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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Delta Lake Explained: Reliable Lakes with ACID
Delta Lake explained with a working table built from real taxi data: what the transaction log is, how each write becomes an atomic version, schema enforcement rejecting a bad append, delete and merge, a merge that quietly re-inserted deleted rows, time travel between versions, and maintenance.
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.
Distributed Systems Basics for Data Engineers
The distributed systems ideas behind Spark, Kafka and cloud warehouses, for data engineers: partitioning and hot keys, why modulo hashing moved 74% of real taxi data when a node was added and consistent hashing 23%, replication, what CAP really says, failures and retries, and why clocks cannot be trusted.
Data Modeling: Star Schema, Snowflake and Data Vault
The three main warehouse modelling approaches compared by building each from the real Chinook database: a star with 3 tables, a snowflake with 6 and a data vault with 11, the joins the same question needs in each, why all three give identical answers, what each optimises for, and how teams combine them.
Data Engineer Salary in Hyderabad 2026: How the Role Pays Compared With Others
Is data engineering a well-paid role, or just a popular one? This guide answers with real 2025 survey data rather than unsourced ranges: data engineers compared with analysts, data scientists, AI/ML engineers and developers in the same country and with the same experience, and what that means for a Hyderabad offer.
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.