Articles
Page 3 of 5Practical guides on automation, ecommerce optimisation, and data engineering by Fawad Hussain Syed.
Why Is My Shopify Store Slow? Diagnosing and Fixing Performance Issues
Systematically diagnose why your Shopify store is slow — identify the real bottlenecks in themes, apps, images, and third-party scripts, then fix them with measurable results.
Automate Shopify Email Reports: HTML Templates, Scheduling, and Delivery
Build automated Shopify email reports using Python — HTML templates, daily sales summaries, scheduled delivery, and conditional alerts that land in your team inbox every morning.
Multi-Platform Ecommerce Data: Build a Unified Reporting API for Shopify and WooCommerce
Build a unified reporting API layer that pulls data from Shopify, WooCommerce, and other platforms — normalise metrics across stores, automate collection, and feed dashboards without manual exports.
Shopify Admin API: Pull Sales, Inventory, and Customer Data with Python
Learn how to automatically pull sales, inventory, and customer data from your Shopify store using Python and the Admin API. Covers authentication setup, handling pagination and rate limits, GraphQL bulk operations for large stores, and building a complete daily data pipeline — no manual CSV exports needed.
Async Python for Faster Data Collection and Processing
Speed up API calls, web scraping, and file processing with async Python. Covers asyncio, aiohttp, semaphores for rate limiting, and patterns for mixing sync and async code.
A/B Testing for Ecommerce: Using Data to Optimise Product Pages
Run statistically valid A/B tests on your ecommerce product pages. Covers experiment design, sample size calculation, significance testing, and common pitfalls with Python.
How to Build a CI/CD Pipeline for Data Workflows
Ship data pipeline changes with confidence using automated testing, linting, and deployment. Covers GitHub Actions, data validation gates, and rollback strategies.
Event-Driven Data Pipelines with Python and Redis
Build event-driven data pipelines that react to changes in real time using Python and Redis Streams. Covers pub/sub patterns, consumer groups, and backpressure handling.
How to Add Structured Logging to Python Data Pipelines
Replace print statements with structured logging that makes debugging production pipelines fast. Covers Python logging, structlog, JSON output, and correlation IDs.
Building a Lightweight Data Quality Framework from Scratch
Build a reusable data quality framework that scores datasets across completeness, accuracy, consistency, and timeliness — with trend tracking and automated alerting on quality degradation.
Containerizing Your Python Pipelines with Docker
Package your Python data pipelines into Docker containers for consistent, reproducible execution — from Dockerfile to docker-compose, with environment management and production deployment patterns.
How to Design Idempotent Data Pipelines That Are Safe to Re-Run
Build data pipelines that produce the same result whether they run once or ten times — using upserts, deduplication, and staging patterns in Python.