, I had to examine the entire data flow from end to end to find the cause of delayed shipment reports. However, for a similar reporting need in my own side project, a simple SQL query and a web interface were sufficient. For me, this was not only a time saver but also provided mental relief. Ultimately, since these projects are often one-person orchestras, thinking about topics like CI/CD reliability, SLO, and error budget management at a corporate level increases the risk of burnout.

Conclusion: Context Matters in Side Projects

Overcomplicating supply chain data flow management in side projects can be a reflection of our corporate experiences. However, this often leads to unnecessary complexity and wasted time. Based on my own experiences, I can say this: there is a world of difference between the practice of managing the massive data flow in a production ERP and the need to manage the data flow in my own small-scale side project.

When the main goals of side projects are rapid learning, experimentation, and value creation, simplicity is always the best path. Direct SQL, simple APIs, and basic Linux tools are more than sufficient for most side projects. What's important is to understand the real needs of the project and produce solutions with the least complexity that meet those needs. This approach not only helps you complete your project faster but also keeps your personal motivation high.

My clear position is this: Side projects are an opportunity to question the habits brought by the corporate world and rediscover the power of simplicity. When starting my next side project, the first thing I'll do is ask myself, "Do I really need such a complex data flow?"