![]() ![]() ![]() ![]() Created a subdirectory, just to keep things tidy: macros/udfs/.So I decided to put them into our dbt project, using this process: ![]() And we weren’t maintaining separate development/production versions of the UDFs.There was no version control on the UDFs if we needed to update them.The code was not surfaced as part of our dbt project, so it’s unclear what the UDF does.In the past, I’ve created these UDFs in my SQL console as the superuser, however this isn’t ideal as: Our dbt project uses some user defined functions (UDFs). Check out the notes on BigQuery in the comments below. He delivers proof of concepts with customers on Amazon Redshift, helping customers drive analytics value on AWS.Note: This post uses Redshift SQL, however the same concept applies to most data warehouses. Sean Beath is an Analytics Acceleration Lab Solutions Architect at Amazon Web Services. He works with customers to accelerate their Amazon Redshift journey by delivering proof of concepts on key business problems. Randy Chng is an Analytics Acceleration Lab Solutions Architect at Amazon Web Services. If you need any further assistance to optimize your Amazon Redshift implementation, contact your AWS account team or a trusted AWS partner. If you have any questions or suggestions, leave your feedback in the comments section. Visit dbt CLI and Amazon Redshift to get started. As you explore dbt, you will come across other features like hooks, which you can use to manage administrative tasks, for example, continuous granting of privileges.įor a hands-on experience with dbt CLI and Amazon Redshift, we have a workshop with step-by-step instructions to help you create your first dbt project and explore the features mentioned in this post-models, macros, seeds, and hooks. This post covered how you can use dbt to manage data transformations in Amazon Redshift. Instead of replicating the subquery, dbt allows you to create a model for the subquery and reference it later.įigure 5: Data lineage visualization generated by dbt Conclusion In this example, two models rely on the same subquery. The following figure is an example showing how dbt consolidates common logic. This improves maintainability and productivity because common logic can be consolidated (maintain a single instance of logic) and referenced (build on existing logic instead of starting from scratch). Manage common logicĭbt enables you to write SQL in a modular fashion. We also provide the dbt CLI and Amazon Redshift workshop to get started using these features. In this post, we demonstrate some features in dbt that help you manage data transformations in Amazon Redshift. dbt Cloud – A hosted service with added features including an IDE, job scheduling, and more.dbt CLI – Available as an open-source project.Together with price-performance, customers want to manage data transformations (SQL Select statements written by data engineers, data analysts, and data scientists) in Amazon Redshift with features including modular programming and data lineage documentation.ĭbt (data build tool) is a framework that supports these features and more to manage data transformations in Amazon Redshift. Amazon Redshift enables you to use your data to acquire new insights for your business and customers while keeping costs low. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. ![]()
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