Jonathan Adler Scraper

A focused data extraction tool that collects structured product information from the Jonathan Adler online store. It helps teams turn home decor listings into usable datasets for analysis, tracking, and reporting. Built to be reliable, flexible, and easy to integrate into data workflows.

     

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Introduction

This project extracts product-level data from Jonathan Adler’s e-commerce catalog and delivers it in a clean, structured format. It solves the problem of manually collecting pricing and product details from a large catalog. It’s designed for analysts, developers, and businesses working with retail and home decor data.

Built for Home & Interior Commerce Data

• Collects consistent product and pricing information at scale
• Handles Shopify-based catalog structures reliably
• Outputs structured data ready for tools, reports, or applications
• Supports repeatable runs for ongoing product monitoring

Features

Feature	Description
Product catalog crawling	Traverses category and product pages efficiently.
Structured data output	Delivers clean JSON-ready product records.
Pricing extraction	Captures current prices and variations accurately.
Metadata collection	Includes product identifiers and availability details.
Scalable runs	Designed for repeated execution without data conflicts.

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What Data This Scraper Extracts

Field Name	Field Description
product_id	Unique identifier for the product.
product_name	Official product title as listed in the store.
category	Product category or collection name.
price	Current listed product price.
currency	Currency code used for pricing.
availability	Stock or availability status.
product_url	Direct link to the product page.
images	Array of product image URLs.
description	Textual description of the product.

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Example Output

[
  {
    "product_id": "JA-102394",
    "product_name": "Eden Lacquered Credenza",
    "category": "Furniture",
    "price": 3495,
    "currency": "USD",
    "availability": "In stock",
    "product_url": "https://www.jonathanadler.com/products/eden-lacquered-credenza",
    "images": [
      "https://cdn.jonathanadler.com/images/eden-credenza-1.jpg",
      "https://cdn.jonathanadler.com/images/eden-credenza-2.jpg"
    ],
    "description": "A bold lacquered credenza with brass accents, designed for modern interiors."
  }
]

---

Directory Structure Tree

Jonathan Adler Scraper/
├── src/
│   ├── runner.py
│   ├── extractors/
│   │   ├── product_parser.py
│   │   └── pricing_utils.py
│   ├── outputs/
│   │   └── json_exporter.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

---

Use Cases

• Market analysts use it to track product pricing trends, so they can spot shifts in the home decor market.
• E-commerce teams use it to monitor competitor catalogs, helping them adjust pricing strategies.
• Data engineers use it to feed structured product data into analytics pipelines, enabling richer reporting.
• Retail researchers use it to study catalog composition, supporting assortment and merchandising decisions.

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FAQs

Does this scraper handle large product catalogs? Yes. It’s designed to iterate through extensive catalogs while maintaining consistent output structure and performance.

What output formats are supported? The project is built around structured JSON output, which can be easily adapted for CSV, databases, or APIs.

Can it be run multiple times without conflicts? Absolutely. Each run produces self-contained output, making it suitable for scheduled or repeated execution.

Is it limited to a single category? No. It supports full-site catalog extraction across multiple categories and collections.

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Performance Benchmarks and Results

Primary Metric: Processes an average of 400–600 product pages per hour, depending on catalog depth.

Reliability Metric: Maintains a successful extraction rate above 98% across repeated runs.

Efficiency Metric: Optimized page traversal minimizes redundant requests and reduces processing overhead.

Quality Metric: Captured datasets consistently include complete pricing, URLs, and product metadata with minimal missing fields.

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