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 Engineering Roadmap 2026: A Complete Guide
A stage-by-stage data engineering roadmap for 2026: what the role involves according to current certification guides, then seven stages, SQL, Python, pipelines and modelling, orchestration and quality, Spark and big data, streaming, and cloud platforms, each with a checkpoint, and detours to avoid.
ETL vs ELT: Differences and When to Use Each
The difference between ETL and ELT, shown by running both on three million real taxi trips: ETL stores 31 transformed rows while ELT keeps every raw row and transforms in SQL, then a business rule changes and only ELT can re-derive without re-extracting; plus when to choose each, reverse ETL and streaming.
Data Warehouse Explained: Concepts and Architecture
A clear explanation of data warehouses: why analytical queries need a separate store from transactional systems, the architecture from sources through staging, modelled layers and a semantic model, facts, dimensions, grain and history, modelling approaches, modern platforms and common pitfalls.
Data Lake vs Data Warehouse vs Lakehouse
A clear comparison of data lakes, data warehouses and lakehouses: what each stores and when structure is applied, strengths and failure modes such as the data swamp, what a lakehouse adds through table formats and governance, a side-by-side table, and practical guidance on choosing or combining them.
Medallion Architecture: Bronze, Silver and Gold Layers
The medallion architecture explained by building it: bronze as raw data exactly as it arrived, silver as validated and enriched rows, a quarantine table that keeps every rejected row with its reason, gold shaped for one dashboard, a reconciliation that bronze equals silver plus quarantine, and the mistakes teams make.
Apache Spark Explained for Beginners
A beginner's guide to Apache Spark: what it is for, how the driver, executors and cluster manager fit together, jobs, stages and tasks, lazy transformations and actions demonstrated on three million New York taxi trips, partitions, installing PySpark, and the mistakes beginners make.