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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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Spark vs Hadoop: What Changed and Why
What changed when Spark overtook Hadoop MapReduce: the three parts of Hadoop, the MapReduce model emulated step by step on three million taxi trips with its intermediate data written to disk, why in-memory processing and DAGs mattered, what Spark still borrows from Hadoop, and what that means today.
Tokenization in LLMs: Why It Matters More Than You Think
Models do not read words. They read tokens, and tokens decide what a prompt costs, how much fits in the context window and why a model stumbles on spelling and long numbers. This guide explains subword tokenization and measures it: one sentence is 8 tokens in English and up to 99 in Telugu.
Feature Stores Explained: Why Teams Need Them
A feature store keeps the features a model trains on and the features it is served the same. This guide builds one with Feast on a year of real retail orders and shows the problem it solves: a naive join that looked better in validation, 0.798 against 0.727, then did worse in production.
Idempotency in Data Pipelines: Why It Matters
What idempotency means for data pipelines and how to achieve it: why retries and redeliveries are unavoidable, four load strategies put through a rerun and a mid-load crash on real taxi aggregates, where plain appends doubled the data and an untransacted replace exposed 47% of it, and the patterns that hold up.
XGBoost Explained: Why It Wins Kaggle Competitions
XGBoost explained: how gradient boosting builds trees that correct each other, what XGBoost added to make it fast and robust, and why it won so many competitions, tested on 48,842 real census records with native categorical data, missing values and early stopping, and compared honestly with other models.