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This guide lists and explains some common error messages that you may encounter when . Where applicable, it also explains how to resolve the underlying issue. After following the steps to resolve an issue, if the error persists, raise a Support ticket. For more information about Support tickets, read Getting support. If you encounter an error that you think we should add to this guide, contact our Support team to let us know.

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Authorization errors

These errors relate to your authorization methods, account permissions, and credentials.

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Authorization permission mismatch: Signature did not match

The Authorization permission mismatch error message appears if you try to perform an action, but do not have the required permissions. The Signature did not match part of this error message explains that the issue is a mismatch between the signature generated by your Software Development Kit (SDK) and the signature expected by the server. This error message can appear because your account does not have the required permissions, the string used as a signature by your SDK is incorrect, or your credentials are configured incorrectly. To resolve this issue:
  1. Check your account permissions:
    1. Log in to the Azure portal.
    2. Go to the resource where the action is being performed.
    3. Check the access control settings and make sure that your account or service principal has the required permissions for the action you are trying to perform.
    4. Update or grant permissions if required.
  2. If your account permissions are correct, make sure the signatures match:
    1. Enter 'Azure-Storage-Log-String-To-Sign': true into the corresponding method call. For an SAS token, this will be the corresponding generateSas method call.
    2. Compare the logged signature to the signature generated by your SDK.
    3. If they do not match, correct the signature.
    4. If this resolves the issue, we recommend disabling the 'Azure-Storage-Log-String-To-Sign' to avoid potentially exposing sensitive information.
  3. If the signatures match but the issue is not resolved:
    1. Generate a new StorageSharedKeyCredential or SAS token.
    2. Update your credentials in .

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You are not authorized for this operation

This error message appears if you try to perform an action, but do not have the required permissions. As a result, your pipeline fails when trying to access data. This could be because the credentials you have provided are invalid, your user account may not have the required permissions, or your authentication token may be expired or invalid. To resolve this issue:
  1. Check that your credentials—your username and password or token—are spelled correctly.
  2. If your credentials are spelled correctly, log in to your corresponding user account and check that your account has the required permissions for the action you are trying to perform.
  3. If you are using an authentication token, generate a new authentication token and update the component configuration to match.
  4. If applicable, check that the API endpoint you are trying to access is correct and that you have the required permissions to access it.

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Pipeline errors

These errors are related to your pipelines themselves, not specific components.

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Azure Blob: Stream is already closed

This error message appears if your permissions for accessing Azure storage accounts and blobs have not been defined correctly. This can affect any component that reads from or writes to Azure Blob Storage. To resolve this issue, make sure that all your Azure app registrations for contain all the required roles and permissions listed in our Roles and permissions for cloud storage documentation.

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Column [Column name] is of type [data type] and cannot have null entries

This error message appears if a column is expected to contain entries with the specified data type, but contains one or more null values. This could be because your source data contains null values, there is a mismatch between the data types in your source data and pipeline configuration, or a transformation component has inadvertently added null values to your data. To resolve this issue:
  1. Check your source data and clean it to remove any null values in this column. This might involve deleting rows or adding appropriate values.
  2. If there aren’t any null values in your source data, find the component where this column is defined and check that the data type is correct.
  3. If the data type is correct, check the configuration of your transformation components to make sure that a component isn’t adding null values in this column.
If none of these steps work, you can add a component in your transformation pipeline to handle null values in the specified column, for example by replacing any null values with a default value of the correct data type.

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Compilation error: ambiguous column name

This error message appears when an SQL query references a column name that exists in more than one table, for example if two joined tables each contain a column with the same name, and a downstream component references the column without disambiguation. This typically happens when using transformation components like Calculator or Join. To resolve this issue, we recommend using the Rename component to rename any columns with the same name before they are referenced by downstream components. If you need to keep the column names the same, make sure that any SQL expressions that reference these columns specify the table containing the referenced column. For example, if both table_a and table_b contain a name column, use SELECT table_a.name FROM table_a in the expression.

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Expected: [Column name]:[Data type] Actual: [Column name]:[Data type]

This error message appears if the expected data type of a column does not match its actual data type. For example, this message might appear as Expected: Quantity:VARCHAR(40) Actual: Quantity:VARCHAR(2000). This could be because the source data schema is different to the expected schema in your pipeline, the column type is set incorrectly, or a transformation component is changing the data type. To resolve this issue:
  1. Find the component where the column is defined and check the column’s data type. If it is not correct, update the data type.
  2. If the data type is correct, check the schema of your source data. If it is different to your pipeline configuration, change your pipeline configuration accordingly.
  3. If the schema is the same as your pipeline configuration, check the configuration of your transformation components to make sure that a component isn’t changing the column’s data type.

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Failed to find pipeline with name [Pipeline name]

This error message appears if tries to run a pipeline, but cannot find a pipeline with the specified name. This could be because you have spelled the pipeline name incorrectly, the pipeline has been renamed or deleted, or the pipeline is not in the specified location. To resolve this issue:
  1. Check the pipeline name to ensure that it is spelled correctly.
  2. If the name is spelled correctly, check that the pipeline has not been renamed or deleted. If the pipeline has been renamed, change the component configuration to match. If the pipeline has been deleted, you may need to rebuild it.
  3. If the pipeline has not been renamed or deleted, check that it is in the correct location. If the pipeline is in a different location, change the component configuration to match.

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Run pipeline: Something went wrong running your pipeline, please try again

This error message appears if you have an orchestration pipeline and a transformation pipeline with the same name, and the orchestration pipeline contains a Run transformation component to run the transformation pipeline. To resolve this issue, rename either pipeline and adjust the component configuration if necessary.
We recommend using a naming convention for your pipelines to avoid this issue. For example, you could use O_social_media_data for an orchestration pipeline that loads your data and T_social_media_data for a transformation pipeline that transforms this data.

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SQL compilation error: Database [Database name] does not exist or not authorized

This error message appears if the database you are trying to access does not exist or you do not have the required permissions to access this database. As a result, your pipeline cannot compile the SQL query. This could be because the database name is configured incorrectly, the database does not exist yet, or your permissions are not correct. To resolve this issue:
  1. Check that the database exists and that the database name is correct in your database system.
    • If the database doesn’t exist in your database system, create the database.
    • If the database name is different in your database system, update your pipeline configuration to match.
  2. If the database exists and the name is correct, check that your user account has the permissions required to access this database.
  3. If your permissions are correct, check the database name, username and password in your connection settings. If any of these details are incorrect, update them.

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Validation failed due to parameter errors

This error message appears if the Data Selection property contains options that doesn’t recognize. The error message will contain details of the options that have not been recognized. Possible reasons for this error include entering invalid options, typos or errors in the options, or entering an option that is no longer valid or has been deprecated. To resolve this issue, check that all the options you have entered or selected in the Data Selection property are valid and correct. You may find it useful to check our component documentation and the data model for the component, if applicable.

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Resource publication failure: [pipeline]. Error: Resource exceeds size limit

This error message appears if there is a file in your branch larger than 5 MB in size. Too-large files prevent from running pipelines. To resolve this issue, first locate any files larger than 5 MB using either of the following options:
  • Ask to locate any files larger than 5 MB for you.
  • Export your entire branch as a .zip file, then use your local operating system’s file manager or terminal tools to find files exceeding 5 MB.
Once you have identified the oversized files, delete them directly within .