How to Manage Scraped Data in a Simple SSR App

1️⃣ Choose a Data Format for Storage

• JSON – For APIs or NoSQL databases.
• CSV – For tabular data.
• SQL – For relational data.
• NoSQL – For flexible data structures.

2️⃣ Recommended Data Structure

📁 JSON / NoSQL (MongoDB, Firebase)

{
  "products": [
    {
      "id": "prd_001",
      "name": "Smartphone XYZ",
      "category": "Electronics",
      "price": 199.99,
      "supplier": {
        "name": "Shenzhen Tech Co.",
        "contact": "supplier@example.com"
      },
      "stock": 50,
      "rating": 4.5,
      "importDate": "2025-03-01T10:00:00Z"
    }
  ]
}

📊 Relational Database (SQL - MySQL, PostgreSQL)

CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    category VARCHAR(100),
    price DECIMAL(10,2),
    supplier_id INT,
    stock INT,
    rating FLOAT,
    import_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (supplier_id) REFERENCES suppliers(id)
);

CREATE TABLE suppliers (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    contact VARCHAR(255)
);

3️⃣ Handling Data in the Backend

• Fetching Data → Use an API to pull new data periodically.
• Cleaning & Normalizing → Ensure data consistency.
• Storing Efficiently → SQL for structured, NoSQL for flexible storage.
• Serving via SSR → Fetch from DB and render on Next.js/Nuxt.js/Express.js.

4️⃣ Data Processing Flow

1. Scrape & Fetch → Data is fetched from the source.
2. Validate & Clean → Remove duplicates, incorrect values.
3. Store → Insert into SQL/NoSQL databases.
4. Serve → API endpoints for SSR apps.
5. Update & Archive → Older data can be archived in CSV/JSON.

5️⃣ Best Practices

• Use cron jobs to schedule updates.
• Normalize data in SQL databases for efficiency.
• Use Redis/Memcached for caching.
• Store backups in CSV/JSON format.

Which One Should You Use?

• Use SQL if data has strong relationships.
• Use NoSQL if data is flexible.
• Use JSON/CSV for backups and exporting reports.

Let me know if you need a sample implementation! 🚀