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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Azure Fundamentals (AZ-900): A Quick-Start Guide
AZ-900 is Microsoft's entry-level Azure exam: 45 minutes on cloud concepts, core Azure services, and management and governance. This guide gives the current outline with its weights, explains the ideas behind each area in plain terms, covers format, scoring and retakes, and sets out a three-week study plan.
Azure Synapse Pipelines vs Standalone ADF
Synapse pipelines are built on Azure Data Factory, so most of what you know transfers. The gaps are few and specific: integration runtime sharing, the SSIS runtime, global parameters, Power Query and how deployment works. This guide lists each documented difference, what it means in practice, and how to choose.
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.
Azure Data Engineering Projects for Your Portfolio
Five Azure data engineering projects for a portfolio, each on real public data and each built to demonstrate one skill an interviewer will ask about: metadata-driven ingestion, incremental loading, Spark cleaning with quality rules, a dimensional model with history, and event-driven processing with alerts.
Parameterizing Pipelines in Azure Data Factory
Parameters turn a pipeline that copies one table into a pipeline that copies any table. This guide explains the four places parameters live in Azure Data Factory, how a value travels from a trigger down to a connection, a worked generic copy in JSON, and where parameterising goes too far.
Deploying ML Models on AWS, Azure and GCP
AWS, Azure and Google Cloud all host models behind managed endpoints, but each expects a different serving contract. This guide runs one real model behind all three contracts locally, including Microsoft's own inference server, and shows what changes between clouds and what should stay exactly the same.