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Create transparency in your data warehouse.


The Data Build Tool (dbt) is a software program that takes over the transformation step in an ELT (Extract, Load and Transform) process. In this process, dbt is linked to the data warehouse and, based on the source data, can manage the rest of the database structure and implement the modeling of the data. dbt is currently THE open source program for data transformation and brings many software engineering practices to the world of data. It enables smooth collaboration within the data teams and beyond.

We, the team at Hopmann Marketing Analytics, are competent dbt consultants and are constantly expanding our knowledge through a wide range of projects in this area. We are happy to provide you with comprehensive advice – from evaluation and implementation of the software through to training.



We are happy to advise you on evaluating whether implementing dbt is worthwhile for your company.


We are happy to take care of the complete implementation of the software and conduct small workshops where we show you further potential of dbt in your company.


We offer customized training on how to effectively use dbt, from the first-time use of the software to uncovering optimization potentials through experiences already gathered with the tool in your company.


  • 1.

    Version control with CI/CD

    dbt can be very well organized in the cloud and takes full advantage of Git. This enables version-controlled feature releases and easy coordination within the team with pull requests, branches, issues, and release versions. Moreover, dbt provides the possibility of a personal development environment where changes can first be tested before being transferred to the production environment.

  • 2.

    Programmatic extension of SQL

    dbt uses a mix of SQL to build models, YAML for configuration, and Jinja to extend SQL with additional functions. This creates a fundamentally simple structure that requires little prior knowledge to operate dbt effectively, while on the other hand offering almost unlimited configurability. dbt thus also makes it possible to apply the DRY principle, where code that is used in logically separate elements is defined in one place only and needs to be modified at only one place in case of changes.

  • 3.

    Test functionality

    The testing of data and transformations is becoming increasingly important in order to be sure of the quality of the data provided. dbt provides a range of options for detecting errors before they occur to the user – whether due to faulty transformations or faulty source data.

  • 4.

    Built-in documentation

    To keep the documentation as up-to-date as possible, the documentation in dbt lives right alongside the transformations. This information can then be displayed on its own page and enriched with many meta-information. Among other things, dbt automatically creates a lineage graph that represents the dependencies between the transformations.

  • 5.

    Modularly expandable with packages

    There is a dedicated hub where packages provided by the community, the creators of dbt, or various other providers are available. These can include, for example, custom tests, cross-database syntax, logging, or entire transformations for commonly used data sources.