Converting invalid JSON without validation first causing conversion failures or incorrect results, when malformed JSON, syntax errors, or invalid structure prevent proper parsing and CSV conversion, causing tool errors, incomplete conversions, or corrupted CSV output requiring JSON validation before conversion to ensure input is valid JSON suitable for CSV conversion processing.
Flattening arrays without considering data loss or structural information loss, when array flattening loses array structure, element relationships, or nested data organization that may be important, causing data loss, information reduction, or structural degradation requiring careful consideration of whether array preservation (as JSON strings) or flattening is appropriate based on data requirements and downstream CSV usage.
Using commas inside CSV values without proper quoting causing column misalignment, when unquoted values containing commas (delimiter characters) break CSV structure, causing columns to split incorrectly, data misalignment, or parsing failures requiring proper CSV quoting (double quotes around fields containing commas) to maintain CSV structure integrity and prevent delimiter confusion in CSV output.
Mixing line endings across platforms causing import issues in target systems, when Unix line endings (\n) vs Windows line endings (\r\n) mismatch target system expectations, causing CSV import failures, row parsing errors, or platform-specific issues requiring appropriate line ending selection matching target system requirements (Unix for Linux/Mac, Windows for Excel/Windows systems) for CSV compatibility.
Not reviewing CSV preview before download causing unexpected output format or data issues, when CSV preview reveals formatting problems, data quality issues, or structural anomalies that could cause import failures, causing missed opportunities to correct conversion settings and requiring preview review to validate CSV structure, data integrity, and format correctness before downloading or using converted CSV files.
Using wrong delimiter for target system causing CSV import failures, when delimiter mismatch (comma vs semicolon vs tab) prevents proper CSV parsing in target systems, causing import errors, data misinterpretation, or system compatibility issues requiring delimiter selection matching target system CSV format expectations (comma for US, semicolon for European locales, tab for TSV).
Not flattening deeply nested JSON structures causing unreadable CSV cells, when deeply nested objects create extremely complex CSV cell content that's difficult to read, parse, or work with, causing CSV usability problems and requiring nested object flattening with appropriate separators (e.g., address.city, user.profile.email) to create readable, structured CSV column names and values.
Assuming all JSON data types preserve in CSV when CSV is text-only format, when CSV format stores all values as text strings, causing loss of type information (numbers become strings, booleans become "true"/"false" strings), requiring type conversion after CSV import or understanding that CSV doesn't preserve original JSON data types, with all values serialized as text in CSV format.
Converting JSON with inconsistent object structures causing missing column data, when JSON objects with varying keys create CSV rows with missing columns (empty cells) when some objects lack keys present in other objects, causing CSV structure inconsistencies and requiring consistent JSON object structures or handling missing keys appropriately in CSV conversion to maintain CSV column consistency.
Not preserving original JSON files before CSV conversion causing loss of structured data, when CSV conversion flattens hierarchical JSON structure potentially losing relationships, nesting, or structural information that may be needed later, causing data structure loss and requiring original JSON preservation alongside CSV files for comprehensive data management and potential reverse conversion or structural reference needs.
Ignoring CSV conversion statistics showing data quality issues requiring attention, when conversion statistics reveal unexpected row counts, column counts, or data anomalies indicating conversion problems or data quality issues, missing opportunities to identify and fix conversion issues before CSV deployment and requiring statistics review to validate conversion accuracy and ensure CSV output meets quality requirements.
Using CSV conversion for JSON data requiring hierarchical relationships that CSV cannot preserve, when JSON with complex nested relationships, tree structures, or hierarchical data doesn't map well to flat CSV format, causing structural information loss and requiring understanding that CSV is flat format unsuitable for complex hierarchical data requiring alternative formats (JSON, XML) or specialized conversion approaches for hierarchical data preservation.