Best Tools and Libraries for Hybrid Data Collection: Combining API and Web Scraping

2025-03-31

1. Introduction: The New Challenges of Data Collection

In the era of big data, acquiring comprehensive and accurate data is crucial for business analysis, market research, and AI model training. Relying on a single data source is often insufficient, making the combination of APIs and web scraping a powerful data collection strategy.

API vs. Web Scraping: How to Choose?

· API: Provided by official sources, stable data, structured format (JSON/XML), but may have limitations (e.g., access frequency, data coverage).
· Web Scraping: Can retrieve additional information unavailable via APIs, such as user reviews and dynamic webpage content.

The Best Approach? Hybrid Data Collection!

This article will introduce how to efficiently integrate APIs and web scraping, recommend the best tools and libraries, and help you streamline your data collection process.

2. What is "Hybrid Data Collection"?

Hybrid data collection refers to the combined use of APIs and web scraping to gather information.

Why Combine Both?

· Advantages of APIs:

Reliable and structured data provided by official sources (JSON, XML).
More efficient, avoiding IP bans and complying with platform rules.

· Advantages of Web Scraping:

Can extract additional information not covered by APIs, such as user comments and real-time webpage data.
Suitable for handling JavaScript-rendered content.

Challenges

· Data integration issues: APIs and web scraping provide different data formats that need processing.
· Technical complexity: Requires knowledge of API requests, web page parsing, and data structuring.

3. Tools and Libraries for API Data Collection

① Using Luckdata API to Collect Walmart Data

· Functionality: Luckdata provides an Walmart API for directly retrieving Walmart product data.
· Advantages:

Eliminates the need for complex web parsing, returning structured data directly.
Efficient and stable, reducing the risk of IP bans.

Example Code (Python):

import requests
headers = {
'X-Luckdata-Api-Key': 'your luckdata key'
}
response = requests.get(
'https://luckdata.io/api/walmart-API/get_vwzq?url=https://www.walmart.com/ip/NELEUS-Mens-Dry-Fit-Mesh-Athletic-Shirts-3-Pack-Black-Gray-Olive-Green-US-Size-M/439625664?classType=VARIANT',
headers=headers,
)
print(response.json())

Example Code (Java):

import java.io.IOException;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("https://luckdata.io/api/walmart-API/get_vwzq?url=https://www.walmart.com/ip/NELEUS-Mens-Dry-Fit-Mesh-Athletic-Shirts-3-Pack-Black-Gray-Olive-Green-US-Size-M/439625664?classType=VARIANT"))
.GET()
.setHeader("X-Luckdata-Api-Key", "your luckdata key")
.build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

② requests (Standard HTTP Request Library for Python)

· Use Case: Used to access APIs and retrieve JSON data.
· Advantages:

Simple syntax and easy to use.
Supports authentication, session management, and custom headers.

import requests
headers = {
"X-Luckdata-Api-Key": "your luckdata key"
}
response = requests.get("https://luckdata.io/api/walmart-API/get_vwzq?url=https://www.example.com", headers=headers)
print(response.json())

③ httpx (Supports Asynchronous API Requests)

· Use Case: High-concurrency API requests, improving data scraping speed.
· Advantages:

Supports HTTP/2 for better efficiency.
Suitable for large-scale asynchronous data collection.

4. Tools and Libraries for Web Scraping

① BeautifulSoup: Lightweight Web Page Parsing Tool

· Use Case: Extracts data from static web pages.
· Advantages:

Simple and easy to learn.

from bs4 import BeautifulSoup
import requests
url = "https://www.example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
for title in soup.find_all("h1"):
print(title.text)

② Scrapy: Powerful Web Scraping Framework

· Use Case: Large-scale data scraping, supporting concurrency and pipelines.
· Advantages:

Ideal for efficiently scraping e-commerce and social media data.

import scrapy
class ProductSpider(scrapy.Spider):
name = "products"
start_urls = ["https://www.example.com/products"]
def parse(self, response):
for product in response.css(".product"):
yield {
"name": product.css(".title::text").get(),
"price": product.css(".price::text").get()
}

③ Selenium: Automating Browsers for JavaScript-Rendered Pages

· Use Case: Needed for interacting with web pages and handling JavaScript-rendered content.
· Advantages:

Suitable for scraping dynamic pages such as e-commerce product details and social media posts.

from selenium import webdriver
driver = webdriver.Chrome()
driver.get("https://www.example.com")
print(driver.page_source)
driver.quit()

5. Combining API and Web Scraping

① Scrapy + requests (Hybrid API + Web Scraping Integration)

· Method: Use API to collect structured data, then use Scrapy to extract additional webpage content.
· Advantages:

Combines the efficiency of APIs with the flexibility of web scraping.

import requests
from scrapy import Spider
class HybridSpider(Spider):
name = "hybrid"
def start_requests(self):
headers = {"X-Luckdata-Api-Key": "your luckdata key"}
api_data = requests.get("https://luckdata.io/api/walmart-API/get_vwzq?url=https://www.example.com", headers=headers).json()
for item in api_data:
yield scrapy.Request(url=f"https://www.example.com/{item['id']}", callback=self.parse)
def parse(self, response):
yield {"title": response.css("h1::text").get()}

6. Recommended Tool Selection

Use Case	Recommended Tools	Best For
Small-Scale Projects	`requests + BeautifulSoup + Pandas`	Quick prototyping
Large-Scale Projects	`Scrapy + httpx + Celery`	High-concurrency, large-volume data collection
Dynamic Web Pages	`Selenium + requests`	Handling JavaScript-rendered content

7. Conclusion

Combining APIs and web scraping allows for more efficient and comprehensive data collection.
· APIs provide structured data, such as Luckdata Walmart API.
· Web scraping supplements additional information, such as user reviews and dynamically generated content.
· Using Pandas for data integration and Celery for task management further enhances efficiency.

By selecting the right tools for your needs, you can significantly improve the effectiveness of your data collection process!