From 80dcf47c80572367b5a2eeecc12b07399b0d8770 Mon Sep 17 00:00:00 2001 From: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> Date: Mon, 2 Sep 2024 10:57:56 +0100 Subject: [PATCH] remove sentence (#5997) this pr removes the last sentence of this guide as they do not apply anymore. refer to internal slack thread: https://dbt-labs.slack.com/archives/C02HE19D51R/p1725268090650929 --- .../how-we-structure/5-the-rest-of-the-project.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/docs/best-practices/how-we-structure/5-the-rest-of-the-project.md b/website/docs/best-practices/how-we-structure/5-the-rest-of-the-project.md index 21ea6ad4682..c7522bf12eb 100644 --- a/website/docs/best-practices/how-we-structure/5-the-rest-of-the-project.md +++ b/website/docs/best-practices/how-we-structure/5-the-rest-of-the-project.md @@ -102,11 +102,11 @@ We’ve focused heavily thus far on the primary area of action in our dbt projec ### Project splitting -One important, growing consideration in the analytics engineering ecosystem is how and when to split a codebase into multiple dbt projects. Our present stance on this for most projects, particularly for teams starting out, is straightforward: you should avoid it unless you have no other option or it saves you from an even more complex workaround. If you do have the need to split up your project, it’s completely possible through the use of private packages, but the added complexity and separation is, for most organizations, a hindrance not a help, at present. That said, this is very likely subject to change! [We want to create a world where it’s easy to bring lots of dbt projects together into a cohesive lineage](https://github.com/dbt-labs/dbt-core/discussions/5244). In a world where it’s simple to break up monolithic dbt projects into multiple connected projects, perhaps inside of a modern monorepo, the calculus will be different, and the below situations we recommend against may become totally viable. So watch this space! +One important, growing consideration in the analytics engineering ecosystem is how and when to split a codebase into multiple dbt projects. Our present stance on this for most projects, particularly for teams starting out, is straightforward: you should avoid it unless you have no other option or it saves you from an even more complex workaround. If you do have the need to split up your project, it’s completely possible through the use of private packages, but the added complexity and separation is, for most organizations, a hindrance, not a help, at present. That said, this is very likely subject to change! [We want to create a world where it’s easy to bring lots of dbt projects together into a cohesive lineage](https://github.com/dbt-labs/dbt-core/discussions/5244). In a world where it’s simple to break up monolithic dbt projects into multiple connected projects, perhaps inside of a modern mono repo, the calculus will be different, and the below situations we recommend against may become totally viable. So watch this space! -- ❌ **Business groups or departments.** Conceptual separations within the project are not a good reason to split up your project. Splitting up, for instance, marketing and finance modeling into separate projects will not only add unnecessary complexity, but destroy the unifying effect of collaborating across your organization on cohesive definitions and business logic. -- ❌ **ML vs Reporting use cases.** Similarly to the point above, splitting a project up based on different use cases, particularly more standard BI versus ML features, is a common idea. We tend to discourage it for the time being. As with the previous point, a foundational goal of implementing dbt is to create a single source of truth in your organization. The features you’re providing to your data science teams should be coming from the same marts and metrics that serve reports on executive dashboards. There are a growing number of tools like [fal](https://fal.ai/) and [Continual.ai](http://Continual.ai) that make excellent use of this unified viewpoint. -- ✅ **Data governance.** Structural, organizational needs — such as data governance and security — are one of the few worthwhile reasons to split up a project. If, for instance, you work at a healthcare company with only a small team cleared to access raw data with PII in it, you may need to split out your staging models into their own project to preserve those policies. In that case, you would import your staging project into the project that builds on those staging models as a [private package](https://docs.getdbt.com/docs/build/packages/#private-packages). +- ❌ **Business groups or departments.** Conceptual separations within the project are not a good reason to split up your project. Splitting up, for instance, marketing and finance modeling into separate projects will not only add unnecessary complexity but destroy the unifying effect of collaborating across your organization on cohesive definitions and business logic. +- ❌ **ML vs Reporting use cases.** Similarly to the point above, splitting a project up based on different use cases, particularly more standard BI versus ML features, is a common idea. We tend to discourage it for the time being. As with the previous point, a foundational goal of implementing dbt is to create a single source of truth in your organization. The features you’re providing to your data science teams should be coming from the same marts and metrics that serve reports on executive dashboards. +- ✅ **Data governance.** Structural, organizational needs — such as data governance and security — are one of the few worthwhile reasons to split up a project. If, for instance, you work at a healthcare company with only a small team cleared to access raw data with PII in it, you may need to split out your staging models into their own projects to preserve those policies. In that case, you would import your staging project into the project that builds on those staging models as a [private package](https://docs.getdbt.com/docs/build/packages/#private-packages). - ✅ **Project size.** At a certain point, your project may grow to have simply too many models to present a viable development experience. If you have 1000s of models, it absolutely makes sense to find a way to split up your project. ## Final considerations