Dbt_utils

Welcome to this tutorial on surrogate key generation using dbt's utility dbt_utils. One of its many utilities is the generation of surrogate keys, which are essential for data modeling and analytics.

This post will run through how to install and use some popular and some unsung dbt utils in your project. The dbt-utils project in general is maintained by duh dbt Labs. Its contributors include a mix of developers from both dbt Labs and the wider data community. At the time of writing, the project repo on GitHub has a little under stars. This list is not exhaustive, but it encompasses most of the commonly used and widely used utils chosen by data teams working with dbt.

Dbt_utils

Full Changelog : 1. The original treated null values and blank strings the same, which could lead to duplicate keys being created. If needed, it's possible to opt into the legacy behavior by setting the following variable in your dbt project:. Our recommendation is that existing users should opt into the legacy behaviour unless you are confident that either:. If you use Postgres or Snowflake and need identical backwards-compatible behaviour, use dbt. Review the cross database macros documentation for the full list, or the migration guide for a find-and-replace regex. To continue to use it, add the below to your packages. Full Changelog : 0. This is the first release candidate for dbt utils 1. A full migration guide will accompany the final release, but here is the changelog:. Because of this, it is possible to opt in to the legacy behaviour by setting the following variable in your dbt project:.

Web macros are designed to help you work with web-related dbt_utils, such as URLs, dbt_utils. To continue to use it, add the below to your packages.

This dbt package contains macros that can be re used across dbt projects. Check dbt Hub for the latest installation instructions, or read the docs for more information on installing packages. Asserts the equality of two relations. Optionally specify a subset of columns to compare or exclude, and a precision to compare numeric columns on. Asserts that a valid SQL expression is true for all records. This is useful when checking integrity across columns.

Packages can be used to share common code and resources across multiple dbt projects, and can be published and installed from the dbt Hub , from GitHub or can be stored locally and installed by specifying the path to the project. Reusability: packages allow you to reuse code across multiple projects and models. This can save you a lot of time and effort, as you don't have to copy and paste the same code into multiple places. Collaboration: packaging your models in a package allows multiple people to work on the same models at the same time. You can use version control systems like git to manage changes to the models, and use tools like the dbt test command to ensure that the models are correct and reliable. Sharing: packaging your models or macros in a package allows you to share them with others. You can publish your package on the dbt Hub or on GitHub, and others can install and use your models in their own dbt projects. Managing: packages make it easier to manage your codebase.

Dbt_utils

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:.

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Full Changelog : 1. This macro returns an iterable Jinja list of columns for a given relation , i. Reload to refresh your session. The macro also has optional prefix and suffix arguments. We have chosen to collate all of the breaking changes into one hit; after this, functionality in dbt utils 1. This macro creates a cross-database way to produce a hashed surrogate key based on the fields you specify. At the time of writing, the latest version is 1. Contributors JoshuaHuntley, dbeatty10, and davidbloss. Generally speaking, you can categorize most dbt utils into five major groups: SQL generators Generic tests Jinja helpers Web macros Introspective macros This list is not exhaustive, but it encompasses most of the commonly used and widely used utils chosen by data teams working with dbt. Getting started with dbt. Any columns exclusive to a subset of these relations will be filled with null where not present. A "relation" refers to any kind of database object that contains data and can be queried.

Learn the essentials of how dbt supports data practitioners.

Datafold is the fastest way to test dbt code changes. Transform Data. Contributors Looking to level up data testing? The original treated null values and blank strings the same, which could lead to duplicate keys being created. At the time of writing, the project repo on GitHub has a little under stars. This macro cross-database way to sum up fields that can have null values, based on the fields you indicate. Provide a full git sha hash, e. You switched accounts on another tab or window. This macro merges the items from the relations argument using 'union all'.