The Learning Approach

You know how they say the best way to learn something is by building it? Well, I decided to put that to the test with some advanced backend concepts that I'd been wanting to dive deeper into. Instead of building yet another todo app or blog, I thought - why not try to implement some core features that power automation platforms like n8n and Zapier?

This isn't about building the next unicorn startup or competing with billion-dollar companies. It's about getting my hands dirty with some really interesting backend engineering challenges while building something that actually does cool stuff.

Why Automation Features Make Great Learning Projects

I've been fascinated by how platforms like Zapier and n8n work under the hood. Think about it - they're dealing with:

• Distributed systems (handling thousands of workflows simultaneously)
• Queue management (processing jobs in the background)
• Webhook handling (receiving and processing HTTP requests from anywhere)
• Database design (storing complex workflow configurations)
• API integrations (talking to dozens of different services)
• Background processing (running tasks without blocking users)

These are exactly the kind of advanced backend concepts I wanted to learn more about. Plus, building automation features gives you immediate feedback - you can literally watch your webhooks get triggered and see workflows execute in real-time.

What I've Built So Far (And What I Learned)

Authentication & JWT Management

What I built: Standard user registration and login with JWT tokens

What I learned: This was my warm-up. Getting comfortable with JWT signing, verification, middleware patterns, and secure password handling. Nothing groundbreaking, but good fundamentals practice.

Tech stack: Express.js middleware, bcrypt for password hashing, jsonwebtoken for token management

Workflow CRUD with Complex Data Modeling

What I built: Users can create, read, update, and delete workflow configurations

What I learned: This got interesting fast. Workflows aren't just simple records - they're complex graphs with nodes, connections, and execution logic. I had to think about:

• How to store flexible node configurations in a relational database
• Managing relationships between workflows, nodes, and executions
• Handling workflow versioning (what happens when someone updates a running workflow?)

Tech stack: PostgreSQL with Prisma ORM, learning about proper database schema design for graph-like data

Background Job Processing (The Fun Part!)

What I built: BullMQ + Redis setup for processing workflows asynchronously

What I learned: This is where things got really exciting. Instead of trying to execute everything synchronously, I learned how to:

• Set up Redis as a message broker
• Design job payloads that contain everything needed for execution
• Handle job failures and retries gracefully
• Monitor queue health and performance

The "aha" moment: Realizing that most complex backend systems are really just sophisticated job queues with nice APIs on top.

Tech stack: BullMQ for job management, Redis for queue storage, learning about distributed task processing

Webhook System (The Coolest Part!)

What I built: Dynamic webhook creation - users can generate unique URLs that trigger their workflows

What I learned: This blew my mind to implement. I built a system where:

• Each workflow can have multiple webhook endpoints
• Incoming HTTP requests get captured, validated, and queued for processing
• The webhook URLs are completely dynamic (generated per workflow)
• Response handling (what do you send back to the webhook caller?)

The technical challenge: How do you route dynamic URLs efficiently? How do you validate incoming data? How do you handle webhook security?

Tech stack: Express.js dynamic routing, UUID generation, request validation with Zod

Node-Based Execution Engine

What I built: Support for different node types (TRIGGER, ACTION, CONDITION) that can be chained together

What I learned: This was like building a mini programming language interpreter. Each node type needs:

• Different execution logic
• Input/output data handling
• Error management
• Context passing between nodes

The complexity: Making nodes generic enough to be flexible but specific enough to actually do useful work.

The Learning Challenges I Didn't Expect

What I'm Learning Next

Scheduled Job Processing (Distributed Cron)

I want to build a system where users can schedule workflows to run at specific times. Sounds simple, but implementing distributed scheduling is surprisingly complex:

• How do you handle server restarts?
• What about daylight saving time?
• How do you prevent duplicate executions across multiple servers?

Email Integration Deep Dive

Planning to implement email sending and receiving. This involves:

• SMTP client configuration and connection pooling
• Email template engines and personalization
• Parsing incoming emails (MIME, attachments, etc.)
• Managing bounce handling and deliverability

API Integration Patterns

I want to build connectors for popular services like Slack and GitHub. This means learning about:

• OAuth 2.0 flows and token management
• Rate limiting and backoff strategies
• Webhook verification (each service does it differently)
• API versioning and compatibility

Why This Approach Works for Learning

Instead of reading about these concepts in isolation, I'm learning them in context by building something that actually works. Each feature forces me to understand not just the "what" but the "why" behind different architectural decisions.

When I implemented webhooks, I didn't just learn the Express.js API - I learned why webhook systems exist, how they handle security, and what happens when they scale.

When I built the queue system, I didn't just learn BullMQ - I learned about the fundamental problems that job queues solve and why every major backend system needs them.

The Real Goal

I'm not trying to build the next Zapier. I'm trying to understand how complex backend systems actually work by implementing simplified versions of their core features. Each piece teaches me something new about distributed systems, API design, data modeling, or infrastructure management.

Plus, at the end of this, I'll have built something genuinely useful that I can actually use for my own automation needs. Even if it's just 10% as powerful as the real platforms, it's 100% mine and I understand every line of code.

Next up: Diving into distributed cron systems and the surprising complexity of "just run this job every hour." Should be fun!