Data Build Tool (dbt) for Enterprise Analytics: From Foundations to Production-Ready Transformations

Module 1: Introduction to Analytics Engineering

Topics

• Role of the Analytics Engineer

• ETL vs ELT

• Modern data stack overview

• Where dbt fits in the data ecosystem

• Transformation layer in lakehouse/warehouse

Subtopics

• Collaboration between data engineers and analysts

• Shift from traditional ETL to ELT workflows

• Cloud data platforms and transformation-first approach

• Analytics engineering in modern data teams

Module 2: dbt Core Concepts

Topics

• dbt architecture

• Models, seeds, snapshots

• ref() function and DAG building

• Materializations (view, table, incremental)

• Project structure & configuration

Subtopics

• How dbt compiles SQL

• Dependency management through DAGs

• Incremental model logic

• YAML configuration basics

• Testing and documentation fundamentals

Module 3: Working with dbt Projects

Topics

• Installing dbt

• Connecting to a data warehouse

• profiles.yml configuration

• Environment setup (dev vs prod)

• Running dbt commands

Subtopics

• CLI usage and project initialization

• Target environments and credentials management

• dbt run, test, seed, snapshot, and docs commands

• Debugging and troubleshooting common issues

Module 5: Data Quality & Testing in dbt

Topics

• Built-in tests (not null, unique, relationships)

• Custom tests

• schema.yml configuration

• Testing strategies in enterprise environments

• Handling failed tests

Subtopics

• Generic vs singular tests

• Writing reusable custom test macros

• Test-driven analytics development

• CI integration for automated testing

• Debugging and resolving test failures

Module 6: Documentation & Lineage

Topics

• Auto-generated documentation

• Data lineage visualization

• Column-level descriptions

• Data contracts

• Metadata best practices

Subtopics

• Using dbt docs generate and dbt docs serve

• Understanding model-level and column-level lineage

• Maintaining documentation in YAML

• Enforcing schema consistency with contracts

• Improving discoverability and governance

Module 7: Incremental Models & Snapshots

Topics

• Incremental materializations

• Change Data Capture (CDC) patterns

• Snapshot strategies

• Handling late-arriving data

• Performance considerations

Subtopics

• is_incremental() logic

• Merge vs append strategies

• Slowly Changing Dimensions (SCD Type 1 & 2)

• Managing historical data

• Optimizing incremental model performance

Module 8: Performance Optimization

Topics

• Query optimization

• Partitioning strategies

• Indexing considerations

• Minimizing compute cost

• Managing model dependencies

Subtopics

• Warehouse-specific optimization techniques

• Clustering and partition pruning

• Reducing redundant transformations

• Efficient DAG structuring

• Cost-aware transformation design

Hands-on Labs

• Implement built-in and custom dbt tests

• Build a snapshot model

• Create an incremental transformation

• Generate and review the documentation site

• Optimize a slow-running model

This module sequence strengthens analytics engineering practices by focusing on quality, governance, performance, and scalable transformation design.

Module 9: dbt in Enterprise Architecture

Topics

• Integration with data warehouse/lakehouse

• Medallion architecture alignment

• Multi-environment strategy

• Separation of compute & storage

• Security considerations

Subtopics

• Positioning dbt in modern data platforms

• Bronze, Silver, Gold layer mapping

• Dev, staging, and production environments

• Warehouse resource management

• Role-based access control and credential handling

Module 10: CI/CD & Version Control

Topics

• Git workflows for dbt

• Branching strategies

• Automated testing in CI pipelines

• Deployment workflows

• Promotion from dev → prod

Subtopics

• Feature branch development

• Pull request review process

• Integrating dbt tests in CI

• Automated build and deployment pipelines

• Environment-based release management

Module 11: dbt Cloud & Orchestration

Topics

• dbt Cloud overview

• Job scheduling

• Environment variables

• Alerts & monitoring

• Integration with orchestration tools

Subtopics

• Managing jobs and runs

• Configuring environment-specific variables

• Monitoring job performance

• Failure notifications and alerting

• Integrating with Airflow or other orchestration systems

Module 12: Governance & Best Practices

Topics

• Access control & permissions

• Data ownership models

• Model versioning

• Documentation standards

• Audit and compliance readiness

Subtopics

• Role-based governance models

• Stewardship and accountability

• Semantic versioning practices

• Standardized documentation templates

• Compliance reporting considerations

Capstone: Enterprise Analytics Project

Project Components

• Design layered data model

• Implement incremental loads

• Add data quality checks

• Generate lineage documentation

• Present architecture and optimization strategy

Upon completion, participants will be able to:

• Design scalable transformation layers using dbt

• Implement modular, maintainable SQL models

• Apply data quality testing frameworks within dbt projects

• Build incremental and snapshot-based data pipelines

• Integrate dbt with CI/CD workflows and version control systems

• Align analytics engineering practices with enterprise governance standards

• Optimize data warehouse cost and query performance