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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Slowly Changing Dimensions (SCD) Types Explained
The slowly changing dimension types every data engineer uses: Type 0, 1, 2 and 3 and when each fits, a working example on the real Chinook database where a support-rep reassignment makes Type 1 misattribute revenue while Type 2 keeps it right, how to load Type 2 rows, and the mistakes that corrupt history.
Data Engineering vs Data Science: Roles Compared
Data engineering and data science compared through the work each role does: what they are responsible for according to published role definitions, a typical week in each, the skills and tools that differ and overlap, how the two depend on each other on one project, hybrid roles in between, and how to choose.
Idempotency in Data Pipelines: Why It Matters
What idempotency means for data pipelines and how to achieve it: why retries and redeliveries are unavoidable, four load strategies put through a rerun and a mid-load crash on real taxi aggregates, where plain appends doubled the data and an untransacted replace exposed 47% of it, and the patterns that hold up.
Schema Evolution: Handling Changing Data Structures
How to handle changing data structures, using a real public dataset whose schema changed three ways in three years: detecting an added column, a renamed column and five type changes, why a strict merge refuses and a permissive one keeps both spellings, reading by name, compatibility rules, and safe practices.
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.
Window Functions in SQL for Data Engineers
The window function patterns data engineers use every week, each run on the real Chinook database: ROW_NUMBER for the latest row per key, RANK and DENSE_RANK on ties, LAG for gaps between events, frames for running totals and moving averages, and the default RANGE frame that silently changes a running total.