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.
All articles
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 Kinesis Explained: Real-Time Streaming
Amazon Kinesis is AWS's family of streaming services. This guide explains Kinesis Data Streams, shards, partition keys, sequence numbers and retention, shows on 2,000 real taxi events how the choice of partition key spreads or skews load across shards, and covers capacity modes, consumer types and where Firehose fits.
Athena Federated Queries Explained
Athena Federated Query lets one SQL statement read from sources outside S3, such as DynamoDB, MySQL or Redshift, and join them with data lake tables. This guide explains connectors, predicate pushdown, passthrough queries, the documented limits, how billing works, and when copying the data is the better design.
S3 vs EBS vs EFS: AWS Storage Compared
S3, EBS and EFS are three different kinds of storage: objects reached over an API, a disk attached to one server, and a file system shared by many. This comparison explains how each is accessed, what it can and cannot do, how it scales and is billed, and which suits a data lake, a database, or shared working files.
AWS Glue Data Catalog and Crawlers Explained
The Glue Data Catalog is the index of an AWS data lake, and crawlers are the usual way to fill it. This guide explains what the catalog stores, how a crawler classifies files and decides between tables and partitions, what three real files with drifting schemas show, and the settings that keep crawlers predictable.
Amazon EMR Explained: Big Data on AWS
Amazon EMR runs Spark, Hadoop, Hive and other big data frameworks on AWS. This guide explains primary, core and task nodes, why data belongs in S3 and not in the cluster's HDFS, how work is submitted as steps, transient versus long-running clusters, the Serverless and EKS options, and what decides the bill.