Dbt_utils

This dbt package contains dbt_utils that can be re used across dbt projects. Check dbt Hub dbt_utils the latest installation instructions, or read the docs for more information on installing packages. Asserts the equality of two relations, dbt_utils. Optionally specify a subset of columns to compare or exclude, and a precision to compare numeric columns on.

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.

Dbt_utils

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. It should contain the following:. At the time of writing, the latest version is 1. Run the following command to install the new dependencies. 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. You have tables related to orders, users, and products. Using star and pivot macros can help simplify your SQL queries and make them easier to maintain. You want to create a consolidated view from your orders, users, and products tables—but you want to exclude repetitive ID fields, prefix the column names from the users and products tables to avoid confusion. You can use dbt utils!

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The dbt-utils package enhances the dbt experience by offering a suite of utility macros. Designed to tackle common SQL modeling patterns, it streamlines complex operations, allowing users to focus on data transformation rather than the intricacies of SQL. The dbt-utils package is a gem in the world of data transformations. Let this cheat sheet guide you swiftly through its features, ensuring you get the most out of dbt-utils. The SQL generators in the dbt-utils package streamline your modeling tasks. By automating common SQL patterns, they minimize manual coding and guarantee consistent, high-quality queries.

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.

Dbt_utils

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. Generic tests are essential for maintaining data quality. They allow you to set up automated tests on your data models to ensure consistency and accuracy.

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Grouping in tests. 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. Contributors joellabes, jeremyyeo, and 3 other contributors. Looking for an enterprise data platform? Note that the method is case-insensitive. This test ensures that when certain columns are combined, their values are unique. Learn more. Governance Teams. All reactions. Data Lineage. To use this feature, the names of grouping variables can be passed as a list. Latest commit History Commits. Packages 0 No packages published. Dispatch macros. You would define this as follows:.

In dbt, you can combine SQL with Jinja , a templating language. Using Jinja turns your dbt project into a programming environment for SQL, giving you the ability to do things that aren't normally possible in SQL.

They allow you to set up automated tests on your data models to ensure consistency and accuracy. The log function is then used to print the name of each relation found. They empower you to dynamically interact with and understand the underlying structure of your datasets. Review the cross database macros documentation for the full list, or the migration guide for a find-and-replace regex. Dispatch macros. This test ensures the timestamp column in the given model has data that's newer than a specific date range. This macro formats the input in a way that will print nicely to the command line when you log it. Discover the importance of data lineage for tracking and managing the flow of your data. Comprehensive overview comparing dbt Cloud and dbt Core, exploring their historical evolution, functionalities, cost structures, and integration capabilities within the context of the modern data stack. This test checks the connection between two models, similar to the basic relationship checks. In this scenario, the star macro helps you to select all columns from each table while excluding specified columns like id , and add prefixes to all columns from the users and products tables to avoid naming conflicts. Dismiss alert. At the time of writing, the latest version is 1.