dbt on Databricks

Unlock the Power of Data Transformation: dbt on Databricks

Elevate your data engineering skills with this comprehensive course focused on dbt on Databricks. Learn to master data transformation using the data build tool (dbt) within the powerful Databricks platform, leveraging the scalability of Apache Spark for efficient data pipelines.

Why dbt on Databricks?

While Databricks provides robust data processing, dbt enhances your workflow by offering a structured framework for version-controlled SQL transformations. This combination delivers cleaner, more maintainable, and reusable code, all while harnessing the power of Spark for scalable data pipelines.

Course Highlights:

• dbt & Key Concepts: Gain a deep understanding of dbt, Jinja templating, and YAML configuration. Learn how these elements work together to streamline your data transformation processes.
• Environment Setup: Follow step-by-step instructions to configure dbt Cloud with Databricks, implement version control best practices, and understand core components of your data pipelines.
• Data Modeling & Transformations: Explore multi-layer data architecture (Bronze, Silver, Gold models) and learn practical techniques for source referencing, schema configuration, and building efficient data pipelines using dbt commands.
• Advanced Testing & Validation: Implement robust data quality checks using generic tests and singular tests, progressing from syntax validation to comprehensive data tests. Integrate dbt packages like dbt_utils for enhanced functionality.
• Jinja, Macros & Custom Functions: Master Jinja syntax and macros to create reusable, scalable code. Dynamically manipulate data models, modify schemas, and develop custom logic for specific use cases.
• Materializations Explained: Understand various materialization strategies, including tables, views, incremental loads, and snapshots. Learn to apply these techniques to scenarios like SCD Type 2 for dimension tables and incremental updates for fact tables.
• Deployment & Production Workflows: Set up a production-ready dbt environment on Databricks, manage jobs, and deploy models seamlessly. Configure environment and target variables for streamlined CI/CD workflows.
• Developing with dbt Core: Experience the flexibility of dbt Core through local project setups, GitHub integration, and command-line navigation. Learn best practices for version control and collaboration.
• Troubleshooting & Advanced Techniques: Gain insights into resolving common connection issues, optimizing project performance, and scaling workloads on Databricks.

Who Should Take This Course?

This course is designed for data engineers, data analysts, and data architects with existing SQL skills who want to enhance their data transformation capabilities using dbt on Databricks. Basic knowledge of Python, Git, and cloud-based data environments is recommended.

Benefits of Learning dbt on Databricks:

• Build robust data pipelines: Learn to build, test, and deploy reliable data pipelines.
• Improve data quality: Implement comprehensive data validation techniques.
• Enhance code maintainability: Leverage version control and modular code.
• Scale data transformations: Utilize the power of Apache Spark on Databricks.
• Become a skilled data professional: Gain a competitive edge in the data industry.

This hands-on course provides practical skills through projects, exercises, and downloadable resources, empowering you to tackle complex analytics workflows and become a proficient data professional.