Building Data Pipelines for E-Commerce Intelligence

Introduction

A production retail intelligence pipeline has five stages. Product Data Scrape powers the ingestion layer for many enterprise data teams. This guide shows the full architecture.

Five-Stage Pipeline Architecture

• INGEST Product Data Scrape API → Raw zone (S3/GCS as JSON)
• STAGE Raw → Bronze tables (typed, partitioned)
• TRANSFORM Bronze → Silver (cleaned, deduplicated)
• MODEL Silver → Gold (business-ready aggregations)
• SERVE Gold → BI tools / APIs / downstream apps

Stage 1: Ingestion from Product Data Scrape

import boto3
from datetime import datetime

s3 = boto3.client("s3")

async def ingest_from_product_data_scrape(urls):
    # Fetch from Product Data Scrape API
    results = await pds_api.batch_fetch(urls)
    
    timestamp = datetime.utcnow()
    key = f"raw/products/dt={timestamp.date()}/hour={timestamp.hour:02d}/{timestamp.timestamp()}.parquet"
    
    s3.put_object(
        Bucket="my-data-lake",
        Key=key,
        Body=pd.DataFrame(results).to_parquet()
    )

Stage 3: Silver Layer (Cleaned)

-- dbt model: silver_products.sql
WITH ranked AS (
    SELECT *,
        ROW_NUMBER() OVER (
            PARTITION BY product_id, retailer
            ORDER BY scraped_at DESC
        ) AS recency_rank
    FROM {{ ref('bronze_products') }}
)
SELECT
    product_id,
    retailer,
    REGEXP_REPLACE(title, '[🔥⭐✨]', '') AS title,
    INITCAP(brand) AS brand_normalized,
    price_current,
    CASE 
        WHEN price_msrp > 0 
        THEN ROUND((price_msrp - price_current) / price_msrp * 100, 1)
        ELSE 0 
    END AS discount_pct,
    availability = 'in_stock' AS is_available,
    scraped_at
FROM ranked
WHERE recency_rank = 1
  AND title IS NOT NULL;

Sample Bronze Table from Product Data Scrape Ingestion

{
  "table": "bronze.products",
  "partition": "dt=2026-06-09",
  "schema_version": "v2.4",
  "row_count": 8945721,
  "size_gb": 12.4,
  
  "sample_row": {
    "product_id": "B0CHX1W1XY",
    "retailer": "amazon_us",
    "title": "Echo Dot (5th Gen) Smart Speaker",
    "brand": "Amazon",
    "price_current": 44.99,
    "price_msrp": 49.99,
    "currency": "USD",
    "availability": "in_stock",
    "rating": 4.6,
    "reviews_count": 142891,
    "scraped_at": "2026-06-09T10:23:00Z",
    "data_source": "product_data_scrape",
    "ingested_at": "2026-06-09T10:25:14Z",
    "bronze_load_id": "load_2026_06_09_10"
  },
  
  "quality_checks": {
    "row_count_check": "passed",
    "freshness_check": "passed",
    "null_rate_check": "passed",
    "price_sanity_check": "passed"
  }
}

How Product Data Scrape Helps

We deliver data directly to your S3 bucket, BigQuery dataset, Snowflake table — pre-cleaned, deduplicated, and QA-passed. You skip Bronze and Silver layers entirely.

About Product Data Scrape

Product Data Scrape is the leading provider of managed web scraping services and ready-to-use product datasets. We help 200+ brands, retailers, and AI companies turn the messy public web into clean, structured product data.

Our Services: - Web Scraping API — REST API for developers (1,000 free credits) - Scraper as a Service — Custom scrapers built in 7-10 days - Ready Datasets — 100+ pre-built datasets, free 1,000-row samples in 24 hours

Contact: - Website: https://www.productdatascrape.com - Email: sales@productdatascrape.com