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 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.
Career Switch to Data Science: A Realistic Roadmap
Most data scientists hold a master's degree or a doctorate, and that shapes any realistic career switch. Data from O*NET and from 574 working data scientists shows the education, experience and tools the job really involves, and why moving through an analyst role first is the most reliable route for career changers.
Azure Data Factory (ADF) Interview Questions
Senior Azure Data Factory interviews rarely ask for definitions. They hand you a design problem and watch how you reason. Here are six of those exercises, each built on a documented ADF limit, with the trap the interviewer is looking for and a model answer. The numbers come from a small calculator you can rerun.
Copy Activity in Azure Data Factory: A Practical Guide
The Copy activity is the workhorse of Azure Data Factory. This guide explains what it does in three steps, the formats and stores it handles, column mapping, the sink write behaviours, insert, pre-copy script and upsert, compared on a real table across three runs, fault tolerance, staging, and reading the output.
AWS Interview Questions for Data Engineers
AWS interview questions for data engineers on S3, Glue, Athena, Redshift, Kinesis, Step Functions and security, each with the points a strong answer includes, the follow-up to expect, and three end-to-end scenario questions.
Mapping Data Flows in ADF: Transformations Without Code
Mapping data flows let you design transformations on a canvas in Azure Data Factory and run them on Spark clusters the service manages. This guide explains how they work, walks through one flow stage by stage with real row counts, and sets out when a data flow is the right tool.