In as much detail as possible, describe the problem or experience related to your idea. Please provide the context of what you were trying to do and include specific examples or workarounds:
I was working on a CloudWorks integration from Anaplan to BigQuery. The goal was to load data from Anaplan into existing BigQuery tables. When the integration ran, it failed with the error message: “Table does not exist or not enough permissions”, and no data was loaded into the BigQuery tables.
Later, we discovered the real root cause: a data type mismatch. A decimal number from an Anaplan line item was being loaded into a BigQuery column defined as INTEGER. Because BigQuery could not coerce the decimal into an integer, the load failed. However, instead of returning a clear “type mismatch” or “invalid value for column” error, the integration surfaced the generic and misleading message about the table not existing or insufficient permissions.
How often is this impacting your users?
This type of issue can occur whenever there is a schema mismatch between Anaplan exports and BigQuery tables—especially with numeric fields where decimals are common. It may not happen every day, but when it does, it can consume a lot of time because the error message points you in the wrong direction. For teams doing frequent or large-scale integrations, this could surface regularly during new integrations, schema changes, or model enhancements.
Who is this impacting? (ex. model builders, solution architects, partners, admins, integration experts, business/end users, executive-level business users)
- Model builders – defining line item formats and exports, they are directly affected when integrations fail without clear reasons.
- Integration experts / solution architects – they are the ones troubleshooting the error, checking permissions, and aligning schemas.
- Admins – may be pulled in to verify access, roles, and project-level permissions unnecessarily.
- Partners – when implementing solutions for clients, this kind of misleading error increases delivery time and complexity.
- Business/end users – they experience delays in data availability in BigQuery, which can impact reporting and analytics timelines.
What would your ideal solution be? How would it add value to your current experience?
Accurate, specific error messaging for schema/data issues When a load fails due to a type mismatch (e.g., decimal into INTEGER), the error should clearly state something like:“Data type mismatch: value X in column Y cannot be written to INTEGER field. Check source data or target schema.”
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