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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Building a Streaming Pipeline with Kafka and Spark
A streaming pipeline built and run end to end: 77,033 real taxi events on a Kafka topic, read by Spark Structured Streaming, parsed from JSON, aggregated into hourly event-time windows with a watermark and written to a sink, with the real output showing why the final window is not emitted, plus what production adds.
Data Contracts: Reliable Pipelines Between Teams
What a data contract is and how to use one: the schema, quality rules, ownership and service levels it records, a small contract written for a real taxi dataset and checked against the 2022, 2024 and 2025 files, where two versions break it for different reasons, where to enforce contracts, and how to start.
dbt Tutorial: Transformations in the Modern Data Stack
A practical introduction to dbt: where it fits as the T in ELT, a complete working project, two models, a seed and seven tests, built on three million real taxi trips with DuckDB, how ref orders the build, what a warning-level test looks like, materializations, and the habits that keep dbt projects maintainable.
From Backend Developer to Data Engineer: A Roadmap
A transition guide for backend developers moving into data engineering: the skills that carry over unchanged, a concept map from requests and services to batches and pipelines, the habits that work against you in analytical systems, a four-phase learning plan, and a first project that shows the switch.
Spark Performance Tuning: Shuffles, Joins and Caching
A practical guide to Spark performance: reading the physical plan, why 200 shuffle partitions became one under adaptive query execution, how Spark chooses broadcast or sort-merge joins and how hints change it, what skew looks like in real partition sizes, when caching pays, and a tuning checklist.
How to Become a Data Engineer in 2026
A practical guide to becoming a data engineer: what the job is like day to day, the three common routes in from analysis, software and study, a six-step plan that ends in two projects you can defend, how to practise on real data, where certifications help, what interviews probe, and an honest view of timelines.