Building a Unified API Data Layer: A Standardized Solution for Cross-Platform Integration

2025-05-20

In the era of multi-platform e-commerce data integration, businesses often face heterogeneous APIs from platforms like JD.com, Taobao, and Pinduoduo. These APIs differ greatly in structure, field naming conventions, and response formats, forcing developers to write separate compatibility logic for each one. To improve system consistency and development efficiency, it is crucial to build a unified API data layer that transforms messy and inconsistent data into standardized, structured formats. This article walks through how to design a middle-layer model and encapsulate conversion logic to enable seamless cross-platform support.

1. Why Is API Data Standardization Important?

When integrating APIs from different platforms (such as JD Official, LuckData, Taobao, etc.), developers often encounter the following issues:

Problem Scenario	Example
Inconsistent field names	`skuId`, `sku_id`, `productId`—difficult to distinguish
Mismatched data types	Some platforms return strings, others return numbers
Missing/inconsistent fields	Arrays on one platform, nested objects on another
Different response formats	One platform returns `data.result.list`, another uses `items[]`

These issues lead to:

Complex front-end logic that adapts to different APIs
Bloated backend code filled with conditional checks
High cost of switching third-party providers and tight coupling

To solve this, it's recommended to create a unified data model at the backend, enabling all platform responses to be normalized into human-readable, developer-friendly structures.

2. Comparing Response Structures from Different Platforms

Let’s compare the response structures of the “JD Product Search” API from JD Official and LuckData:

JD Official:

{
"jingdong_union_open_goods_jingfen_query_responce": {
"code": "0",
"result": "{\"data\":{\"goodsList\":[{...}]}}"
}
}

LuckData:

{
"code": 200,
"message": "success",
"data": {
"items": [
{
"sku_id": "100045",
"title": "Lenovo Legion",
"price": 6599
}
]
}
}

Observations:

JD Official’s response is deeply nested, with JSON wrapped inside a string
LuckData follows a flatter, RESTful style with more intuitive field names

This makes LuckData much easier to work with and more suitable for standardized integration.

3. Designing a Unified Data Model: UnifiedGoodsModel

To reduce client-side coupling and enable cross-platform reuse, define a universal product model such as:

{
"sku_id": "string",
"title": "string",
"price": "number",
"shop_name": "string",
"sales": "number",
"source": "jingdong|taobao|pinduoduo|luckdata"
}

This model includes:

Consistent naming conventions
Key product attributes used across platforms
Traceable source field to identify the data origin

With this model, your frontend and business logic can consume data in a stable and uniform way, regardless of the underlying source.

4. Implementation: Backend Data Transformation

On the server side, create platform-specific transformers to convert raw API responses into the unified model. For example:

def transform_jd_official(data):
parsed = json.loads(data['jingdong_union_open_goods_jingfen_query_responce']['result'])
return [
{
"sku_id": item['skuId'],
"title": item['skuName'],
"price": float(item['priceInfo']['price']),
"shop_name": item.get('shopInfo', {}).get('shopName', ''),
"sales": item.get('inOrderCount30Days', 0),
"source": "jingdong"
} for item in parsed['data']['goodsList']
]
def transform_luckdata(data):
return [
{
"sku_id": item['sku_id'],
"title": item['title'],
"price": item['price'],
"shop_name": item.get('shop_name', ''),
"sales": item.get('sales', 0),
"source": "luckdata"
} for item in data['data']['items']
]

Then route the transformation dynamically based on the data source:

def standardize_response(raw_response, source_type):
if source_type == 'jd_official':
return transform_jd_official(raw_response)
elif source_type == 'luckdata':
return transform_luckdata(raw_response)
else:
raise ValueError("Unsupported source type")

No matter how complex or different the raw API formats are, the frontend will always receive a standardized model.

5. Unified Response Wrapper Format

To ensure consistency for frontend integration, the server should return a uniform response format:

{
"code": 0,
"message": "OK",
"data": {
"items": [ ...UnifiedGoodsModel... ],
"total": 120,
"source": "jingdong"
}
}

Benefits:

Unified status codes and messages
Clear data node (items), total count, and source field
Abstracts away the complexity of upstream platforms

6. Why LuckData Has an Advantage in Adaptation

LuckData offers several advantages for data normalization:

Unified field schema: Standardized field names out-of-the-box
Flat structure: Minimal nesting makes parsing simple
Complete data: Fields like sales volume are estimated if not available
Cross-platform aggregation: Supports JD, Taobao, Pinduoduo through a single interface

✅ If you're building a unified product search API, LuckData is an ideal backend provider to streamline development.

7. Building Multi-Platform Support

As your business grows, you may need to integrate:

JD Official API
Taobao Open Platform
Pinduoduo Open Platform
LuckData’s aggregated API

You can modularize each platform’s transformation logic:

# transform/taobao.py # transform/pinduoduo.py # transform/luckdata.py # transform/jd_official.py

Then create a central dispatch function:

def transform_dispatch(platform: str, response_data):
mapping = {
'jd_official': transform_jd_official,
'luckdata': transform_luckdata,
'taobao': transform_taobao,
'pinduoduo': transform_pdd
}
if platform not in mapping:
raise ValueError("Unsupported platform")
return mapping[platform](response_data)

This makes it easy to scale, extend, and maintain integration across platforms.

8. Conclusion

Standardizing data structures is one of the most critical steps in multi-platform API integration. By establishing a unified data model like UnifiedGoodsModel and wrapping platform-specific logic, you can:

Decouple the frontend from external APIs
Quickly onboard or switch third-party providers like LuckData
Improve development efficiency, system stability, and maintainability

LuckData’s built-in structural design makes it a powerful foundation for constructing a reliable and standardized API data layer.

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