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 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.
ADF Data Flow Transformations: A Complete Reference
A reference to the transformations available in Azure Data Factory mapping data flows, grouped by task: shaping columns, filtering and marking rows, combining streams and handling nested data. Each has its SQL equivalent and the documented detail worth knowing, with row counts from a real example.
Data Science Career Guide: Roles, Skills and Growth Path
A map of data science careers: what data analysts, data scientists, machine learning engineers, data engineers and analytics engineers actually do, the skills that separate them, realistic routes into a first role, and how people grow from junior work to senior, lead and specialist positions.
Microsoft Fabric vs Azure Data Factory: What's New
Microsoft calls Fabric Data Factory the next generation of Azure Data Factory. This guide maps each Data Factory concept to its Fabric counterpart, lists what Fabric adds and what its documentation says is still missing, explains the move from pay-as-you-go to capacity billing, and sets out the three upgrade paths.
Data Engineering Tools You Must Learn in 2026
A grounded guide to data engineering tools in 2026: the jobs a data platform must do and the tool that usually does each, SQL and Python first, then orchestration, transformation, Spark, storage formats, streaming and cloud, with notes on current versions such as Spark 4, Kafka 4 and Airflow 3, and what to skip.
Streaming Data with Kinesis to S3 and Redshift
A practical guide to landing streaming data on AWS: Kinesis Data Streams into S3 with Firehose, how size-or-time buffering decides file counts, shown on a real day of 77,033 events, converting to Parquet and partitioning on the way, the two routes into Redshift, duplicates, and the compaction most streams still need.