DBT Training
Build Reliable Analytics Transformation Pipelines with dbt
Build practical, job-ready skills in dbt (data build tool) for SQL-based data transformation and analytics engineering. Learn modular data models, testing, documentation, Jinja, macros, incremental processing, and reliable deployment workflows through hands-on exercises.
Enquire About TrainingCourse Overview
DBT Training is designed to take learners from the fundamentals of Apache Spark and dbt to advanced analytics engineering data engineering and production-oriented development.
You will work with SQL modeling, SQL Models & Data Marts, incremental models, Structured Streaming, dbt workflows, Azure Data Lake Storage, Git, CI/CD and performance optimization concepts. The learning path focuses on the practical responsibilities of a dbt developer and data engineer.
Course Highlights
Gain practical dbt development expertise through a comprehensive curriculum covering Apache Spark, SQL modeling, incremental models, data engineering, analytics engineering architecture, Azure integration, performance optimization, and real-world project implementation.
SQL Transformation & Modeling
Build clean, modular SQL models using reusable transformations, joins, aggregations and analytics logic.
Sources & Model Lineage
Organize sources and models, define dependencies with ref(), and understand lineage through the project DAG.
Reliable Transformation Workflows
Build batch, incremental, and streaming data pipelines using practical dbt data engineering patterns.
SQL Models & Data Marts
Develop analytical queries, tables, views, SQL workloads, and data warehouse-style solutions using SQL Models & Data Marts.
Documentation & Testing
Document models and columns, test data assumptions, and make transformation logic easier to trust.
Incremental Processing
Use incremental models and appropriate materializations to make transformation workloads more efficient.
Git & Deployment
Understand version control, collaborative development, deployment workflows, Git integration, and CI/CD concepts for dbt projects.
Trainer Experience
Learn from an industry professional with 19+ years of experience in database, data engineering, analytics, and modern data technologies.
Why Choose Our DBT Training?
The training follows a structured path from fundamentals to advanced development, with emphasis on practical data engineering tasks and production-oriented dbt solutions.
Hands-On Development
Practice SQL modeling, testing, documentation and reusable transformations through practical exercises.
Beginner to Advanced
Progress from dbt fundamentals to modular models, testing, macros and deployment practices.
Real-World Data Engineering
Understand model dependencies, incremental transformations, data quality checks and deployment workflows used in modern analytics teams.
Performance Engineering
Learn how model design, materializations, incremental processing and compiled SQL affect transformation workloads.
Warehouse & BI Integration
Understand how dbt models use configured warehouse connections and declared source data.
Production Development Practices
Learn Git, CI/CD, deployment workflows and reusable engineering practices for dbt projects.
Who Can Join?
Freshers & Graduates
Students and graduates who want to build a career in SQL-based analytics engineering.
SQL & Database Professionals
SQL developers, database professionals and developers looking to move into modern data platforms.
ETL & Data Engineers
ETL developers and data engineers who want to develop modular SQL transformation and testing skills.
Analytics Professionals
Analytics and BI professionals who want to understand scalable data processing and analytics engineering architecture.
Data Platform Professionals
Azure professionals who want to expand their skills into dbt and data engineering.
Python Developers
Developers with SQL knowledge who want to learn modern analytics engineering practices.
Career Opportunities
dbt Developer
Build, test, document and maintain SQL transformation models using dbt.
Data Engineer
Build dependable transformation workflows and curated datasets for analytics and reporting.
Data Model Developer
Organize staging, intermediate and mart models into a maintainable analytics project.
Analytics Engineer
Develop reusable SQL models and transformation logic for analytical datasets.
Data Platform Engineer
Work on model automation, testing, documentation, deployment and production support.
Cloud Data Engineer
Develop warehouse-based transformation solutions using dbt and supported data platforms.
What You Will Gain
Build readable SQL models using CTEs, joins, aggregations, window functions and reusable transformations.
Organize staging, intermediate and mart models using clear naming and dependency conventions.
Create tested transformation workflows, including incremental models and data quality checks.
Define and document data sources, model dependencies and warehouse transformation logic.
Review model design and materializations to improve maintainability and warehouse efficiency.
Apply Git, CI/CD, automated checks and production-oriented deployment practices to dbt projects.
Trusted DBT Training
Learn from an experienced training institute with a practical, technology-focused approach to professional training.
100+
Training Programs
50+
Practical Learning Activities
20+
Technology & Data Platform Areas
19+
Years Industry Experience
STUDENT SUCCESS STORIES
What Our Students Say
Student Success Story
DBTStudent feedback can be added here after receiving approval to publish the testimonial.
Student Success Story
Data EngineeringAdd an approved student review describing the learning experience, practical exercises and outcomes.
Student Success Story
dbt & Analytics EngineeringThis section is ready for your verified student success stories and testimonials.
Upcoming Free Demo Class
Live dbt Training Sessions
Training Image 1
Space reserved for a live dbt training session image.
Training Image 2
Space reserved for a live dbt training session image.
Training Image 3
Space reserved for a live dbt training session image.
Training Image 4
Space reserved for a live dbt training session image.
Training Image 5
Space reserved for a live dbt training session image.
Training Image 6
Space reserved for a live dbt training session image.
Training Image 7
Space reserved for a live dbt training session image.
Training Image 8
Space reserved for a live dbt training session image.
Training Image 9
Space reserved for a live dbt training session image.
Course Syllabus
Learn dbt step by step, from SQL transformation fundamentals to modular models, testing, documentation, automation, and production deployment.
01. Introduction to dbt & Analytics Engineering
FOUNDATION
- What is dbt (data build tool)?
- Role of dbt in modern analytics engineering
- ELT versus ETL concepts
- How dbt works with a data warehouse
- Project structure and configuration files
- Core dbt workflow and command-line basics
- Understanding development and production environments
02. SQL Refresher for Data Transformation
SQL
- SELECT, filtering, sorting and aggregation
- Joins, CTEs and subqueries
- Window functions and analytical calculations
- Null handling and data type conversion
- Reusable SQL patterns for transformation projects
- Writing readable and maintainable SQL
03. Models, Sources & Dependencies
MODELING
- Creating and configuring dbt models
- Model naming and folder conventions
- Using ref() to define model dependencies
- Declaring sources and using source()
- Understanding the Directed Acyclic Graph (DAG)
- Staging, intermediate and mart model layers
- Materializations: view, table, ephemeral and incremental
04. Data Quality Tests
TESTING
- Purpose of testing in analytics engineering
- Built-in tests: unique, not_null, accepted_values and relationships
- Adding tests to models and source columns
- Writing singular SQL tests
- Generic tests and reusable validation patterns
- Running tests and interpreting failures
- Test selection and data quality workflows
05. Documentation & Project Discovery
DOCUMENTATION
- Documenting models, columns and sources
- YAML configuration files
- Generating and serving dbt documentation
- Exploring model lineage and dependencies
- Adding descriptions and business context
- Maintaining documentation with code changes
06. Jinja, Macros & Reusability
ADVANCED SQL DEVELOPMENT
- Jinja basics: variables, expressions and control flow
- Using Jinja in SQL models
- Creating and calling macros
- Macro arguments and reusable logic
- Built-in dbt functions and packages
- When to use macros and how to keep them maintainable
07. Snapshots & Incremental Models
CHANGE TRACKING
- Snapshot use cases and configuration
- Tracking changes in source records
- Snapshot strategies and historical data concepts
- Incremental model design
- Unique keys and incremental filters
- Full refresh versus incremental runs
- Handling late-arriving and updated records
08. Analytics Modeling & Data Marts
DATA MODELING
- Staging, intermediate and mart layers
- Fact and dimension tables
- Business keys and surrogate keys
- Star schema fundamentals
- Reusable business logic and metrics
- Designing clean downstream datasets for BI tools
09. dbt Commands & Workflow
DEVELOPMENT WORKFLOW
- Common dbt commands and project validation
- Running models and selecting resources
- Building models and tests together
- Using selectors and tags
- Debugging compilation and runtime errors
- Environment variables and project configuration
10. Git, CI/CD & Deployment
PRODUCTION PRACTICES
- Git basics and collaborative development
- Branching and pull request workflow
- Development versus production targets
- Automating builds and tests in CI
- Deployment practices and environment configuration
- Reviewing changes before production runs
- Monitoring runs and troubleshooting failures
11. Performance & Best Practices
OPTIMIZATION
- Understanding compiled SQL
- Choosing appropriate materializations
- Reducing unnecessary model dependencies
- Incremental processing and warehouse cost awareness
- Model organization and naming conventions
- Code review, maintainability and reusable patterns
12. End-to-End dbt Project
CAPSTONE
- Set up a dbt project connected to a supported warehouse
- Build staging, intermediate and mart models
- Define sources, dependencies and tests
- Add documentation and lineage
- Implement incremental processing or snapshots where appropriate
- Use Git-based collaboration and deployment workflow
- Validate and present the finished transformation project
Sample DBT Training Videos
Sample Video 1
Space reserved for an DBT training video.
Sample Video 2
Space reserved for an DBT training video.
Frequently Asked Questions
What is dbt?
dbt (data build tool) helps data teams transform data in a warehouse using SQL, while supporting modular models, testing, documentation and dependency management.
Who is this DBT Training for?
The course is suitable for SQL developers, ETL professionals, data analysts, data engineers and aspiring analytics engineers who want to build maintainable transformation projects.
Do I need prior dbt experience?
No prior dbt experience is required. Basic SQL knowledge is recommended because the training focuses on SQL-based transformations.
Will I learn SQL modeling and testing?
Yes. You will learn to build modular SQL models, define dependencies, add tests, document models and use reusable macros.
Does the course cover incremental models?
Yes. The curriculum covers incremental model configuration, unique keys, incremental filters, full refresh concepts and handling changed records.
Does the training include data quality testing?
Yes. You will learn built-in tests such as unique, not_null, accepted_values and relationships, along with SQL-based tests and troubleshooting.
Will documentation and Git be covered?
Yes. The curriculum includes model and column documentation, lineage, Git collaboration, CI/CD concepts and deployment workflows.
Is the training available online?
Yes. The course can be offered online or offline, subject to the available batch schedule.
Ready to Learn DBT?
Learn dbt step by step, practice real-world data engineering scenarios and build scalable analytics engineering solutions.
Discuss Your Training Requirements