Dbt_utils

Full Changelog : 1, dbt_utils. 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 dbt_utils following variable in your dbt project:. Our recommendation is that existing users should opt into the dbt_utils behaviour unless you are confident that either:, 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.

Dbt_utils

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. The macro accepts an optional argument where that allows for asserting the expression on a subset of all records. Asserts that a timestamp column in the reference model contains data that is at least as recent as the defined date interval. The default is true. Asserts that values in a given column have exactly the same cardinality as values from a different column in a different model. Asserts the referential integrity between two relations same as the core relationships assertions with an added predicate to filter out some rows from the test. This test confirms that a column contains sequential values. It can be used for both numeric values, and datetime values, as follows:. Asserts that the combination of columns is unique. For example, the combination of month and product is unique, however neither column is unique in isolation.

This macro provides the SQL needed to create a sequence of dates. They can format timestamps, log dbt_utils, and more, 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.

Learn the essentials of how dbt supports data practitioners. Upgrade your strategy with the best modern practices for data. Support growing complexity while maintaining data quality. Use Data Vault with dbt Cloud to manage large-scale systems. Implement data mesh best practices with the dbt Mesh feature set. Reduce data platform costs with smarter data processing. Establishes a standardized Data Vault structure with dbt Cloud. Creates new business opportunities through collaborative analytics. Serves up multimedia content on a global scale with dbt Cloud.

Dbt_utils

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.

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Learn the fundamentals with Castor's expert guide. This macro returns a dictionary from a query. Reload to refresh your session. Book a Demo. The test ensures that a given SQL expression holds true for every record, which is particularly helpful in maintaining column integrity. Contributors kmclaugh, courentin, and 3 other contributors. Automated Documentation. This macro returns the sql required to build a date spine. Further customizations are available with the optional arguments. This test ensures a column's values are within a certain range, set by a minimum and maximum value.

Those who have been building data warehouses for a long time have undoubtedly encountered the challenge of building surrogate keys on their data models.

This test ensures a column's values are within a certain range, set by a minimum and maximum value. This article delves into the different types of dbt utils, including SQL generators, generic tests, Jinja helpers, web macros, and introspective macros. They are described this way :. Let's say you have a fact table called orders, along with two dimension tables for users and products. View all files. This macro provides a dictionary based on a SQL query, eliminating the need to work with the Agate library to process the outcome. Welcome to this tutorial on surrogate key generation using dbt's utility package. The end points must also evaluate to numeric values and not be equal. Introspective macros in dbt-utils are designed to query dbt metadata and generate dynamic SQL based on the results. By automating common SQL patterns, they minimize manual coding and guarantee consistent, high-quality queries. Collective Intelligence. This macro implements a cross-database way to generate a hashed surrogate key using the fields specified. It also allows for filtering out specific rows, like test entries or recent entries, which might have temporary inconsistencies because of data processing limits. How to Use : This macro concatenates the fields and applies a cryptographic hash using MD5 by default to produce a unique ID. You can also specify a 'unit' for the distance.