How to design a web scraper for amazon that updates your shopify store for price fluctuations in real time?

1. High-Level Overview

Our application consists of the following main modules:

• Data Ingestion Module

  • Responsibility: Poll Amazon for product details (price, image, description, stock, delivery date) via the Oxylabs API.
  • Process: Extracts and normalizes data, then writes it into the database along with timestamps.
  • Scheduling: Can be implemented using a scheduler (cron jobs or a task queue like Celery/RabbitMQ).

• Database

  • Responsibility: Persist raw product data and later store the modified price (with markup) along with timestamps.
  • Suggestions: PostgreSQL or MongoDB depending on our query and scalability requirements.

• Backend API Server

  • Responsibility:
    • Serves as the middleware between the frontend and the backend services.
    • Provides REST (or GraphQL) endpoints for fetching product data.
    • Processes markup calculations, saves updates, and triggers Shopify updates.
    • Pushes real‑time updates (via WebSockets) to the Admin Dashboard.
  • Tech Options: Node.js with Express, Python (FastAPI or Django), or any other modern backend framework.

• Real-Time Communication Server

  • Responsibility:
    • Delivers live updates to the Admin Dashboard (e.g., using WebSockets with Socket.io).
  • Integration: Could be integrated within our Backend API Server or as a dedicated microservice.

• Admin Dashboard (Frontend)

  • Responsibility:
    • Display real‑time product data.
    • Allow the admin to apply a percentage markup.
    • Provide a control (button) for each product to trigger the update on Shopify.
  • Tech Options: Modern JavaScript frameworks such as React, Angular, or Vue.js.
  • Real-Time: Use WebSocket clients (e.g., Socket.io client) to receive live updates.

• Shopify Integration Module

  • Responsibility:
    • When an admin approves a price update, this module calls the Shopify API to update the product’s price in real time.
  • Security: Ensure that the API credentials and tokens for Shopify are securely stored and managed.

• Monitoring & Logging

  • Responsibility:
    • Monitor data ingestion jobs, API performance, error handling, and system alerts.
  • Tools: Prometheus, Grafana, and the ELK stack (Elasticsearch, Logstash, Kibana).

2. Data Flow & Component Interaction

1. Data Ingestion:

   • A scheduled job (or continuously running service) uses the Oxylabs API to fetch real‑time product data from Amazon.
   • The Data Ingestion Module processes and stores the data in the Database with the current timestamp.

2. Real-Time Dashboard:

   • The Backend API Server queries the Database and pushes live updates to the Admin Dashboard via WebSockets.
   • The dashboard displays the current price along with product details.

3. Price Markup & Update:

   • An admin enters a markup percentage on the dashboard.
   • The Backend API calculates the new price and stores it (with a timestamp) in the Database.
   • The dashboard immediately reflects the modified price.

4. Shopify Update:

   • Upon clicking the “Update Shopify” button, the Backend API triggers the Shopify Integration Module.
   • The module calls the Shopify API to update the product’s price in our Shopify store.
   • A confirmation is received and the database is updated accordingly.

5. Feedback Loop:

   • Any successful updates (or errors) are fed back into the system and displayed on the dashboard for transparency.

3. Suggested Tools & Tech Stack

4. Additional Considerations & Requirements

• Authentication & Security:

  • Use API keys and tokens for secure communication with Oxylabs and Shopify.
  • Implement admin authentication (e.g., using JWT or OAuth) on the dashboard and API endpoints.

• Error Handling & Retry Logic:

  • Implement robust error handling and retry mechanisms in the Data Ingestion and Shopify Integration modules.
  • Log errors for later analysis and use monitoring tools to alert on failures.

• Scalability & Performance:

  • Consider caching frequently accessed data.
  • Use load balancing for your backend services if the number of products or request rate is high.
  • For very high-frequency updates, a message queue (e.g., RabbitMQ or Kafka) can help decouple and process tasks asynchronously.

• Deployment & CI/CD:

  • Containerize your services using Docker.
  • Use CI/CD pipelines (e.g., GitHub Actions, Jenkins) for automated testing and deployment.

• Data Retention & Analytics:

  • Optionally, store historical data to analyze price trends over time.
  • Provide admin reports and dashboards for analytics.

. Outro

This design outlines a modular and scalable approach:

• Data Ingestion gathers real‑time product data.
• Backend API processes data, calculates markups, and communicates with both the Admin Dashboard and Shopify.
• Real-Time Communication ensures the dashboard stays updated as Amazon prices fluctuate.
• Shopify Integration lets admins push approved price changes to their online store.