In dbt cloud your documentation is made available within the UI itself. These Markdown files contain information about the SQL code in each file, as well as any comments or documentation you have written in your dbt project.ĭbt core docs serve is another command in the dbt CLI that allows you to view the documentation that has been generated by the dbt core docs generate command. When you run the dbt core docs generate command, dbt will create a docs/ directory in your project, and within that directory, it will create Markdown files for each model, test, and seed file in your project. This documentation includes information about your project’s models, tests, and seed files, as well as the relationships between these elements. In dbt cloud generating this documentation is equally straightforward ( ). dbt stand alone documentationĭbt docs generate is a command in the dbt CLI (command-line interface) that generates documentation for your dbt project. Fortunately, this is changing as more recent tools such as dbt (Data Build Tool) facilitates the creation and visualization of documentation. While the focus of current and new data tools has been put almost entirely on data processes rather than its documentation, it is no surprise that the latter is often overlooked. Additionally, the pressure put on data analysts to produce results within tight deadlines often depletes all capacities again deprioritizing the documentation process. This requires repetitive documentation in every step of the process, which ultimately compromises the quality of the documentation, or even its creation as a whole. The broader the BI stack becomes, the more difficult it gets to keep track and to document the nuances along the pipeline process. Furthermore, it helps to ensure that changes to the data structure are properly documented and communicated to all relevant stakeholders. It allows new users to quickly understand the data and how it is used. Moreover, proper documentation helps to improve the maintainability of the BI system over time. This includes information about the data sources, the data model, and the relationships between different data elements. Therefore, having a clear understanding of the data structure allows users to know how the data is organized and how it can be accessed and used. This is essential for making informed business decisions based on the data. In short, the appropriate documentation of data architecture is important in business intelligence because it helps to ensure that the data being analyzed is accurate and reliable. “The broader the BI stack becomes, the more difficult it gets to keep track and to document the nuances along the pipeline process” The importance of data architecture documentation in business intelligence In this article, we highlight the importance of the documentation of data architecture in business intelligence and also introduce a solution for model synchronization from dbt to Metabase which facilitates access to table relationships, model and column descriptions, semantic types, and exposures. This is a common misconception, as a rich documentation can save time (and money) in future processes. However, in practice, the former is often overlooked because the priority is heavily placed on delivering results in the form of reports and dashboards. In theory, the proper documentation of BI processes is as fundamental as the treatment of the data itself.
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