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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GCP Data Engineering Projects for Your Portfolio
A portfolio project is evidence, and evidence means numbers, failure handling and a README someone can follow. This guide sets out six Google Cloud data engineering projects in rising difficulty, says what each proves and costs, and profiles a real public dataset to show what the write-up should contain.
Data Engineering Tools You Must Learn in 2026
A grounded guide to data engineering tools in 2026: the jobs a data platform must do and the tool that usually does each, SQL and Python first, then orchestration, transformation, Spark, storage formats, streaming and cloud, with notes on current versions such as Spark 4, Kafka 4 and Airflow 3, and what to skip.
Real-Time Feature Engineering for Online Models
Online models need features from events seconds old, and those features must match the ones the model was trained on. On a month of real New York taxi pickups, this guide builds a streaming window counter, shows a one-character boundary change breaking 3.7% of lookups, and measures what a two-minute lag costs.
Azure Data Engineering Projects for Your Portfolio
Five Azure data engineering projects for a portfolio, each on real public data and each built to demonstrate one skill an interviewer will ask about: metadata-driven ingestion, incremental loading, Spark cleaning with quality rules, a dimensional model with history, and event-driven processing with alerts.
AWS Data Engineering Projects for Your Portfolio
Five AWS data engineering projects for a portfolio, on real public data: a serverless lake queried with Athena, event-driven ingestion with Lambda, an orchestrated Glue pipeline, a streaming path through Kinesis and Firehose, and a Redshift warehouse. Each lists the services, what it proves and cost cautions.
Data Engineering Interview Questions and Answers
Data engineering interviews test whether you can move data reliably at the right scale. This guide maps the rounds with model answers, then works a design-round sizing task from real New York taxi data, where the numbers show that the obvious big-data design is the wrong one.