IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
All articles
Window Functions in SQL for Data Engineers
The window function patterns data engineers use every week, each run on the real Chinook database: ROW_NUMBER for the latest row per key, RANK and DENSE_RANK on ties, LAG for gaps between events, frames for running totals and moving averages, and the default RANGE frame that silently changes a running total.
Cloud Run vs Cloud Functions vs App Engine
Google Cloud has three ways to run code without managing servers, and they have converged: functions are now deployed on Cloud Run, and Google recommends Cloud Run over App Engine for new work. This guide explains what each is today, where Cloud Run jobs fit for data work, what scaling to zero saves and how to choose.
Glue Workflows vs Step Functions for Orchestration
Glue workflows and Step Functions can both run a multi-step pipeline on AWS, but one only knows about Glue and the other coordinates almost any service. This comparison covers what each can start, how they handle failure and reruns, how they are triggered and billed, and where managed Airflow fits as a third option.
Lambda Functions, map, filter and reduce in Python
Lambda, map, filter and reduce explained by what they are good for: key functions for sorting and max, map and filter as lazy one-shot iterators, why reduce is usually replaced by a built-in, the operator module, and the loop-variable trap that makes every lambda return the same value.
DAX FILTER and ALL Functions Explained
The DAX FILTER and ALL functions explained with real sales numbers: how ALL on a table or a column produces three different shares of total, what ALLEXCEPT, ALLSELECTED and REMOVEFILTERS change, when FILTER is genuinely needed, such as conditions on measures, and why Microsoft recommends Boolean filters where possible.