Learn how to resolve the “Unable to Deploy the Data Stream” error in GCS for Customer Data Cloud. Our Google Cloud Support team is here to help you with your questions and concerns.

“Unable to Deploy the Data Stream” Error in GCS for Customer Data Cloud

One of our customers recently reported the following error while creating a new Google Cloud Storage (GCS) data stream in Salesforce Customer Data Cloud:

Data Cloud: Error “Unable to deploy the data stream, please try again.”

This message is usually followed by a Gack ID, offering little insight into what went wrong. The lack of detailed feedback can make troubleshooting difficult. This is where our guide comes in handy.

It will help you understand common causes and walk you through effective solutions to get the data stream up and running.

Common Causes of the Deployment Error

1. A common cause for the error seems to be when seemingly optional fields in the UI are required behind the scenes. For example, the File Name field might appear optional in Salesforce Data Cloud, but if left blank, the deployment will fail.

   If you’re also working with BigQuery and hit similar module issues, check out this related guide on resolving ModuleNotFoundError: No module named ‘google.cloud.bigquery’.
2. The data stream might be blocked due to:
   • Firewall settings or IP whitelisting restrictions.
   • Incomplete or incorrect connection profiles.
   • Misconfigured SSH tunnels when using SSH forwarding.

   When working in Google Cloud environments, it’s essential to understand which GCS storage class is best suited to your use case. Here’s a helpful overview of Google Cloud Storage types to help you choose wisely.
3. The error can also be due to adding too many tables at once, leading to backfill errors, improper use of the UI’s include/exclude options, or missing CDC (Change Data Capture) updates or unsupported data types in source tables.

   If you’re incorporating event-driven architecture or using triggers in your data workflow, you might also find this article on Google Cloud Firestore triggers useful.
4. At times, the error message may be entirely generic. In these cases, we have to review stream logs and setup details for further information.

   This isn’t uncommon—Google Cloud errors can be cryptic. For example, here’s an in-depth look at the OR_BACR2_34 error, another vague but fixable issue in the platform.

Solution 1: Validate File and Bucket Configuration

• Ensure all objects being accessed have the correct permissions.
• Do not ingest files with more than 1,000 columns. This is a known upper limit.
• If the file lacks headers, the first row must have values in every column. Preferably, include column headers to avoid issues.
• Furthermore, GCS data streams support a maximum of 500 mapped fields.

If we are using a wildcard in the File Name, double-check all files in the target directory. Make sure they meet the 1,000-column limit or have empty values in the first row, especially for headerless files.

Solution 2: Don’t Skip the File Name Field

Even though the File Name attribute may look optional in the interface, it is required for deployment. Leaving it blank will lead to this error, and unfortunately, the UI doesn’t make this clear.

[Need assistance with a different issue? Our team is available 24/7.]

Conclusion

The error “Unable to deploy the data stream, please try again” is often the result of overlooked configuration issues. From missing required fields to firewall restrictions and data schema mismatches, multiple factors can contribute.

In brief, our Support Experts demonstrated how to resolve the “Unable to Deploy the Data Stream” error in GCS for Customer Data Cloud.