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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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ADF Expressions and Functions: A Practical Guide
The expression language is what makes Azure Data Factory pipelines dynamic. This guide explains when a JSON value is an expression, whole-value expressions versus string interpolation, the system variables, the six function families with documented examples, and the patterns that cover most real pipelines.
Data Modeling in Redshift: A Practical Guide
How to model data in Amazon Redshift: a star schema written as Redshift DDL, why primary and foreign keys are declared but not enforced, how to choose column types, where distribution and sort keys fit, what to do with semi-structured data, and when a materialized view or a wide table is the better answer.
Airflow for ML Pipelines: A Practical Guide
Airflow can schedule more than data loads: it can retrain a model, score each day, check the error and raise an alert. This guide builds a training DAG and a scoring DAG on real bike rental data, and shows them handling a normal day, a bad day, and the day Hurricane Sandy cut the data off.
Python Logging: A Practical Setup for Real Projects
A logging setup you can copy into a real project: why logging beats print, how loggers, handlers and levels fit together, one dictConfig that writes readable console output and a rotating log file, logging exceptions with tracebacks, adding request IDs, and keeping secrets out of logs.
Regular Expressions in Python: A Practical Guide
Regular expressions in Python for everyday jobs: extracting IDs from text, validating formats, cleaning and rewriting strings. Raw strings, search, match and fullmatch, named groups, greedy versus lazy, re.sub with a function, verbose patterns, and the pitfalls that make patterns slow or wrong.
Working with JSON in Python: A Practical Guide
A practical guide to JSON in Python with the json module: how Python types map to JSON and what does not survive a round trip, reading and writing files, pretty and compact output, serialising dates and Decimals, keeping money exact when parsing, understanding decode errors, JSON Lines for large data, and the NaN trap.