Nordstrom Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Use AlterLab's Extract API to POST a URL and a JSON schema, receiving validated, typed JSON output for Nordstrom product pages. No HTML parsing, no brittle selectors — just structured data ready for downstream pipelines.

Why use Nordstrom data?

Nordstrom's product catalog offers rich, structured e‑commerce information that fuels several engineering workflows:

• Training AI models: Price, title, and availability data improve recommendation and demand‑forecasting models.
• Competitive intelligence: Monitor assortment changes, pricing strategies, and new arrivals across categories.
• Affiliate and content platforms: Enrich product feeds with accurate, up‑to‑date metadata for blogs, newsletters, or price‑comparison sites.

What data can you extract?

From any publicly accessible Nordstrom product page you can pull:

• title – product name as displayed
• price – current sale price (string to preserve formatting)
• currency – ISO currency code (e.g., "USD")
• sku – retailer‑specific stock keeping unit
• availability – "in_stock", "out_of_stock", or "preorder"
• rating – average star rating (string, e.g., "4.5")
• image_url – primary product image All fields are defined in a JSON schema; AlterLab validates and casts the output accordingly, guaranteeing typed JSON without post‑processing.

The extraction approach

Building a scraper with raw HTTP requests and HTML parsers (BeautifulSoup, Cheerio, etc.) quickly becomes fragile:

• Frequent DOM changes break CSS selectors.
• JavaScript‑rendered content requires headless browsers, adding latency and maintenance.
• Anti‑bot measures (CAPTCHAs, rate limits) demand proxy rotation and fingerprint evasion. AlterLab's data API abstracts these challenges. The platform handles request routing, automatic retries, JavaScript rendering, and proxy rotation, returning only the data you asked for in the shape you defined.

Quick start with AlterLab Extract API

First, install the AlterLab Python client (or use cURL directly). See the Getting started guide for installation details.

Python example

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "The title field"
    },
    "price": {
      "type": "string",
      "description": "The price field"
    },
    "currency": {
      "type": "string",
      "description": "The currency field"
    },
    "sku": {
      "type": "string",
      "description": "The sku field"
    },
    "availability": {
      "type": "string",
      "description": "The availability field"
    },
    "rating": {
      "type": "string",
      "description": "The rating field"
    }
  }
}

result = client.extract(
    url="https://www.nordstrom.com/s/nike-air-max-270/12345678",
    schema=schema,
)
print(result.data)

The highlighted lines (5‑12) show the schema definition; the call returns a JSON object matching those keys.

cURL example

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.nordstrom.com/s/nike-air-max-270/12345678",
    "schema": {"properties": {"title": {"type": "string"}, "price": {"type": "string"}, "currency": {"type": "string"}}}
  }'

This minimal schema extracts just title, price, and currency — useful for quick price‑check jobs.

Batch/async usage (Python)

For high‑volume jobs, launch extractions concurrently and handle pagination:

import asyncio
import alterlab

client = alterlab.Client("YOUR_API_KEY")

async def extract_one(url):
    return await client.extract(url=url, schema=schema)

async def main():
    urls = [
        f"https://www.nordstrom.com/s/{slug}/{pid}"
        for slug, pid in product_list  # pre‑fetched list of (slug, pid)
    ]
    tasks = [extract_one(u) for u in urls]
    results = await asyncio.gather(*tasks)
    for r in results:
        print(r.data)  # each is typed JSON per schema

if __name__ == "__main__":
    asyncio.run(main())

This pattern scales to thousands of pages while respecting AlterLab's rate limits; see the pricing page for cost estimates at scale.

Define your schema

The Extract API expects a JSON Schema draft‑07 document under the schema key. Each property you list becomes a field in the output; AlterLab attempts to locate the corresponding value on the page and coerces it to the declared type. If a field cannot be found, it returns null (or you can add "default"). Example schema for a full product record:

{
  "type": "object",
  "properties": {
    "title": {"type": "string"},
    "price": {"type": "string"},
    "currency": {"type": "string"},
    "sku": {"type": "string"},
    "availability": {"type": "string", "enum": ["in_stock","out_of_stock","preorder"]},
    "rating": {"type": "string"},
    "image_url": {"type": "string", "format": "uri"}
  },
  "required": ["title","price","sku"]
}

By marking title, price, and sku as required, the API will only return a successful response when those fields are present, simplifying error handling in pipelines.

Handle pagination and scale

Nordstrom often paginates category or search results. To collect all items:

1. Extract the listing page with a schema that returns an array of product URLs.
2. Iterate over those URLs, firing concurrent extraction jobs.
3. Batch requests (e.g., 50 URLs per job) to stay within your plan's rate limit. AlterLab's pricing is usage‑based; check AlterLab pricing for per‑extraction costs and volume discounts. Credits never expire, allowing you to burst during sales events without waste.

Key takeaways

• AlterLab's Extract API turns any public Nordstrom page into typed JSON via a simple schema POST.
• No need to maintain selectors, handle JavaScript, or manage proxies — focus on the data model.
• Define exactly the fields you need; the API validates and returns clean, ready‑to‑use JSON.
• Scale safely with asynchronous batches and transparent cost tracking.