* Data 360 *

Hey @Santosh Narayan,

To clarify, I’m not looking to routinely rebuild the index. My concern applies to the initial build, because historical ContentVersion records may have accumulated in the DMO before the index is created. 

For example, a 100 MB file could be ingested on day one and replaced on day three. When I create the index on day five, I only want the latest published version processed. However, Salesforce Engineering confirmed that my initial build also processed non-latest versions available through the configured DMO relationship, consuming credits unnecessarily. 

I am ultimately trying to figure out how to exclude obsolete file versions from processing and credit consumption during the first build, and any future full rebuild.

Has anyone here had success creating similar web tracking activity that Pardot(AE) captured? I.E., referrer, page visits in sequential order, all showing on the lead/contact records and used the Marketing Cloud Next forms.  

I'm having trouble capturing this data on the lead/contact record and reading that hidden fields on the forms might be my only hope since I'm using the MC Next forms and I can't send an identity event from the SDK on our primary host domain since the form is MC Next is technically a subdomain so the primary domain can't read the email. 

That generic query failure in Data Explorer typically happens when the metadata synchronization between your DLOs, DMOs, and search indexes is interrupted during initial async build jobs. Because you are working in a Full Copy sandbox, background chunking and retrieval jobs can occasionally time out or lag due to resource constraints. To troubleshoot the issue, test metadata responsiveness by running a minimal query on just the primary key in Data Explorer. You should also review your Data Cloud job status and ensure that object permissions are not blocking access to the chunk objects. If failures persist, drop and rebuild the search index or manually refresh the data stream to allow background jobs to re-run successfully.

 In my project, I encountered a similar situation where we were unable to promote Data Kits to other Salesforce instances, whether it was a Partial Copy or a Full Copy sandbox. We reached out to Salesforce Support, but they didn’t have a clue. From my perspective, combining Salesforce CLI, VS Code, and AI

is the best approach to successfully deploy Data Kits. 

The Issue

Salesforce generates a single package.xml file for the DevOps Data Kit. While it contains all the items required for deployment, the deployment itself fails due to underlying Salesforce platform limitations and bugs. 

The Resolution

1. Extract the Data Kit package.xml file.
2. Retrieve all metadata from your source organization.
3. Run a validation against the destination organization.
4. Capture all the deployment errors you receive.
5. Feed the package.xml and error screenshots into an AI tool, then ask it to split the main package.xml into smaller, logical parts for deployment. Note: This step involves some trial and error. If you are lucky, you will get it right on the first try; otherwise, it might take 2 to 3 iterations.
6. Deploy the single Data Kit in phases based on the split versions of the package.xml generated by the AI, bypassing the single-file method provided by Salesforce.

Additional Context

When you open a Salesforce Support ticket for assistance, they rarely know how to fix this particular issue. Eventually, they suggested we use Change Sets to promote the changes. However, manually selecting Data Kit dependencies is incredibly tedious—in our case, a single Data Kit had 1,000+ dependencies

. By using AI to break down the massive package into manageable parts, I successfully promoted our changes all the way to Production without any issues. 

Probable Root Cause

Salesforce launched Data Cloud in a rush. I noticed a very similar pattern when they launched Community Cloud back in 2013, where we faced persistent deployment issues for it all the way up until 2020 and the main issue was promoting dependencies in sequence. same we are noticing in data kit deployments.

 In my project, I encountered a similar situation where we were unable to promote Data Kits to other Salesforce instances, whether it was a Partial Copy or a Full Copy sandbox. We reached out to Salesforce Support, but they didn’t have a clue. From my perspective, combining Salesforce CLI, VS Code, and AI

is the best approach to successfully deploy Data Kits. 

The Issue

Salesforce generates a single package.xml file for the DevOps Data Kit. While it contains all the items required for deployment, the deployment itself fails due to underlying Salesforce platform limitations and bugs. 

The Resolution

1. Extract the Data Kit package.xml file.
2. Retrieve all metadata from your source organization.
3. Run a validation against the destination organization.
4. Capture all the deployment errors you receive.
5. Feed the package.xml and error screenshots into an AI tool, then ask it to split the main package.xml into smaller, logical parts for deployment. Note: This step involves some trial and error. If you are lucky, you will get it right on the first try; otherwise, it might take 2 to 3 iterations.
6. Deploy the single Data Kit in phases based on the split versions of the package.xml generated by the AI, bypassing the single-file method provided by Salesforce.

Additional Context

When you open a Salesforce Support ticket for assistance, they rarely know how to fix this particular issue. Eventually, they suggested we use Change Sets to promote the changes. However, manually selecting Data Kit dependencies is incredibly tedious—in our case, a single Data Kit had 1,000+ dependencies

. By using AI to break down the massive package into manageable parts, I successfully promoted our changes all the way to Production without any issues. 

Probable Root Cause

Salesforce launched Data Cloud in a rush. I noticed a very similar pattern when they launched Community Cloud back in 2013, where we faced persistent deployment issues for it all the way up until 2020 and the main issue was promoting dependencies in sequence. same we are noticing in data kit deployments. 

Hi @Cosmin Stanescu

, 

I recently ran into a very similar issue. After some investigation, I found that Salesforce had sent a Security Notification regarding Data Cloud access for portal and guest users.

The notification stated:

According to Salesforce, this only affects environments where administrators have explicitly configured access to Data Cloud (Data 360) entities for portal or guest users, for example by assigning Data Cloud permissions to community users, exposing Apex classes through Experience Cloud, or using Retriever-based access.

In my case, the issue was related to these security changes. After reviewing and updating the Data Cloud entity access settings according to Salesforce's guidance, the flow started working again. I would recommend checking whether your org received the same Security Notification and reviewing the

Based on my experience, it appears that Salesforce has tightened security around external access to Data Cloud objects, which may explain why something that worked previously is no longer working for guest users. Hope that helps!