E-commerce Full-Link Monitoring Platform: Building a Scalable System with Microservices, API Integration, and Dashboard Visualization

✅ Background: Beyond Data Collection – Toward Structured and Intelligent Monitoring

In the rapidly evolving landscape of social and e-commerce integration, data analytics teams face several critical challenges:

• Fragmented data: Multiple APIs, platforms, and field formats make manual aggregation inefficient and error-prone.

• No unified access: Business teams seeking inventory, pricing, or popularity metrics often rely on manual exports by data teams.

• No alert mechanism: When a product goes viral, sells out, or price wars begin, no one is immediately notified.

This article presents a hands-on approach using LuckData API + Kafka + MongoDB + Streamlit to build a full-link e-commerce monitoring platform supporting multi-platform data integration, real-time alerts, and multidimensional visualization—enabling smarter and faster business decisions.

✅ System Architecture Design

System Overview Diagram:

            ┌─────────────┐
│ LuckData API│ (Supports TikTok / Lazada / Amazon / Douyin)
└─────┬───────┘
│
┌─────▼─────┐       ┌────────────┐
│ Data Ingest │────▶│ Kafka Topic│ (Streaming log & metrics)
└─────┬─────┘       └────┬───────┘
│                    │
┌────────▼─────┐      ┌──────▼─────────┐
│ MongoDB Store │◀─────│ Data Processor │ (Cleaning / Aggregation)
└────────┬─────┘      └──────┬─────────┘
│                    │
┌────────▼──────────────┐     ▼
│ Streamlit / Grafana UI│──▶ Alert Services (DingTalk / Email / WeChat)
└───────────────────────┘

This architecture ensures modular, scalable, and real-time processing capabilities from source ingestion to alerting and visualization.

✅ Data Source Integration

Example: Fetching TikTok Video Data (via LuckData API)

import requests
def fetch_tiktok_videos(user_id):
url = "https://luckdata.io/api/tiktok-api/get_user_posts"
params = {"userid": user_id}
headers = {"apikey": "YOUR_API_KEY"}
res = requests.get(url, headers=headers, params=params)
return res.json().get("data", [])

This structure can be easily adapted to Lazada product search, Amazon price listings, Pinduoduo sales data, and more. Building a standardized API wrapper helps unify cross-platform data ingestion.

✅ Data Ingestion and Kafka Pipeline

Kafka enables efficient data streaming and decoupled processing across services.

from confluent_kafka import Producer
import json
p = Producer({"bootstrap.servers": "localhost:9092"})
def push_to_kafka(topic, data):
p.produce(topic, json.dumps(data).encode("utf-8"))
p.flush()

Producers push API-fetched data into Kafka Topics. Consumers asynchronously read, transform, and write structured data into MongoDB—applying standardization, SKU mapping, and deduplication logic as needed.

MongoDB provides flexibility for handling heterogeneous and nested data structures across platforms.

✅ Dashboard Design Recommendations (Streamlit Example)

Streamlit offers rapid development for real-time dashboards. Below is an example comparing prices across Lazada regions:

import streamlit as st
import pandas as pd
from pymongo import MongoClient
client = MongoClient("mongodb://localhost:27017")
db = client["ecommerce"]
collection = db["lazada_prices"]
df = pd.DataFrame(list(collection.find()))
st.title("Lazada Cross-Country Price Comparison")
st.bar_chart(df.groupby("site")["price"].mean())

Additional recommended components:

• Inventory Trends: Visualize restock frequency and stockout risks.

• Video Heatmaps: Monitor performance of marketing content.

• Anomaly Flags: Highlight SKUs with stock or price issues.

• Top-Performing Content: Leaderboards for products and creators based on interaction metrics.

Grafana can also be used for metrics monitoring, supporting real-time updates and customizable alerting.

✅ Smart Alerting Mechanism

The alerting module enables rules-based notifications via DingTalk, WeChat, or email to relevant teams (operations, inventory, support).

Sample configurable conditions:

• Inventory < 5 and Heat > 80: Triggers high-risk stockout alert.

• Price deviation > ±10% over past 3 days: Detects price volatility.

• Sudden surge in content interactions: Indicates viral potential, alerting marketing teams.

Sample implementation:

def check_inventory_warning(product):
if product["stock"] < 5 and product["heat"] > 80:
send_alert(f"{product['title']} is at risk of selling out!")

With anomaly detection modules, thresholds and conditions can be dynamically adjusted for improved precision.

✅ Extensibility Design

The system is modular and extensible—allowing for the integration of A/B testing, recommendation engines, automated restocking, and price optimization logic.

✅ Conclusion

Why waste time manually comparing prices and stocks in Excel when you can build your own intelligent e-commerce radar system?

This lightweight yet scalable platform is ideal for:

✅ Multi-platform price comparison
✅ Visualizing trends in video performance and product engagement
✅ Real-time stockout/price-drop alerts
✅ Automated assistance for product selection and inventory strategy

With this solution, data teams can significantly enhance operational efficiency and enable intelligent, automated decision-making.

Articles related to APIs :

• Global Price Intelligence Across Four Major E-commerce Platforms: Real-Time Analysis of Amazon, Walmart, Lazada, and Pinduoduo

• Social Influence Evaluation: Building Influencer Rankings and Commerce Scores via TikTok & Douyin API

• Douyin/Kuaishou E-commerce Product Selection Strategy: Leveraging Social Popularity and Sales Data

• Precise ROI Analysis for Short Video E-commerce: Building a Data-Driven Model with Douyin API and E-commerce Sales Data

• Cross-Platform User Analysis: A Practical Guide to Building Precise Personas with TikTok, Douyin, and Lazada Data

• Behind Viral Short Videos: How to Predict TikTok Trends Using API