Dbt_utils

This post will run through how to install and use some popular and some unsung dbt utils in your project, dbt_utils, dbt_utils. The dbt-utils dbt_utils in general is maintained by duh dbt Labs. Its contributors include a mix of developers from both dbt Labs and the wider data community.

Meet Castor AI, your on-demand data analyst, always available and trained specifically for your business. These utilities simplify the process of writing complex logic in dbt, allowing users to leverage existing solutions. This article delves into the different types of dbt utils, including SQL generators, generic tests, Jinja helpers, web macros, and introspective macros. It provides a comprehensive guide on how to install these utilities and offers practical examples of how to use them in a dbt project. They can generate SQL code based on specific requirements, reducing the need for manual coding.

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:. By creating a new macro instead of updating the behaviour of the old one, we are requiring all projects who use this macro to make an explicit decision about which approach is better for their context. Our recommendation is that existing users should opt into the legacy behaviour unless you are confident that either a your surrogate keys never contained nulls, or b your surrogate keys are not used for incremental models, snapshots or other stateful artifacts and so can be regenerated with new values without issue. Skip to content. You signed in with another tab or window.

Utility functions for dbt projects.

Welcome to this tutorial on surrogate key generation using dbt's utility package. One of its many utilities is the generation of surrogate keys, which are essential for data modeling and analytics. Null values can be tricky when generating surrogate keys. If any value is null, the entire concatenated string might return as null. While MD5 is the default hashing function, you might prefer using SHA for its cryptographic advantages. How to Switch : Override the default hashing macro in dbt by adding a macro called hash.

Welcome to this tutorial on surrogate key generation using dbt's utility package. One of its many utilities is the generation of surrogate keys, which are essential for data modeling and analytics. Null values can be tricky when generating surrogate keys. If any value is null, the entire concatenated string might return as null. While MD5 is the default hashing function, you might prefer using SHA for its cryptographic advantages. How to Switch : Override the default hashing macro in dbt by adding a macro called hash. The file should include this file:. Surrogate keys are invaluable in data modeling, especially when dealing with data from diverse sources. They ensure no duplicate rows, establish relationships with other tables, and help identify the grain of the table. Surrogate Keys : When your data doesn't come with a unique primary key, surrogate keys come to the rescue.

Dbt_utils

For you , friend, we wrote it down for you. These are partial duplicates, meaning your entity of concern's primary key is not unique on purpose or perhaps you're just dealing with some less than ideal data syncing. You may be capturing historical, type-two slowly changing dimensional data, or incrementally building a table with an append-only strategy, because you actually want to capture some change over time for the entity your recording. Or, as mentioned, your loader may just be appending data indiscriminately on a schedule without much care for your time and sanity. You have this historical record that captures all the changes made to the entities. As discussed, the grain of the dataset you want to capture is the combination of the columns we deem important that make each row unique. These questions can help you figure out the core entity that you are tracking, and the real grain at which changes should be captured in your new model. We only need to keep the most recent one, so row 2 above can be removed from the cleaned dataset, giving us the output below.

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This list is not exhaustive, but it encompasses most of the commonly used and widely used utils chosen by data teams working with dbt. You can use dbt utils! This test checks the connection between two models, similar to the basic relationship checks. Releases 70 1. If needed, it's possible to opt into the legacy behavior by setting the following variable in your dbt project:. Try CastorDoc today. For example, let's say you have a transformation for a table called orders, and you want to log a message before and after the transformation. Book a Demo. You have tables related to orders, users, and products. Case Studies. This test asserts that a specific column has the same number of unique values as another column in a different table. New Release. About Us. However, these approaches can become non-perfomant on large data sets, in which case we recommend using this test instead. This macro returns the sql required to build a date spine.

Meet Castor AI, your on-demand data analyst, always available and trained specifically for your business. These utilities simplify the process of writing complex logic in dbt, allowing users to leverage existing solutions. This article delves into the different types of dbt utils, including SQL generators, generic tests, Jinja helpers, web macros, and introspective macros.

Because the results are objects, which in turn can be acted on, this is a very powerful abstraction. Imagine you're a Data Scientist at Amazon, and you need to organize your data into a few downstream tables to prep it for analysis. Dispatch macros. They can be used to get column names, relations, and more. Notifications Fork Star 1. Discover the importance of data lineage for tracking and managing the flow of your data. Governance Teams. It replaces Boolean values with the strings 'true' or 'false'. Welcome to this tutorial on surrogate key generation using dbt's utility package. Meet Castor AI, your on-demand data analyst, always available and trained specifically for your business. Branches Tags. In what scenarios might the introspective macros be particularly beneficial for data teams? Any unique columns will have 'null' values where they don't exist in some relations.