fix reference

Signed-off-by: Nok <[email protected]>

Makefile

@@ -31,7 +31,7 @@ secret-scan:
 	trufflehog --max_depth 1 --exclude_paths trufflehog-ignore.txt .
 
 build-docs:
-	uv pip install --system -e ".[docs]"
+	uv pip install -e ".[docs]"
 	./docs/build-docs.sh "docs"
 
 show-docs:

docs/source/deployment/databricks/databricks_dbx_workflow.md

@@ -1,7 +1,7 @@
 # Use an IDE, dbx and Databricks Repos to develop a Kedro project
 
 ```{warning}
-`dbx` is deprecated in 2023, the recommended workflow now is to use [Databricks Asset Bundles](./databricks_ide_development_workflow.md)
+`dbx` is deprecated in 2023, the recommended workflow now is to use [Databricks Asset Bundles](./databricks_ide_databricks_asset_bundles_workflow.md)
 ```
 This guide demonstrates a workflow for developing Kedro projects on Databricks using your local environment for development, then using dbx and Databricks Repos to sync code for testing on Databricks.
 

docs/source/deployment/databricks/databricks_deployment_workflow.md

@@ -15,7 +15,7 @@ Here are some typical use cases for running a packaged Kedro project as a Databr
 
 Running your packaged project as a Databricks job is very different from running it from a Databricks notebook. The Databricks job cluster has to be provisioned and started for each run, which is significantly slower than running it as a notebook on a cluster that has already been started. In addition, there is no way to change your project's code once it has been packaged. Instead, you must change your code, create a new package, and then upload it to Databricks again.
 
-For those reasons, the packaging approach is unsuitable for development projects where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_ide_development_workflow.md).
+For those reasons, the packaging approach is unsuitable for development projects where rapid iteration is necessary. For guidance on developing a Kedro project for Databricks in a rapid build-test loop, see the [development workflow guide](./databricks_ide_databricks_asset_bundles_workflow.md).
 
 ## What this page covers
 

docs/source/deployment/databricks/databricks_notebooks_development_workflow.md

@@ -2,7 +2,7 @@
 
 This guide demonstrates a workflow for developing Kedro projects on Databricks using only a Databricks Repo and a Databricks notebook. You will learn how to develop and test your Kedro projects entirely within the Databricks workspace.
 
-This method of developing a Kedro project for use on Databricks is ideal for developers who prefer developing their projects in notebooks rather than an in an IDE. It also avoids the overhead of setting up and syncing a local environment with Databricks. If you want to take advantage of the powerful features of an IDE to develop your project, consider following the [guide for developing a Kedro project for Databricks using your local environment](./databricks_ide_development_workflow.md).
+This method of developing a Kedro project for use on Databricks is ideal for developers who prefer developing their projects in notebooks rather than an in an IDE. It also avoids the overhead of setting up and syncing a local environment with Databricks. If you want to take advantage of the powerful features of an IDE to develop your project, consider following the [guide for developing a Kedro project for Databricks using your local environment](./databricks_ide_databricks_asset_bundles_workflow.md).
 
 In this guide, you will store your project's code in a repository on [GitHub](https://github.com/). Databricks integrates with many [Git providers](https://docs.databricks.com/repos/index.html#supported-git-providers), including GitLab and Azure DevOps. The steps  to create a Git repository and sync it with Databricks also generally apply to these Git providers, though the exact details may vary.
 

docs/source/deployment/databricks/index.md

@@ -12,7 +12,7 @@ To avoid the overhead of setting up and syncing a local development environment
 
 **I want a hybrid workflow model combining local IDE with Databricks**
 
-The workflow documented in ["Use Databricks Asset Bundles to deploy a Kedro project"](./databricks_ide_development_workflow.md) is for those that prefer to work in a local IDE.
+The workflow documented in ["Use Databricks Asset Bundles to deploy a Kedro project"](./databricks_ide_databricks_asset_bundles_workflow.md) is for those that prefer to work in a local IDE.
 
 If you're in the early stages of learning Kedro, or your project requires constant testing and adjustments, choose this workflow. You can use your IDE's capabilities for faster, error-free development, while testing on Databricks. Later you can make the transition into a production deployment with this approach, although you may prefer to switch to use [job-based deployment](./databricks_deployment_workflow.md) and fully optimise your workflow for production.