To meet our client’s requirements and goals, we used Python and Django for the backend, which is responsible for processes like Big Data mining and processing. The platform has multiple integrations with external services like ML-driven aggregator (Visenze), payment (Stripe), purchase tax calculation (TaxJar), and many others. We implemented delayed loading to optimize platform performance, and continuous integration to ensure users always get an up to date range of items. Additionally, we solved the problem with size unification with regular equations, so the platform’s backend makes sure that users don’t have a negative experience with brand size differences. The system is based on cloud solutions and micro-service architecture. After we created a separate microservice for marketing processes, features that were impossible to realize are now easy to implement.
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