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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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.
Apache Iceberg vs Delta Lake vs Hudi
A comparison of the three open table formats: the problems they all solve on a data lake, Iceberg's hidden partitioning and partition evolution, Delta Lake's transaction log and Spark roots, Hudi's copy-on-write and merge-on-read tables and incremental queries, interoperability through UniForm, and a choice guide.
Apache Spark Interview Questions and Answers
Spark interviews test whether you can predict what Spark will do with your code. Seven core questions are answered here with evidence from a real Spark session over three million taxi trips: job counts, physical plans, partition numbers and row distributions, instead of definitions recited from memory.
Apache Kafka Explained: Streaming Data Basics
Apache Kafka explained from the ground up: events, topics and partitions, why keys decide ordering, how consumer groups track offsets and why data is not deleted when read, each shown by producing 1,000 real taxi events to a running broker and reading them back, plus replication and delivery guarantees.
Workflow Orchestration with Apache Airflow DAGs
Once you can write an Airflow DAG, the next problems are about design: files that slow the scheduler, branches that silently skip the final step, and dozens of near-identical pipelines. This guide runs four DAG design patterns on Airflow 3.3.2 and shows what each one fixes, with real output.