Articles
Page 5 of 6Practical guides on automation, ecommerce optimisation, and data engineering by Fawad Hussain Syed.
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 deduplication, 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.