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.
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Data Versioning with DVC: A Hands-On Guide
Git versions code but chokes on data. DVC fills the gap by keeping a small pointer to each dataset version in Git and the data itself in its own store. This hands-on guide versions a real dataset twice, rolls back to the first version, and builds a pipeline that reruns training only when something changes.
How to Build a Data Portfolio Website
A data portfolio website is worth building when it does something GitHub alone cannot: present projects as short case studies a non-technical reader can follow. This guide covers the pages you need, a case-study structure, free static hosting on GitHub Pages within its documented limits, and a checklist.
Data Partitioning Strategies in ADF Copy Activity
A large table copied through one query is limited by one connection. This guide explains the partition options of the Azure Data Factory Copy activity, how dynamic range bounds really work, how to choose a partition column, with skew measured on 2.9 million real rows, and how partitioning applies to files.
Handling Late-Arriving Data in Pipelines
A practical approach to late-arriving data: event time versus arrival time, lateness measured on 2.9 million real taxi trips, where a midnight report missed 0.81% of each day and a tail lingered for hours, then the fixes: waiting, reprocessing windows, streaming watermarks and late-arriving facts.
AWS Interview Questions for Data Engineers, Part 2: The boto3 Coding Round
Many AWS data engineering interviews include a short coding round with boto3. The tasks look simple, but each hides a trap: pagination, retries, duplicates, request limits and key ordering. Here are five such tasks, run against a local AWS emulator, with the trap and the answer that shows experience.
Probability for Data Science: Concepts You Must Know
Six probability concepts data scientists use, each computed and checked by simulation in Python: why a positive result from a good test can mean only a 9% chance, expected value for decisions, binomial and Poisson counts, why averages become normal, and whether a model's probabilities can be trusted.