Build the Perfect Data Stack for Analytics Engineering
Published 11/2025
Created by Johanna Grossmann
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 51 Lectures ( 4h 3m ) | Size: 2.82 GB
DBT Production Setup : Data Modeling, Automation, CI/CD & Cost Optimization
What you’ll learn
Build a production-ready DBT project and understanding everything about DBT set up
Having best practices about data modeling and SQL code convention
How to automate everything that is too time consuming : testing, documentation and cleaning
Monitor and optimize data warehouse costs
Requirements
Know how to code in SQL
Maybe an idea of what DBT is
Description
Master the complete analytics engineering workflow by building a production-ready data stack from scratch using DBT (Data Build Tool), the industry-standard transformation framework trusted by data teams worldwide.This comprehensive course takes you from zero to advanced DBT practitioner, covering everything needed to build, deploy, and maintain scalable data pipelines in real-world production environments. You’ll learn the exact methodologies and best practices I’ve developed over 12+ years working across data analyst, data scientist, and analytics engineer roles in fast-growing startups.What you’ll build:Complete three-layer data architecture (staging, intermediate, mart) following software engineering principlesAutomated CI/CD pipelines with DBT Cloud for pull request testing and production deploymentsCost monitoring system to track and optimize data warehouse expensesSelf-healing testing framework with automated failure remediationProduction-grade incremental models for efficient data processingKey topics covered:DBT project setup with development/production environment separationGranularity-based data modeling that scales from thousands to billions of rowsVersion control workflows with Git and automated quality enforcement via pre-commit hooksSQL linting with SQLFluff and automated documentation generationWorkflow automation using Makefiles and GitHub ActionsQuery cost attribution and optimization strategiesAdvanced DBT features: seeds, macros, snapshots, and custom testsWho this is for: Data analysts transitioning to analytics engineering, data engineers building transformation layers, or anyone responsible for maintaining data pipelines serving hundreds of employees and millions of rows.By the end, you’ll have a battle-tested, production-ready data stack that actually works at scale—not just theory, but proven practices from real company environments.
Who this course is for
anyone interested in analytics engineering or in data platform construction
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