DBT - Data Build Tool Course in chennai

Module 1: Introduction to Analytics Engineering and dbt

• Analytics engineering concepts: transforming raw data into reliable datasets
• dbt overview: what dbt does and where it fits in the modern data stack
• dbt Core vs dbt Cloud: differences, setup, and use cases
• Connecting dbt to data warehouses: Snowflake, BigQuery, Redshift, and Databricks

Module 2: dbt Project Structure and Models

• dbt project anatomy: dbt_project.yml, profiles.yml, and folder structure
• Writing your first dbt model: SELECT-based SQL transformations
• Model materialisation types: table, view, incremental, and ephemeral
• Ref function: building dependency chains between models

Module 3: Sources, Seeds, and Staging Models

• Defining sources in schema.yml for raw data documentation
• Source freshness checks and alerting configuration
• Loading CSV seed files for static reference data
• Staging model patterns: renaming, casting, and light transformation

Module 4: Testing and Data Quality

• Built-in dbt tests: unique, not_null, accepted_values, and relationships
• Writing custom singular and generic tests
• dbt-expectations and Great Expectations integration
• Test severity levels and running tests in CI pipelines

Module 5: Documentation and Data Lineage

• Writing model and column descriptions in YAML
• Generating and serving the dbt docs site
• DAG visualisation: understanding upstream and downstream dependencies
• Documenting business logic for data consumers

Module 6: Advanced dbt Features

• Jinja templating: macros, variables, and conditional logic
• dbt packages: installing and using dbt-utils and dbt-audit-helper
• Incremental models: strategies for handling late-arriving data
• Snapshots: capturing slowly changing dimensions (SCD Type 2)

Module 7: dbt Cloud, Deployment, and CI/CD

• dbt Cloud IDE: development, scheduling, and job management
• dbt Cloud jobs: building scheduled and triggered runs
• Integrating dbt with GitHub Actions for CI/CD pipelines
• Slim CI: running only modified models and downstream tests

Module 8: Capstone Project and Assessment

• End-to-end analytics engineering project: raw to mart layer
• Data quality test suite implementation and documentation
• dbt Cloud deployment pipeline setup and job scheduling
• Final assessment and course certification

Experience in the Industry Learn from analytics engineers who have built production dbt projects on Snowflake, BigQuery, and Databricks for e-commerce, fintech, and SaaS companies managing complex multi-layer data transformations.

Backgrounds at the Top Our dbt trainers have worked at data-first technology companies and analytics consultancies where dbt is the standard tool for scalable, version-controlled SQL transformation pipelines.

Clear & Effective Teaching dbt models, sources, testing, Jinja macros, snapshots, incremental strategies, and dbt Cloud deployment are explained clearly with real analytics engineering examples and data warehouse context.

Hands-On Learning Focus Students build complete dbt projects from staging through mart layers, implement data quality tests, write macros, and deploy pipelines through structured project-based labs.

Up-to-Date Knowledge Trainers keep content current with the latest dbt Core and dbt Cloud releases, dbt Mesh architecture for multi-project deployments, and evolving analytics engineering best practices.