By completing this course, learners will:

• Install and configure dbt projects.

• Build modular data models with SQL and Jinja.

• Implement data tests and quality checks.

• Document models and lineage for transparency.

• Deploy dbt workflows in production environments.

• Integrate dbt with orchestration and BI tools.

Course Syllabus

Module 1: Introduction to dbt

• What is dbt?

• dbt vs ETL/ELT tools

• Installing dbt and setup

Module 2: Core Concepts

• dbt projects, models, and DAGs

• SQL + Jinja templating

• Data lineage and dependency graphs

• Configurations and properties

Module 3: Building Models

• Creating staging and marts layers

• Incremental models

• Ref and source functions

• Best practices for modular SQL

Module 4: Testing & Quality

• Built-in dbt tests (unique, not null, accepted values)

• Custom tests with SQL

• Data quality workflows

• Test automation in pipelines

Module 5: Documentation & Lineage

• Auto-generating dbt docs

• Model descriptions and metadata

• Lineage visualization in dbt docs

• Best practices for documentation

Module 6: Advanced Features

• Macros and reusable SQL logic

• Seeds and static datasets

• Snapshots for slowly changing dimensions

• Hooks and operations

Module 7: Warehouse Integrations

• dbt with Snowflake

• dbt with BigQuery

• dbt with Redshift

• dbt with Databricks and Postgres

Module 8: Deployment & Orchestration

• dbt Cloud vs dbt Core

• Running dbt jobs in CI/CD pipelines

• Orchestration with Airflow, Dagster, and Prefect

• Monitoring and logging

Module 9: Real-World Projects

• Building a sales analytics mart

• Customer churn model with dbt + BigQuery

• Financial reporting pipelines

• Marketing attribution models

Module 10: Best Practices & Future Trends

• The rise of analytics engineering

• dbt vs legacy ETL tools

• Data governance and compliance

• The future of dbt in the modern data stack

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in dbt and analytics engineering. This certification demonstrates readiness for roles in data analytics, data engineering, and BI development.

dbt skills prepare learners for roles such as:

• Analytics Engineer

• Data Analyst (SQL-heavy workflows)

• Data Engineer (ELT pipelines)

• BI Developer (dashboards & reports)

• Data Platform Engineer

dbt is now a cornerstone of the modern data stack, used by companies worldwide (Fivetran, Snowflake, dbt Labs ecosystem), making it one of the most in-demand data tools.

1. What is dbt?
A data transformation framework that lets analysts and engineers write modular SQL models with testing, documentation, and version control.

2. How does dbt differ from ETL tools?
dbt focuses only on the T (Transform) step inside the warehouse, not extraction or loading.

3. What programming language does dbt use?
SQL with Jinja templating for modularity and reusability.

4. What are dbt models?
SQL files that define transformations, compiled into queries and materialized as views or tables in the warehouse.

5. What are seeds in dbt?
CSV files loaded into the warehouse as tables for static reference data.

6. What are snapshots in dbt?
A feature for tracking historical changes in source tables (Slowly Changing Dimensions).

7. What warehouses does dbt support?
Snowflake, BigQuery, Redshift, Databricks, Postgres, and others.

8. What are the benefits of dbt?

• Brings software engineering practices (testing, version control) to analytics

• Strong data lineage visibility

• Works directly inside warehouses (no extra infra)

9. What are challenges with dbt?

• SQL-only; not suited for complex ML/streaming tasks

• Requires warehouse performance tuning

• Governance and scaling in very large teams

10. Where is dbt being adopted?
By analytics-driven companies, SaaS platforms, finance, retail, and enterprises adopting the modern data stack with cloud warehouses.