address review comments

Signed-off-by: Nok <nok.lam.chan@quantumblack.com>

docs/source/deployment/databricks/databricks_ide_development_workflow.md → docs/source/deployment/databricks/databricks_ide_databricks_asset_budnels_workflow.md

@@ -1,10 +1,10 @@
-# Use Databricks Asset Bundles to deploy a Kedro project
+# Use an IDE and Databricks Asset Bundles to deploy a Kedro project
 
 ```{note}
-The `dbx` package is deprecated by Databricks, and dbx workflow documentation is moved to a [new page](./databricks_dbx_workflow.md).
+The `dbx` package was deprecated by Databricks, and dbx workflow documentation is moved to a [new page](./databricks_dbx_workflow.md).
 ```
 
-This guide demonstrates a workflow for developing a Kedro Project on Databricks using Databricks Asset Bundles. You will learn how to develop your project using a local environment, then use `kedro-databricks` and Databricks Asset Bundle to package your code for running pipelines on Databricks. To learn more about Databricks Asset Bundles and customization, read [What are Databricks Asset Bundles](https://docs.databricks.com/en/dev-tools/bundles/index.html).
+This guide demonstrates a workflow for developing a Kedro Project on Databricks using Databricks Asset Bundles. You will learn how to develop your project using a local environment, then use `kedro-databricks` and Databricks Asset Bundle to package your code for running pipelines on Databricks. To learn more about Databricks Asset Bundles and customisation, read [What are Databricks Asset Bundles](https://docs.databricks.com/en/dev-tools/bundles/index.html).
 
 ## Benefits of local development
 
@@ -12,7 +12,7 @@
 
 - Auto-completion and suggestions for code, improving your development speed and accuracy.
 - Linters like [Ruff](https://docs.astral.sh/ruff) can be integrated to catch potential issues in your code.
 - Static type checkers like Mypy can check types in your code, helping to identify potential type-related issues early in the development process.
 
 To set up these features, look for instructions specific to your IDE (for instance, [VS Code](https://code.visualstudio.com/docs/python/linting)).
 
@@ -39,9 +39,9 @@
 ## Set up your project
 
 ### Note your Databricks username and host
 Note your Databricks **username** and **host** as you will need it for the remainder of this guide.
 
 Find your Databricks username in the top right of the workspace UI and the host in the browser's URL bar, up to the first slash (e.g., `https://adb-123456789123456.1.azuredatabricks.net/`):
 
 ![Find Databricks host and username](../../meta/images/find_databricks_host_and_username.png)
 
@@ -62,7 +62,7 @@
 ```
 
 
 ### Authenticate the Databricks CLI
 **Now, you must authenticate the Databricks CLI with your Databricks instance.**
 
 [Refer to the Databricks documentation](https://docs.databricks.com/en/dev-tools/cli/authentication.html) for a complete guide on how to authenticate your CLI. The key steps are:
@@ -86,7 +86,7 @@
 If you are not using the `databricks-iris` starter to create a Kedro project, **and** you are working with a version of Kedro **earlier than 0.19.0**, then you should [disable file-based logging](https://docs.kedro.org/en/0.18.14/logging/logging.html#disable-file-based-logging) to prevent Kedro from attempting to write to the read-only file system.
  ```
 
 ## Create the Databricks Asset Bundles using `kedro-databricks`
 
 `kedro-databricks` is a wrapper around the `databricks` CLI. It's the simplest way to get started without getting stuck with configuration.
 1. Install `kedro-databricks`:
@@ -111,14 +111,14 @@
 
 This command reads the configuration from `conf/databricks.yml` (if it exists) and generates the Databricks job configuration inside a `resource` folder.
 
 ### Running a Databricks Job Using an Existing Cluster
 
 By default, Databricks creates a new job cluster for each job. However, there are instances where you might prefer to use an existing cluster, such as:
 
 1. Lack of permissions to create a new cluster.
 2. The need for a quick start with an all-purpose cluster.
 
 While it is generally [**not recommended** to utilise **all-purpose compute** for running jobs](https://docs.databricks.com/en/jobs/compute.html#should-all-purpose-compute-ever-be-used-for-jobs), it is feasible to configure a Databricks job for testing purposes.
 
 To begin, you need to determine the `cluster_id`. Navigate to the `Compute` tab and select the `View JSON` option.
 
@@ -126,7 +126,7 @@
 ![Find cluster ID through UI](../../meta/images/databricks_cluster_id1.png)
 
 You will see the cluster configuration in JSON format, copy the `cluster_id`
 ![cluster_id in the JSON view](../../meta/images/databricks_cluster_id2.png)
 
 Next, update `conf/databricks.yml`
 ```diff
@@ -140,7 +140,7 @@
 ```
 kedro databricks bundle --overwrite
 ```
 ## Deploy Databricks Job using Databricks Asset Bundles
 
 Once you have all the resources generated, deploy the Databricks Asset Bundles to Databricks:
 
@@ -148,7 +148,7 @@
 kedro databricks deploy
 ```
 
 You should see output similar to:
 
 ```
 Uploading databrick_iris-0.1-py3-none-any.whl...
@@ -162,7 +162,7 @@
 
 There are two options to run Databricks Jobs:
 
 ### Run Databricks Job with `databricks` CLI
 
 ```bash
 databricks bundle run
@@ -181,7 +181,6 @@
 
 Copy that URL into your browser or go to the `Jobs Run` UI to see the run status.
 
 ### Run Databricks Job with Databricks UI
 Alternatively, you can go to the `Workflow` tab and select the desired job to run directly:
-![alt text](../../meta/images/databricks-job-run.png)
-```
+![Run deployed Databricks Job with Databricks UI](../../meta/images/databricks-job-run.png)