Building a Robust pSEO Data Pipeline

Overview

Your programmatic SEO site is only as good as your data. This guide covers how to build a data pipeline that's reliable, scalable, and maintainable.

Pipeline Architecture

Core Components

1. Data Sources: APIs, scrapers, manual inputs
2. Ingestion Layer: Collection and normalization
3. Storage: Database with proper indexing
4. Processing: Enrichment, validation, deduplication
5. Output: API for your frontend/SSG

Recommended Architecture

Sources → Ingestion (Airflow/Cron) → PostgreSQL → Validation → API → SSG Build

Step 1: Data Ingestion

API Ingestion

• Use rate limiting to respect API limits
• Implement exponential backoff for failures
• Log all requests for debugging
• Cache responses to reduce API calls

Web Scraping

• Respect robots.txt and rate limits
• Use rotating proxies for scale
• Handle dynamic content (Playwright/Puppeteer)
• Monitor for structure changes

Step 2: Data Storage

Schema Design

• Normalize for consistency
• Index columns used in queries
• Use JSONB for flexible attributes
• Track updated_at for freshness

Example Schema

CREATE TABLE entities (
  id SERIAL PRIMARY KEY,
  slug VARCHAR(255) UNIQUE NOT NULL,
  name VARCHAR(255) NOT NULL,
  category_id INTEGER REFERENCES categories(id),
  attributes JSONB,
  status VARCHAR(50) DEFAULT 'draft',
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW()
);

Step 3: Data Validation

Validation Rules

• Completeness: Required fields present
• Format: URLs valid, dates parseable
• Business logic: Prices positive, ratings 1-5
• Uniqueness: No duplicate entities

Quality Monitoring

• Set up alerts for validation failure spikes
• Track data freshness per source
• Monitor for duplicate content
• Regular audits of sample pages

Step 4: Automation

Scheduling

• High-frequency data: Hourly updates
• Moderate: Daily updates
• Static data: Weekly/monthly

Tools

• Simple: Cron jobs
• Medium: GitHub Actions
• Complex: Apache Airflow, Dagster

Step 5: Monitoring

Key Metrics

• Pipeline success rate
• Data freshness (time since last update)
• Entity coverage (% with complete data)
• Error rates by source