Data Integration - Working with External APIs

This week was my first real introduction to integrating external data into a backend system, and honestly, it was a bit of a mindset shift.

Up until this point, everything I had built was self-contained:

• my own database
• my own entities
• my own logic

But now, suddenly, my application had to rely on someone else’s system.

From isolated systems to connected systems

The biggest realization for me this week was:

A backend is not just something that serves data, it also consumes data.

Instead of only working with my own PostgreSQL database, I now had to:

• send HTTP requests
• receive JSON responses
• map external data into my own system

This made the backend feel much more “real-world”.

Using the RandomUser API

To get hands-on experience, I integrated the RandomUser API, which generates fake user data.

Example request:

https://randomuser.me/api/

This returns a JSON response with user information like:

• name
• email
• dob
• password hashes or plain text passwords

Instead of manually creating test users, I could now fetch realistic data automatically.

Mapping external data into my system

One of the first design decisions I had to make was:

Should I use the API’s data structure directly?

I chose not to.

Instead, I created DTOs to match the API response, and then mapped that data into my own domain model.

For example:

• API > RandomUserDTO
• Internal > User entity

This separation was important because:

• I don’t control the external API
• the API structure could change
• my domain model should remain stable

Connecting it to my project

Even though I restarted my current membersystem project later, I initially used this integration in an earlier version of the system.

The idea was to:

• quickly generate users for testing
• simulate realistic data
• avoid manually inserting test records

This was especially useful when testing relationships like:

• users belonging to companies
• memberships and roles

Even though I didn’t carry this feature into the final version, it gave me a much better understanding of how external data can support development and testing.

Handling JSON in Java

Working with JSON responses was a new challenge.

I had to:

• understand nested JSON structures
• create matching DTO classes
• deserialize responses using Jackson

This made it clear how important structure is when working with external data.

Small mismatches in field names or structure could easily break everything.

Error handling and uncertainty

One thing that stood out immediately was how unreliable external systems can be.

Compared to my own database, external APIs can:

• be slow
• return unexpected data
• go down completely

This forced me to think about:

• null checks
• fallback behavior
• defensive programming

A key takeaway here was:

Never assume external data is correct or even available.

Design decisions

To keep things clean, I made sure to:

• isolate API calls in a dedicated service layer
• avoid mixing HTTP logic into controllers
• keep the rest of the system unaware of where the data came from

This separation made the system easier to understand and extend.

Challenges

Some of the challenges I ran into:

• understanding the structure of real API responses
• mapping nested JSON correctly
• dealing with missing or null values
• debugging HTTP calls when things didn’t work

It was definitely more “messy” than working with my own controlled data.

Key takeaways

This week taught me that:

• backend systems often depend on external data sources
• DTOs are essential when working with APIs
• separation between external and internal models is critical
• error handling becomes much more important

Even though this was a relatively small feature, it changed how I think about backend systems.

They are not just isolated applications, they are part of a larger ecosystem.