データのエラーをトラブルシューティングするにはどうすればよいですか?

Data Manager helps teams connect, clean, and reconcile data by bringing in data from multiple types of data sources. Data Manager connects to these sources using the Adapter client to fetch from data sources and bring data into Data Manager.

If you encounter errors while fetching, check both your data and data source configuration.

1. Check connectivity

Because the Data Manager client runs locally and can retrieve information from both local and cloud sources, you need a reliable path between the data source and the client. Network problems such as closed firewalls, DNS failures, incorrect URLs, timeouts, and intermittent connectivity can cause API calls to fail unpredictably.

Key checks:

• Review proxy server configuration – if a proxy server is being used, specify this when fetching your data, or the data source may not be reachable. Common arguments include:

  • --proxy or -p – set the proxy server address

  • --proxy-user – username to authenticate against the proxy

  • --proxy-pass – password to authenticate against the proxy

  • --proxy-default-credentials – use default OS credentials to authenticate to the proxy

• Review server certificate status – verify that the SSL certificate for the server is valid. If the SSL is invalid, you may see errors such as:
  “The site cannot provide a secure connection – SSL protocol error.”
  If appropriate in your environment, you may use:

  • --bypass-invalid-certificate

• Check for security software interference – endpoint protection tools (for example, Forcepoint One Endpoint, Symantec, Windows Defender, or similar tools) may block connections or interfere with SSL.

  • Review security software logs for blocked connections or SSL‑related errors.

  • If permitted, temporarily disable the software and retry the connection.

  • If the connection works when security software is disabled, add the Data Manager client executable to the software’s exclusion list, then retry the connection.

  • If you cannot change these settings or issues persist, work with your IT administrator and then Atlassian Support if required.

• Perform a handshake test (ping) – confirm that the Adapters client can contact the data sources:

  • Locate the IP address of the data source’s server.

  • Use ping from Terminal (macOS, Linux) or Command Prompt (Windows/DOS) to verify connectivity.

2. Check authentication and authorization

Each Adapter has specific authentication and authorization requirements. These are listed in the each data sources documentation under “Authentication and authorization”.

• For product data sources, confirm that API keys, tokens, credentials, and scopes/permissions are correctly configured in the source system.

• For SQL/ODBC data sources, confirm that the user has read‑only access and connection strings are correct.

• For Schema or flat file data sources, confirm that local paths, file permissions, and any required tokens are valid.

3. Troubleshoot errors from a flat file data source

If a flat file data source (CSV or TXT data source) fails:

Verify audit logs

• Check the audit logs.

  • A checkmark indicates success; a warning icon indicates an issue.

  • Select the warning icon for error details.

Review raw data

• Use the raw data screen to check for missing attributes or fields.

  • Missing fields may indicate data source configuration error.

Check the delimiter character

• The delimiter separates values (for example, comma, tab, semicolon).

  • If the delimiter is missing or misconfigured, errors such as Line 280: The column count is not correct. can appear.

Verify column names and mappings

• Each column in your data source should have a corresponding column mapping.

  • Errors can occur if:

    • a column name is incorrect

    • the total number of columns is mismatched

    • a column contains unexpected spaces or trailing whitespace

  • Example error:
    Line 280: The column count is not correct. Error in line 1: Cannot find column 'cpu' in the Column Mappings.

Confirm the qualifier character

• A qualifier wraps text so that values containing delimiters are treated as a single value.

  • A common qualifier is " (double quote).

  • 例:

    • Without qualifier: Microsoft Windows 10, v. 22H2 is read as two values.

    • With qualifier: "Microsoft Windows 10, v. 22H2" is read as one value.

  • Ensure standard double quotes are used, not “smart quotes”.

Check date formats

• Ensure the date format in the adapter configuration matches the source data exactly.

  • Example error:
    Column 'whenCreated', date value '20/04/2009 1:47' cannot be converted to 'd/MM/yyyy HH:mm (8/02/2006 06:30).'

  • In this case, the correct format is likely d/MM/yyyy H:mm rather than d/MM/yyyy HH:mm.

4. Troubleshoot errors from a SQL & ODBC data source

For errors in a SQL & ODBC data source:

Verify data

• Check audit logs to confirm the fetch ran successfully.

  • A warning icon indicates a problem; select it for more details.

Review raw data

• Use the raw data tab to ensure required attributes and fields are present.

  • If fields are missing, review the SQL query used in the configuration.

Review data source configuration

• For SQL connections:

  • Confirm database address and database name.

  • Confirm authentication method:

    • For username/password, ensure credentials are valid.

    • For integrated security, ensure the connection string and credentials are correct and supplied by the source owner.

  • Test the SQL query directly against the source with the SME; verify that the structure and returned fields match expectations.

  • For ODBC connections:

    • Ensure the correct driver is installed.

    • If using a DSN, ensure the DSN and driver are correctly configured.

    • If using a connection string, review it with the database source owner.

5. Troubleshoot errors from a product data source

For product data sources that connect to third‑party tools:

Verify data

• Confirm via the audit log that data was brought in.

Review packet size and limit

• For API connections, timeout issues can occur if the data set is large.

  • Consider lowering the API limit (page size) to reduce timeouts.

Review raw data

• If attributes are missing:

  • a product data source may use a predefined call that does not return those fields

  • a custom data source configuration may point to the wrong endpoint or field list

  • a report or API call in the source application may not be correct or accessible with current permissions

Review the API call

• Refer to the data source’s documentation under “API call” (for example, the Azure Virtual Machine data source uses Device.Read.All).

• Review returned fields

  • Confirm that the fields listed in the data source documentation are present.

  • If fields are missing or misaligned, adjust the configuration or underlying report in the source.