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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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CDC (Change Data Capture) Explained with Examples
What change data capture is and how it works: the problems it solves that watermarks cannot, log-based capture in SQL Server and Debezium, a working trigger-based example on the real Chinook database that replicates an insert, six updates and a delete into a copy, safe replays after a crash, and pitfalls.
SQL for Data Analysts: The Queries You Need Daily
The SQL an analyst writes every day, each query run on a real sample database: profiling a table before trusting it, reconciling totals, monthly reports that keep empty months, share of total with a window, period-over-period change with LAG, new versus returning customers and the latest record per customer.
GCP Networking Basics for Data Engineers
Most data services on Google Cloud need no network setup, until a job must run on private addresses or reach a database. This guide covers VPC networks, subnets, firewall rules, Private Google Access, Cloud NAT and service perimeters, and sizes subnets for pipeline workers with a short script.
Statistics for Data Science: The 20% You Actually Need
The statistics data scientists use constantly, each idea shown on real data in Python: when the median beats the mean, confidence intervals by bootstrapping, what a p-value does and does not say, why effect sizes matter, how testing many things manufactures findings, and how Simpson's paradox reverses a conclusion.
SQL for Data Science: Queries Every Data Scientist Must Know
The SQL queries data scientists write every week, each run on a real sample database with its output: joining and aggregating, the join fan-out that inflated revenue ninefold, finding what is missing with an anti-join, CTEs and window functions for running totals and top-N per group, and counting around NULLs.
Airflow Tutorial: Orchestrating Data Pipelines
A practical introduction to Apache Airflow: what orchestration adds to a pipeline, the scheduler, DAG processor and metadata database, a complete three-task DAG written with the TaskFlow API and run on real taxi data with dag.test, how XComs, retries and logical dates work, an idempotent rerun, and design habits.