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
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.
Data Warehouse Interview Questions and Answers
Data warehouse interviews ask how the platform behaves, not just what terms mean. Six questions answered on millions of real New York taxi trips: why columnar files are smaller, how partition pruning skips data, why reloads duplicate rows, what schema drift looks like, where stray rows go, and when to summarise.
Snowflake Explained: The Cloud Data Warehouse
Snowflake explained for data engineers: the three layers of storage, compute and cloud services, why separate virtual warehouses isolate workloads, how credits and per-second billing work with a worked example, micro-partitions and pruning, Time Travel and cloning, and habits that keep performance up and costs down.
Building a Data Warehouse: A Step-by-Step Guide
A hands-on guide to building a data warehouse: choosing the business process and grain, staging raw data, building date, zone and payment dimensions, loading a trip fact table with rules, testing for orphan keys, and querying the star, all run in DuckDB on real New York taxi data, plus what production adds.