HomeExpertise04 · System integration

System integration

Middleware between systems that do not talk to each other: marketplace connections, stock and order data sync. This page shows what it fails on, how I built it, and what you can check it against.

Submitted once, arriving twice — with nobody retyping anything.Video · 21:9
Area
04 of 04
Stack
8 technologies
Reference
ERP integration
Field mapping on paper, two systems side by sideAI-generated
The agreement that precedes every integration and that nobody sees afterwards.21:9

The situation: two systems, two truths

Two systems, two truths. Stock in the shop doesn't match stock in the ERP, and the correction happens by hand — every morning, by someone who should be doing something else.

Spreadsheet, corrected by hand every morningAI-generated
An hour a day that nobody planned for and that appears in no quote.16:9
Same article, same minute, two numbers. One of them goes to the customer.Video · 21:9

Way of working

A mapping layer instead of direct coupling. Every discrepancy is logged, not overwritten: when two systems contradict each other, that's information and not an error you're allowed to write away.

EvidenceBefore and after2 exhibits

Photo: field mapping between two systems, printed and annotatedAI-generated

The reference project for this area

A capability with no project behind it is a list of technologies. This is the project — with the number it produced, and the case study where you can check it.

Fashion · ERP ↔ shop

0

manual imports since go-live

Three systems, one set of data — without handwork

Apparel Magic and the shop keep stock, prices and orders in sync; every discrepancy lands in the log instead of an inbox.

Python SQL / MariaDB REST-APIs Apparel Magic TB.One Shopify

The same case from inside: one sync runVideo · 16:9
Screenshot: the mapping table for two field names
The same case from outside: the field mapping16:9

All reference projects

What I worked on

Four kinds of work, each with the one picture that makes it checkable. Numbered so they can be pointed at — not because one follows another: each stands on its own.

  • 01

    Connecting ERP to a shop or marketplace

    Screenshot · The same item in ERP and shop, side by side
  • 02

    Middleware for mismatched data models

    AI image · Field mapping on paper, two data modelsAI-generated
  • 03

    Automated stock and order synchronization

  • 04

    Retiring manual import and export processes

    Screenshot · Import folder, empty — the job runs on its own

The stack: Shopify, JTL, REST APIs

After the situation and the way of working, nobody is still asking what is installed — they are asking whether any of it is real. The line stays; two frames below it answer.

Apparel Magic TradeByte / TB.One Shopify Shopware JTL MariaDB PostgreSQL REST-APIs

In use

Repeatable

Screenshot · the field mapping both systems agree on

Scale

A single connection took two to four weeks. The work almost never sat in the interface, it sat in the rules nobody had written down beforehand.

Small case
Connection · Two systemsAI-generated
After two to four weeks the two of them agree.16:9
Large case
The field that means something different on each side — negotiated, not read out of a schema.Video · 16:9

Common questions

Five questions that keep coming up about this area — answered from the projects they came up in.

Which systems have you connected before?
Apparel Magic, TradeByte/TB.One, Shopify, Shopware, JTL and a series of in-house ERP systems. A system nobody has heard of is not the exception here, it is the normal case.
What happens when shop and ERP report different stock?
The discrepancy is logged, not overwritten. A contradiction between two systems is information — writing it away loses exactly the trail you need at the end of the month.
Is the sync real-time or on an interval?
Both, depending on the field. Stock runs event-driven, master data on an interval. Wanting everything in real time costs more than it returns and turns any one fault into a fault everywhere.
How long did a single connection take?
Two to four weeks. The work almost never sat in the interface, it sat in the rules nobody had written down beforehand — rounding, special prices, returns.
What if the legacy system has no API?
Then the middleware takes what is there: database access, CSV export, SFTP, a scheduled report if it comes to that. Less elegant, but checkable — and the legacy system stays untouched.

Contact

Half a minute, and you know more.

Half a minute on what I work on and how. If a question is left, write to me — an answer within one working day.

Four areas

System integration rarely stands alone — in most projects this area reaches into at least one of the others. That is why the chain belongs together.

One change running through four layers: interface, API, database, cloud — with a timestamp at each stationAI-generated
One change, four layers — why the areas belong together21:9
An order list and its detail view side by side: ORD-4418 selected, net, VAT and total beside itAI-generated

01

Full-stack development

React and Next.js frontends with SSR and Server Components. Backends with TypeScript, Python and PostgreSQL.

A deployment log after the push: image, push, migrations, probe, routing — live after 1:44AI-generated

02

Cloud & Deployment

AWS architecture, deployment via Docker, clean secrets management. Or your own server, when that's the better math.

AI image · test questions on cards, answers beside themAI-generated

03

AI integration

RAG pipelines, embeddings and vector search with pgvector. Production-ready and live in use — not as a demo.

All four areas