dbt Bootcamp Basic to Advance

By Sahil In Grow Data Skills

Self Paced
English
Completion Certificate

Watch demo class

1 Year

Validity (From the date of Enrollment)

30 Hours

Duration

Sessions

Projects

INR 4000

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This course includes

Content Duration30 Hours
Total Video Sessions12
Total Industry Projects3
Quality Assignments & quizzes after each module
Interview preparation guide
Dedicated placement assistance
Resume & Linkedin profile making
Doubt support on private discord community
Certificate of completion

Tech stack you'll learn

Course Content

ETL Fundamentals & Real-world scenarios for ETL pipelines
Data Storage Solutions Overview
Row-oriented storage vs column-oriented storage
Role of Data Marts and Data Lakes in engineering
Evolution of database systems over decades
Key data warehousing architectures: centralized vs modular
Overview of popular ETL solutions
Commercial vendors and their specialties
Open-source frameworks
Cloud-native ETL platforms
Custom-built pipelines
Factors guiding tool selection (cost, scalability, ecosystem)
ETL vs ELT comparison with practical examples
Overview of competing ELT frameworks
Introduction to dbt and its position in modern ELT workflows
Installation & Setup walkthrough - dbt cloud & snowflake

Configuring a dbt Cloud account
Provisioning a Snowflake / BigQuery / Redshift warehouse
Setting up GitHub for version control and collaboration
Linking dbt Cloud with the data warehouse
Alternative CLI setup for local development
Creating the first dbt model from scratch
Exploring the project folder structure in detail
Understanding configuration files (dbt_project.yml & profiles.yml)
Exploring materialization types and when to use each
Intro to YAML syntax for dbt
Defining and referencing data sources
Best practices for organizing models and directories

Using dbt Seeds for static datasets
How to reference seed data in models
dbt Analysis for ad hoc explorations
Creating analysis queries for business insights
Deep dive into materializations
view, table, incremental, ephemeral
How to configure each type efficiently
Understanding dbt artifacts
manifest.json
catalog.json
sources.json
Comparing compiled vs executed SQL
Mastering dbt commands & CLI options
Understanding the buttons in dbt Cloud
Command alternatives in terminal
Graph operators and model selection strategies
Introduction to Jinja templating
Variables, loops, conditions, and comments
Writing reusable logic in Jinja
Macros in dbt
Creating and invoking macros
How macros are compiled and executed
Practical example: dynamic column renaming
Built-in Jinja utilities: invocation_id, adapter, utils
Dynamic SQL generation with Jinja for flexible models
Incremental models
Using is_incremental logic
Incremental configurations and best practices
Example: merge strategy in Snowflake with audit columns

Advanced Jinja techniques
Nested loops, dynamic expressions, string transformations
Using range(), upper(), and other functions for dynamic queries
Incremental model strategies
Merge, append, and delete-insert approaches
Tagging and custom configurations
Organizing models with tags for selective runs
Deploying dbt projects
Environments, job scheduling, and run commands
Using snapshots for historical tracking
Naming conventions and project structuring best practices
dbt AI Integration
Using mcp-server with the Claude to automatically find insights from dbt-models
Ask AI to create staging models, marts, schema.yml tests, or refactor SQL inside your dbt project
Generate dbt code automatically using claude
Read models, lineage, tests, and YAML metadata from your dbt project using AI

dbt Testing
Built-in, singular, generic, and custom tests
Monitoring performance and logs
greatexpectations overview
Documentation generation
Hosting dbt docs, hyperlinking models
Versioning and team collaboration
Debugging strategies for common dbt errors

Project 1: E‑commerce Unified Data Layer Creation

Approach:
- 📍 Load raw data from Snowflake
- 📍 Set up dbt project and pipelines
- 📍 Transform raw data into structured fact and dimension tables
- 📍 Implement incremental load with SCD2 snapshots
- 📍 Test pipelines using generic and custom dbt tests
- 📍 Generate documentation and tag models

Project 2: Healthcare Analytics Warehousing Simulation

Approach:
- 📍 Load raw EMR/claims data into warehouse
- 📍 Clean, detect anomalies, and validate data
- 📍 Build patient dimension and fact tables
- 📍 Implement cohort analysis and churn metrics
- 📍 Set up snapshots and incremental models
- 📍 Unit testing and documentation

Project 3: EdTech ELT Modernization Pipeline (Bonus)

Approach:
- 📍 Set up dbt with CI/CD pipeline
- 📍 Clean and transform data with low-code SQL
- 📍 Document the data model using YAML
- 📍 Run automated unit tests (generic & custom)
- 📍 Schedule pipeline deployment jobs

Crafting resumes that grab recruiter attention
Strategies to succeed in technical interviews
Optimizing LinkedIn profiles for data roles
How To Expand Your Professional Network On Linkedin
Leveraging job portals and approaching referrals

Course Schedule

Mode of the Course:

Self Paced

Course Duration:

30 Hours

Total Video Sessions:

Total Projects:

Validity:

1 Year (From the date of enrollment)

Prerequisite:

Passion And Willingness To Learn

⚠️ Important Notice :

The video may not work on Linux due to DRM restrictions. It is only accessible on Chrome when using Windows or macOS.

Workaround: To access the video on Linux, you can create a Windows virtual machine (VM) and watch the video through the VM. Alternatively, you can use our Android or iOS application to view the video on your mobile device.

Instructor

Sahil Choudhary

Sahil is a Google Certified Data Analyst and a Senior Data Analyst at Sigmoid Analytics. He has worked with startups and Fortune 500 companies, helping them make scalable data-driven decisions. He mentored over 350 students in analytics domain and developed third party products and has written multiple whitepapers for BI applications.