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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Amazon S3 Explained: Storage, Classes and Best Practices
How Amazon S3 storage classes differ — minimum storage duration, minimum billable size, retrieval time — and a four-question method for putting each dataset in the right class, with lifecycle and security defaults for a data-engineering bucket.
Amazon EC2 Explained: Instances, Types and Pricing
Amazon EC2 is the virtual-server service underneath much of AWS. This guide explains instances, AMIs and storage, decodes instance type names such as r5.4xlarge and compares memory per vCPU across families, sets out On-Demand, Savings Plans, Reserved and Spot purchasing, and shows which charges continue after a stop.
Amazon Redshift Explained: The Cloud Data Warehouse
An introduction to Amazon Redshift, AWS's data warehouse: how a leader node, compute nodes and slices run a query in parallel, why data is stored in columns, how managed storage separates compute from storage, Serverless versus a provisioned cluster, how it differs from PostgreSQL, and what to learn first.
Amazon DynamoDB Explained for Beginners
A beginner's guide to Amazon DynamoDB: what a NoSQL key-value database is for, how tables, items, partition keys and sort keys work, a Query and a Scan run on 412 real invoices to show why key design matters, secondary indexes, capacity modes, consistency, and how data engineers get DynamoDB data out for analytics.
Amazon Athena: Querying S3 with SQL
An explanation of Amazon Athena for people new to it: what the service is, how a table is only metadata pointing at files in S3, what happens when a query runs, where results go, how billing by bytes scanned works, how workgroups cap spending, and where Athena fits beside Redshift and EMR.
Recommendation Systems: How Netflix and Amazon Suggest Items
How recommendation systems work, from the ideas behind Amazon's item-to-item filtering and the Netflix Prize to a working recommender built on a real online shop's transactions, then tested honestly against simple baselines, which turn out to be much harder to beat than most tutorials suggest.