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 Engineering Projects for Your Portfolio
Five data engineering projects for a portfolio, each on a real public dataset: a tested batch pipeline and warehouse on NYC taxi data, an orchestrated ingestion of live earthquake feeds, change capture with history on a sample store database, a streaming pipeline on Wikipedia edits, and a Spark job on GitHub events.
Kafka vs RabbitMQ vs Pulsar: A Comparison
Kafka, RabbitMQ and Pulsar compared for data and application teams: the queue model against the log model, shown by sending the same 1,000 events through live RabbitMQ and Kafka brokers, where a second reader got nothing from the queue and everything from the log, then routing, ordering, retention and Pulsar's design.
Parquet vs ORC vs Avro: File Formats Compared
How Parquet, ORC and Avro differ and when to use each: row-based versus columnar layouts, the same 500,000 real taxi trips written as CSV, Avro, Parquet and ORC with measured file sizes, how much of a Parquet file one column occupies, schema evolution, splitting, and a choice guide.
Orchestration: Airflow vs Dagster vs Prefect
Airflow, Dagster and Prefect compared by writing the same real pipeline in each: Airflow's DAG of tasks, Prefect's flows that run like ordinary Python, and Dagster's assets that describe the data to be produced, with the Prefect and Dagster versions run on real taxi data, a comparison table and how to choose.
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.
SQL Optimization for Data Engineers
How to make SQL queries faster with evidence: reading the query plan, a full scan versus a composite index versus a covering index measured in engine steps on 2.9 million real taxi rows, why wrapping a column in a function cost twenty times more work, and the habits that carry over to SQL Server and warehouses.