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
LinkedIn Optimization for Tech Job Seekers in 2026
LinkedIn advice usually stops at keywords and a good photo, but the best evidence points at your network. A randomised experiment with over 20 million members showed which connections lead to new jobs. This guide covers that evidence, Open to Work settings including India's, and a profile checklist.
BigQuery Cost Optimization: Slots, Pricing and Tips
A BigQuery bill has two parts, compute and storage, and nearly all the surprises are in compute. This guide explains both pricing models, prices three query shapes on a real table, gives the break-even between on-demand and slots, and lists the limits that stop one bad query becoming an expensive one.
Cost Optimization for LLM Applications in Production
LLM bills grow with every token, and most applications waste many of them. This guide breaks a request's cost into its parts, measures prompt caching and output length on a local model, then prices five options for a month of traffic using published list rates, from no changes to a cached small model.
AWS Cost Optimization for Data Workloads
A practical guide to AWS cost optimisation for data platforms: what each service bills for, the levers that move the bill for S3, Athena, Glue, EMR, Redshift and streaming, a measured example where storage layout cuts the data a query reads from 306.5 MB to 92 KB, and the budgets, tags and limits that stop surprises.
SQL Optimization for Data Engineers
How to make SQL queries faster with evidence: reading the query plan, a full scan versus a composite index versus a covering index measured in engine steps on 2.9 million real taxi rows, why wrapping a column in a function cost twenty times more work, and the habits that carry over to SQL Server and warehouses.
Cost Optimization for ML Workloads in the Cloud
Most of an ML cloud bill comes from three decisions: how capacity is bought, how fully it is used and how efficiently each prediction is made. With live Azure prices and two real models, this guide shows reservations and spot cutting prices by 38 to 82%, and an always-on VM costing seven times its compute.