Add update to previous limitation mentioned in Data Vault blog post (#…

…4070)

## What are you changing in this pull request and why?
The AutomateDV team informed us of a recent improvement to AutomateDV
that they would like to address on the original post. This PR adds an
"editor's note" to the end of the AutomateDV to reflect the improvement
so the community is well-informed about the updates from the AutomateDV
team that address the critique.

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
and [About
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
so my content adheres to these guidelines.
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

website/blog/2023-07-03-data-vault-2-0-with-dbt-cloud.md

@@ -115,7 +115,9 @@ In terms of the implementation of the Data Vault itself, we recommend familiariz
 
 ### AutomateDV (formerly known as dbtvault)
 
-AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV _expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. 
+AutomateDV is the most popular open source Data Vault package for dbt, with some users having over 5000 Data Vault components in their project. Here in Infinite Lambda, we’ve been using this package for quite some time now, even building on top of it (depending on the specifics of the project). This mature system provides a great way to start your Data Vault with dbt Cloud journey as the learning curve is quite manageable, it is well documented and even comes with tutorials and working examples built on top of Snowflake’s TPCH standard dataset. There is one limitation to using the package and that is _AutomateDV_ expects your source data to contain only one delta load. In order to work around this issue, owners of the package came up with custom dbt materializations to help you with the initial load of your system, however, the performance of such load is in our experience not acceptable. 
+
+_(Editor's note: As of AutomateDV v0.10.0, this performance issue has been resolved and users may use the standard incremental configuration.)_
 
 ### datavault4dbt