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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Real-Time Analytics: Architecture and Tools
How real-time analytics systems are built: the four layers of ingestion, stream processing, serving and consumption, the tools at each layer including Kafka, Spark, Flink and real-time OLAP databases such as Druid, Pinot and ClickHouse, latency budgets, common architectures, and the pitfalls that raise costs.
Marketing Analytics: Metrics and Dashboards That Matter
The marketing analytics metrics that matter, defined precisely: click-through rate, conversion rate, cost per acquisition, return on ad spend and engagement rate, then applied to 12,330 real shop sessions to compare traffic sources by what they deliver, plus a dashboard layout and attribution traps.
Building a Serverless Analytics Stack on AWS
A serverless analytics stack on AWS has no cluster to run: S3 for storage, Glue for the catalog and transformation, Athena for SQL, Lambda and Step Functions for triggers and orchestration, and a BI tool on top. This guide builds it layer by layer, lists what each service bills for, and says where it stops fitting.
Web Analytics Basics: Understanding User Behavior
The basics of web analytics: how Google Analytics 4 measures a site with events, users, sessions, engagement and key events, how UTM tags attribute traffic, and what 12,330 real shop sessions reveal about browsing depth, new versus returning visitors and page value, including two numbers that mislead if read naively.
Data Analytics Roadmap for Non-Tech Backgrounds
A data analytics roadmap for people coming from non-technical fields such as sales, HR, commerce, operations or the arts: what you already have that helps, the maths you really need, a learning order that starts inside your current job, how to handle jargon and doubt, and how to present your background as an advantage.
Data Analytics Projects for Your Resume
Seven data analytics projects for a resume, each on a real, openly licensed dataset and framed as a business question: sales and returns, customer retention and value, bike-share operations, a SQL business review, country indicators, a developer survey and an Indian public dataset, with the skills each proves.