Dbt Cloud – Analytics Engineering Course in bangalore

Module 1: dbt Cloud Platform and Workspace Setup

• dbt Cloud overview: IDE, scheduling, CI/CD, and collaboration features
• Connecting dbt Cloud to Snowflake, BigQuery, Redshift, and Databricks
• Repository integration: GitHub, GitLab, and Azure DevOps setup
• dbt Cloud IDE: file explorer, lineage graph, and preview pane

Module 2: Advanced dbt Modelling Patterns

• Medallion architecture: bronze, silver, and gold layer design with dbt
• Intermediate and mart layer modelling best practices
• Model contracts: enforcing schema and column constraints
• dbt Semantic Layer: defining metrics and business logic centrally

Module 3: Advanced Jinja and Macros

• Jinja templating: loops, conditionals, and set operations in SQL
• Writing reusable macros for common transformation patterns
• dbt-utils macros: surrogate keys, date spines, and pivot utilities
• dispatch and adapter macros for cross-warehouse compatibility

Module 4: Advanced Testing and Monitoring

• Custom generic tests with Jinja and schema.yml arguments
• dbt-expectations: 30+ additional test types for data quality
• Elementary and re_data for anomaly detection and test monitoring
• Source freshness SLAs and alerting configuration in dbt Cloud

Module 5: dbt Cloud Jobs and Orchestration

• Job configuration: commands, schedule, and environment settings
• Slim CI: defer to production and run modified models only
• Job chaining: triggering downstream jobs via API
• Integration with Airflow, Prefect, and Dagster for orchestration

Module 6: dbt Mesh and Multi-Project Architecture

• dbt Mesh overview: connecting multiple dbt projects
• Cross-project references with public models
• Governance: model access control and group definitions
• Producer and consumer project design patterns

Module 7: dbt Semantic Layer and Metrics

• Defining metrics in dbt: measure, dimension, and filter configuration
• MetricFlow: querying metrics consistently across BI tools
• Integrating dbt Semantic Layer with Tableau, Looker, and Power BI
• Caching and performance for metric queries

Module 8: Capstone Project and Assessment

• Multi-layer dbt project: bronze to gold with contracts and tests
• dbt Mesh implementation: producer-consumer project setup
• dbt Semantic Layer metrics definition and BI tool integration
• Final assessment and course certification

Experience in the Industry Gain expertise from senior analytics engineers who have architected large-scale dbt Cloud deployments with advanced modelling patterns, dbt Mesh multi-project setups, and Semantic Layer integrations for enterprise data teams.

Backgrounds at the Top Our dbt Cloud trainers have led analytics engineering practices at data consultancies and technology companies building production-grade dbt projects on Snowflake, BigQuery, and Databricks.

Clear & Effective Teaching Advanced dbt modelling, Jinja macros, dbt-utils, dbt Cloud jobs, Slim CI, dbt Mesh, and Semantic Layer are explained with practical analytics engineering scenarios and real data transformation examples.

Hands-On Learning Focus Students build multi-layer dbt projects, write advanced macros, configure Slim CI pipelines, implement dbt Mesh, and define Semantic Layer metrics through comprehensive project-based labs.

Up-to-Date Knowledge Trainers keep content current with the latest dbt Core and dbt Cloud releases, dbt Explorer features, dbt Mesh architecture patterns, and evolving analytics engineering community best practices.