Articles
Practical guides on automation, ecommerce optimisation, and data engineering by Fawad Hussain Syed.
Build a Python AI Agent for Automated Data Analysis
Build an AI agent in Python that analyses datasets autonomously — writing and executing its own pandas code, generating summaries, and producing reports — with tool-use patterns, sandboxed execution, and guardrails to keep it from going off the rails.
Build a Price Monitoring Bot with Python and Playwright
Build a price monitoring bot that tracks competitor prices on any website — using Playwright for JavaScript-rendered pages, structured extraction with fallback selectors, change detection, and scheduled alerts when prices move.
Bun Rewrites in Rust: What It Means for the JavaScript Runtime
Bun merged a million-line PR rewriting its core from Zig to Rust. This post covers the technical motivations, what changed in the codebase, measured performance impact, and what it signals for the JavaScript runtime ecosystem.
Python Polars vs Pandas: Performance Benchmarks with Real Data
Benchmark Polars against Pandas on real-world data tasks — CSV loading, group aggregations, joins, window functions, and memory usage — with actual numbers so you can decide when switching is worth it.
Build an LLM-Powered Data Pipeline with Python and OpenAI
Build a production data pipeline that uses OpenAI's API to classify, extract, and enrich unstructured text — with structured output parsing, cost controls, rate limiting, and fallback logic so it actually works at scale.
Shopify Performance Monitoring: Automated Alerts for Store Speed
Build an automated monitoring system that tracks Shopify store performance, detects speed regressions, and sends alerts before customers notice — using Lighthouse CI, synthetic checks, and threshold-based notifications.
Ecommerce Data Pipeline: Reporting Architecture That Scales
Design a data pipeline architecture for ecommerce reporting that handles multi-platform ingestion, incremental loads, dimension modelling, and automated report generation — from raw API data to business-ready dashboards.
Python Secrets Management for Automation Pipelines
Manage API keys, database credentials, and tokens across Python automation pipelines — from environment variables to vault integration, with patterns that scale from solo scripts to team deployments.
How to Build Dashboards Without BI Tools: Python + HTML
Build interactive data dashboards using Python and HTML templates instead of expensive BI tools — from data processing to chart rendering to automated deployment, with no licence fees.
Prefect Reusable Task Blocks: Build Composable Workflow Components
Build reusable task blocks in Prefect that snap together into different workflows — shared extractors, validators, loaders, and notification tasks that eliminate duplication across pipelines.
Prefect IntervalSchedule Migration: From Legacy Schedules to Prefect 3
Migrate from deprecated prefect.server.schemas.schedules IntervalSchedule to Prefect 3 scheduling patterns — with side-by-side code comparisons, common errors, and a step-by-step upgrade path.
Shopify Automated Reporting: Build a Self-Updating Sales Dashboard
Build a self-updating Shopify dashboard that tracks sales, inventory levels, and customer metrics automatically — with scheduled data pulls, trend comparisons, and alert thresholds.
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.
How to Automate Shopify Reports: Complete Python Guide
Set up fully automatic Shopify reports — daily sales summaries, inventory alerts, and customer analytics — using Python and the Shopify Admin API with scheduling and email delivery.
Ecommerce Reporting API: How to Automate Store Data Collection and Analysis
Build a unified ecommerce reporting API layer that pulls data from Shopify, WooCommerce, and other platforms — normalise metrics, automate collection, and feed dashboards without manual exports.
Shopify Reporting API: How to Pull Sales, Inventory, and Customer Data Automatically
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.
Web Scraping to Structured Data: Building Reliable Extraction Pipelines
Turn web pages into clean, structured data with Python — using resilient selectors, rate limiting, change detection, and robots.txt compliance for reliable extraction pipelines.
How to Schedule and Orchestrate Multi-Step Workflows with Prefect
Move beyond cron jobs — use Prefect to orchestrate data pipelines with dependency management, automatic retries, parallel execution, and a monitoring dashboard.
SQL for Data Engineers: Window Functions, CTEs, and Query Optimization
Go beyond SELECT * — master window functions, CTEs, and query optimization techniques that turn slow, convoluted SQL into fast, readable queries for reporting pipelines.
How to Build a Notification System That Actually Gets Read
Build a multi-channel notification system that routes alerts to Slack, email, and SMS based on severity — with rate limiting, digests, and escalation so people do not ignore your alerts.
Real-Time vs Batch: Choosing the Right Data Pipeline Architecture
Understand when to use batch processing, real-time streaming, or a hybrid approach — with architecture diagrams, working code for each pattern, and a decision framework.
Secrets, Keys, and Tokens: Securing Your Python Automation Scripts
Stop hardcoding API keys in your scripts. Learn how to manage secrets properly with environment variables, vaults, token rotation, and secure CI/CD pipelines.
Testing Data Pipelines: A Practical Guide with pytest
Write tests that catch broken pipelines before they produce wrong reports — with pytest fixtures, DataFrame assertions, mock APIs, and CI integration.
How to Build Self-Healing Data Pipelines That Recover from Failures
Build data pipelines that detect failures, retry intelligently, recover from partial runs, and alert you only when human intervention is actually needed.
How Modern Systems Move from Manual Work to Automated and Intelligent Systems
The shift from manual processes to automated and intelligent systems — how automation, ecommerce optimisation, and data pipelines connect into a system-level approach to eliminating repetitive work.
How to Design Data Pipelines for Reliable Reporting Systems
Design data pipelines that handle multiple sources, ensure data quality, automate scheduling, and produce reliable reports — with architecture patterns and working Python examples.
How to Clean Messy Excel Data Using Python
A step-by-step guide to cleaning common Excel data problems — duplicates, missing values, inconsistent formatting, merged cells — using pandas, with a working example on a real messy dataset.
How to Build a Data Dashboard Without Manual Excel Work
Build an automated data dashboard pipeline — from raw sources (Excel, APIs) through cleaning and aggregation to dashboard-ready output — replacing manual spreadsheet work entirely.
How to Improve Ecommerce Conversion Using Data and Automation
Use data to identify checkout, speed, and UX bottlenecks in ecommerce stores — then automate the insights and reporting that connect performance improvements to revenue.
How to Automate Shopify Reports with Python and the Shopify API
Automate Shopify reports using Python and the Shopify Admin API — set up automatic daily and weekly sales reports, inventory tracking, and ecommerce KPI dashboards without manual spreadsheets or CSV exports.
How to Fix Slow Shopify Stores: A Performance Checklist
A practical checklist for diagnosing and fixing Shopify performance issues — covering themes, apps, scripts, images, and backend bottlenecks with measurable before-and-after results.
How to Automate Data Workflows Using APIs and Python
Build automated data pipelines that fetch from APIs, clean and transform data, combine multiple sources, and export results — with scheduling for hands-free operation.
Python Automation: Real Workflows That Replace Manual Processes
Common automation workflows — Excel processing, API integrations, reporting pipelines — with before-and-after comparisons and working Python code for each.
Automate Excel Reports with Python: A Practical Guide Using pandas and openpyxl
Build a complete Python pipeline that reads raw data, transforms it, and generates formatted Excel reports — then schedule it to run automatically. Includes a full copy-paste script.