One-Week Build: How a Zero-Tech Team Can Quickly Launch an "E-commerce + Social Media" Data Platform

High technical barriers, limited manpower, and fragmented data are common challenges when building a data platform. This article provides a “Minimum Viable Data Platform (MVP)” solution that even non-technical teams can implement to launch a real-time monitoring system across e-commerce and social media platforms within one week.

Core Objectives

• Lightweight data platform architecture designed for small teams without backend engineers

• Integrate product and social data from Douyin/TikTok, Pinduoduo, and Lazada

• Fast deployment: no backend or only basic use of Google Apps Script / Python

1. MVP Architecture Design: Simplest Viable System

This architecture combines existing tools into a complete data platform:

2. Step-by-Step: Fetching Data into Spreadsheets

Below is a sample of collecting Douyin trending videos and Lazada product prices into Google Sheets.

✅ Example: Fetch Douyin Trending Videos into Google Sheets

function fetchDouyinRankings() {
var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName("Douyin");
var url = "https://luckdata.io/api/douyin-API/get_xv5p?city=110000&type=rise_heat&end_date=20241224&page_size=10&start_date=20241223";
var response = UrlFetchApp.fetch(url);
var data = JSON.parse(response.getContentText());
var videos = data.data;
sheet.clearContents();
sheet.appendRow(["Video Title", "Likes", "Author", "Publish Time"]);
for (var i = 0; i < videos.length; i++) {
sheet.appendRow([
videos[i].title,
videos[i].like_count,
videos[i].author_name,
videos[i].create_time
]);
}
}

Use Google Apps Script’s trigger function to schedule automatic daily updates.

✅ Example: Fetch Lazada Product Data into Spreadsheet

function fetchLazadaProducts() {
var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName("Lazada");
var url = "https://luckdata.io/api/lazada-online-api/gvqvkzpb7xzb?page=1&site=vn&query=airfryer";
var response = UrlFetchApp.fetch(url);
var data = JSON.parse(response.getContentText());
var products = data.data;
sheet.clearContents();
sheet.appendRow(["Product Title", "Price", "Link"]);
for (var i = 0; i < products.length; i++) {
sheet.appendRow([
products[i].title,
products[i].price,
products[i].url
]);
}
}

3. Real-Time Dashboard Building (Optional Tools)

Option 1: Google Data Studio

• Data Source: Google Sheets

• Visualizations include:

  • Product price trend charts

  • Like count trends for videos

  • Cross-platform comparisons

• Benefits: No-code, easy collaboration, quick to launch

Option 2: Rapid Prototype with Streamlit (Python)

import streamlit as st
import pandas as pd
df = pd.read_csv("douyin_data.csv")
st.title("Douyin Trending Dashboard")
st.dataframe(df)

Easily build a frontend for your data and host locally or online.

4. Set Up Alerts: Push Notifications via Feishu/Slack

For example, price alerts that compare today's and yesterday’s data:

function priceChangeAlert() {
var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName("Lazada");
var rows = sheet.getDataRange().getValues();
for (var i = 1; i < rows.length; i++) {
var priceToday = parseFloat(rows[i][1]);
var priceYesterday = parseFloat(rows[i][2]);
if (Math.abs(priceToday - priceYesterday) / priceYesterday > 0.2) {
sendFeishu("Price Alert: " + rows[i][0] + " has changed by more than 20%");
}
}
}

You can also use Slack or email APIs to notify team members of anomalies.

5. Suggested Project Folder Structure

/project/
├── douyin_fetch.gs          # Fetch trending videos
├── lazada_fetch.gs          # Fetch product search results
├── alert_logic.gs           # Price alert logic
├── dashboard.gsheet         # Visualization spreadsheet
└── README.md

Clear modular design makes future maintenance and expansion easier.

✅ One-Week Implementation Timeline

Conclusion

This architecture requires no servers or databases, and no full-time engineers. With just spreadsheets and lightweight scripts, any team can begin building their own “e-commerce + social media” data platform. Perfect for startups, product selection teams, and marketing departments aiming for data-driven decisions.

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