Outcome 1
A complete raw-to-mart analytics model map
Free course
Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage
From raw tables to trusted business metrics, step by step. Beginner-friendly, interactive, and built for freshers learning production data work.
Outcomes
What you will be able to build and explain
Each outcome is tied to architecture, operational judgement, or a concrete deployment habit you can reuse at work.
Outcome 2
Tested dbt-style staging, intermediate, fact, and dimension models
Outcome 3
Governed metric specs for revenue, active users, and conversion
Outcome 4
A semantic layer map with entities, measures, dimensions, and metrics
Outcome 5
A lineage blast-radius worksheet from source column to dashboard and AI consumer
Outcome 6
A capstone design that can be used as a fresher portfolio artifact
Learning loop
Learn the model, practice the decision, keep the checklist
A beginner-friendly production analytics engineering course for freshers. You will learn how modern data teams transform raw warehouse tables into tested dbt models, governed metrics, semantic layer definitions, and lineage-aware data products. The course uses inline SQL/YAML exercises, diagrams, quizzes, and revealable answers so learners can practice without setting up a GitHub repository.
01
Inspect the architecture
Start every module with the system model: components, trust boundaries, data flow, and the production problem it solves.
02
Practice the failure mode
Labs and exercises focus on the operational edge cases that separate tutorial knowledge from production confidence.
03
Ship with judgement
Production notes, common mistakes, and tradeoffs make the course useful when you are designing or reviewing real systems.
Good fit
Who should take this course?
This course is written for engineers who need practical production context, not abstract theory.
Freshers who know basic SQL and want to enter data engineering or analytics engineering
Backend engineers moving toward data platform work
Data analysts who want software-engineering discipline with dbt
Students who get confused by warehouse, dbt, metrics, and semantic layer terminology
Junior data engineers who want to build trustworthy models, not just pipelines
AI builders who need governed data and metrics before using LLMs over warehouse data
Curriculum
Full course path
16 modules, 16 inline exercises, 28+ hours of production-focused learning.
Instructor
Vishal Anand
Senior Product Engineer & Tech Lead
Creator of DRF API Logger and author of production-focused CodersSecret courses. Vishal teaches engineering through concrete systems, diagrams, operational failures, and practical tradeoffs.
FAQ
Questions before you start
Topics