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HMA ACADEMY

Mastering dbt: Intensive course on data modeling

Price
2 days: 1.760 € (net) p. P. / 3 days: 2.640 € (net) p. P.
Level
Data analysts, data engineers and anyone interested in modern data modeling

This HMA Academy training is all about modern data modeling with dbt (data build tool). The intensive course is designed to provide participants of all experience levels with the skills to develop and manage efficient and scalable data pipelines. Over the course of the workshop, you will not only learn how to use dbt in practice, but also gain in-depth knowledge in the areas of data modeling, testing and documentation. The course covers a wide range of topics including setting up and configuring dbt, creating and managing transformations as well as advanced techniques such as writing your own macros and using the dbt cloud. Hands-on exercises and real-world project work will allow you to directly apply what you have learned, giving you a deep understanding of how dbt works and its benefits. Whether you are a data analyst, business intelligence professional or data engineer, this course will help you enhance your data management skills and make your projects more efficient.

The course is available as either a 2 or 3-day training course. The 2-day dbt training focuses on basic concepts and techniques suitable for smaller projects and teams, while the 3-day training includes additional, advanced modules specifically designed to meet the needs of large dbt projects, large teams and large organizations.

2 or 3-day training / At our training center at Schliersee, in Munich or in-house at your company (on request) / 3-8 participants / Suitable for all departments and industries

Day 1

This intensive training day is designed to give you a general introduction to the Marketing Data Stack (MDS) combined with an in-depth introduction to dbt (Data Build Tool). You will learn to initialize a dbt project from scratch, apply effective versioning control practices and master basic dbt concepts such as models, sources and tests. The theory sessions will be complemented by practical exercises.

Marketing Data Stack

In this session, we will give an introduction to the modern data stack, highlighting the roles of the data engineer and the analytics engineer and how these disciplines are revolutionizing data analysis and processing in companies.

dbt project initialization

You will learn how to initialize a dbt project, including the creation and organization of .yml and .sql files, the integration into a data warehouse and the implementation of version control to secure and coordinate project development.

Version control

This session covers the basics of Git, including setting up and managing different environments, working with feature branches, and conducting code reviews to ensure high code quality and effective team collaboration.

dbt fundamentals

We dive deep into the fundamentals of dbt, explore models, sources and modularity, and discuss how testing and materialization can be used effectively to build powerful, reusable and maintainable data pipelines.

Day 2

On this intermediate training day, we will focus on advanced techniques and features in dbt that are essential to maximize the efficiency, monitoring and automation of data pipelines. You will gain deep insights into the use of Jinja in dbt, advanced materialization strategies, enhanced testing procedures as well as setting up notifications and monitoring.

Jinja, macros and packages

In this session you will learn how to use Jinja templates and macros within dbt to introduce dynamic logic into your code. You will also find out how to use and develop your own packages to increase reusability and modularity.

Advanced Materializations

This session will deepen your understanding of advanced materialization options in dbt, in particular the implementation and optimization of incremental materializations. You will learn how to efficiently process large amounts of data while optimizing the runtime and resource usage of your projects.

Advanced Testing

You will be introduced to the advanced testing techniques available in dbt, including custom tests and the use of macros to create complex test logic. This knowledge will help you to ensure data integrity and quality in dynamic and complex data environments.

Alerting and Monitoring

In the last session of the day, we will focus on setting up notifications and monitoring dbt projects. By using APIs, webhooks and automated notification systems, you will learn how to effectively monitor the status of your data pipelines and react when necessary.

Day 3 (optional)

On the last day of our dbt training, we will focus on the practical application and implementation of dbt projects in production. You will discover the effective methods for deployment, data modeling, the integration of Python in dbt, as well as the use of dbt Mesh and the implementation of a semantic layer. This day concludes the training with an in-depth look at how to use dbt effectively in a fully operationalized environment.

Deployment

In this session, we will discuss the best practices and strategies for successfully deploying dbt projects. You will learn how to safely transition dbt projects into production environments, including the use of CI/CD pipelines and version control management.

Data modelling and modularization

This session will focus on data modeling techniques and modularization within dbt. We will explore how to create effective, reusable and scalable data models that are the foundation for analysis and reporting in an organization.

Python models

You will learn how to integrate Python models into dbt to implement a complex data processing logic that goes beyond traditional SQL operations. These skills expand the range of data science applications that can be realized with dbt.

dbt Mesh

In this session, we will introduce dbt Mesh, a method for networking and managing dependencies in large dbt projects. You will learn how dbt Mesh is used to optimize the project structure and improve collaboration between different teams.

Semantic Layer

We conclude by looking at the implementation of a semantic layer in dbt that serves as a bridge between raw data models and end-user applications. We discuss how this layer simplifies data accessibility and usage while maintaining data integrity.

Goal of the training

The goal of the training is to gain a deep understanding of the data modeling techniques, testing strategies and documentation practices required for the efficient use of dbt. You will learn how to construct complex data models, ensure processing quality through testing and improve team collaboration with clear documentation. After the training, you will be able to use dbt independently in your projects to optimize and automate data processing and analysis. This leads to better, data-driven decisions in your company.

Your trainer

HMA_Team_DR-Simon-Stusak

Dr. Simon Hannemann is Senior Manager Data Engineering at Hopmann Marketing Analytics and a certified expert for various data engineering tools. In his day-to-day work as a consultant and team lead, he is responsible for the successful development of ETL processes for data integration and transformation. He is very keen to pass on his in-depth knowledge and extensive practical experience in his training courses.

Show trainer profile

Individual training for your company

Would you like to implement dbt in your company and are you looking for a hands-on training course for your team? Then we are the right partner for you. We have already implemented several individual dbt training courses for well-known customers. Whether in-house at your company, at our training center at Schliersee or remotely – we are as flexible as your requirements.

Dates Schliersee

03.-05.06.2024

November 2024

Dates Munich

June 2024

November 2024

Interested?
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