Dbt packages

Any kind of contribution is greatly encouraged and appreciated. For making a contribution, please check the contribution guidelines first!

Software engineers frequently modularize code into libraries. These libraries help programmers operate with leverage: they can spend more time focusing on their unique business logic, and less time implementing code that someone else has already spent the time perfecting. In dbt, libraries like these are called packages. As a dbt user, by adding a package to your project, the package's models and macros will become part of your own project. This means:.

Dbt packages

Creating packages is an advanced use of dbt. If you're new to the tool, we recommend that you first use the product for your own analytics before attempting to create a package for others. Packages are not a good fit for sharing models that contain business-specific logic, for example, writing code for marketing attribution, or monthly recurring revenue. Instead, consider sharing a blog post and a link to a sample repo, rather than bundling this code as a package here's our blog post on marketing attribution as an example. We tend to use the command line interface for package development. The development workflow often involves installing a local copy of your package in another dbt project — at present dbt Cloud is not designed for this workflow. We recommend that first-time package authors first develop macros and models for use in their own dbt project. Once your new package is created, you can get to work on moving them across, implementing some additional package-specific design patterns along the way. When working on your package, we often find it useful to install a local copy of the package in another dbt project — this workflow is described here. Use our dbt coding conventions , our article on how we structure our dbt projects , and our best practices for all of our advice on how to build your dbt project.

You can specify a package using one of the following methods, depending on where your package is stored. Some package maintainers may wish to push prerelease versions of packages to the dbt Hub, in order to test out new functionality or compatibility with a new version of dbt. This allows them to leverage it to fulfill a portion of their requirement or gain insight into dbt packages they can develop for a similar requirement, dbt packages.

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Dbt packages

Packages are the easiest way for a dbt user to contribute code to the dbt community. This is a belief that I hold close as someone who is a contributor to packages and has helped many partners create their own during my time here at dbt Labs. The reason is simple: packages, as an inherent part of dbt, follow our principle of being built by and for analytics engineers. You can either share your package with the community or just use it among your teams at your org. So I challenge you after reading this article to test out your skillsets, think about the code that you find yourself reusing again and again, and build a package. A dbt package is basically a mini-dbt project. It can contain macros that help you write something in SQL in significantly less lines.

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Data Engineering. If the packaged project is instead nested in a subdirectory—perhaps within a much larger mono repo—you can optionally specify the folder path as subdirectory. Get Started. If you want to completely uninstall a package, you should either:. What Are dbt Packages? Subscribe to our newsletter. If your package has only been written to work for one data warehouse , make sure you document this in your package README. Use it when you want to include both projects and non-private dbt packages. Custom properties. Can't see your avatar? Use Cases.

Software engineers often use modularised code libraries, empowering them to focus on business logic while leveraging preexisting, perfected code for efficiency. They enable efficient problem-solving as shared analytic challenges are common across organisations. They offer a number of advantages, but the one that warrants our attention first is reusability.

It's critical that you test your models and sources. For example, the function name and order of arguments to calculate the difference between two dates varies between Redshift, Snowflake and BigQuery, and no similar function exists on Postgres! Updated dbt Core. As shown below, add the package s with the proper syntax. This pattern can be seen most packages, including the audit-helper and snowplow packages. Hub packages require a version to be specified — you can find the latest release number on dbt Hub. While we generally try to avoid making breaking changes to these packages, they are sometimes unavoidable. Technology Partners. By Arnab Mondal. The easiest way we've found to do this is to use GitHub Pages. The release notes should contain an overview of the changes introduced in the new version.