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Ingesting Text Files ..

Onboarding Text Files

File Format and Encoding Issues are among the most frequent obstacles. Inconsistent delimiters can occur when files mix separators (commas in some rows, tabs in others) or when text fields contain the delimiter character itself without proper quoting (e.g., "Smith, John" in a comma-delimited file). Line ending mismatches between Windows (\r\n), Unix/Linux (\n), and legacy Mac (\r) systems can cause Pentaho to misread record boundaries, resulting in merged or split rows. Character encoding problems arise when files created in different systems use incompatible encoding standards—a UTF-8 file with special characters (é, ñ, €) will display garbled text if read as ASCII, while files with emojis or international characters require UTF-8 or UTF-16 encoding to process correctly.

Data Quality and Consistency Problems can significantly impact ETL success. Missing values or incomplete records occur when rows have fewer fields than expected, forcing you to decide whether to skip these records, fill them with defaults, or flag them for review. Inconsistent data types within columns—such as a "Revenue" column containing both numeric values (1250.50) and text entries ("N/A" or "pending")—will cause type conversion errors unless handled explicitly. Duplicate records, whether exact copies or near-duplicates differing only in whitespace or capitalization, require deduplication logic to maintain data integrity.

Header Row and Structure Challenges add another layer of complexity. Files may or may not include a header row defining column names, requiring you to manually map column positions to field names when headers are absent. Misaligned headers occur when the header row has a different number of fields than data rows (often due to merged cells in Excel exports or manual editing), causing field mapping errors. Dynamic file naming patterns with timestamps or sequential numbers (e.g., sales_20241114_153045.csv or export_batch_0042.txt) require wildcard matching or regular expressions in Pentaho to process files automatically without hardcoding specific filenames.

Complex Data and Type Conversion Issues require advanced handling techniques. Nested or hierarchical data embedded in flat files—such as pipe-delimited subcategories within a comma-delimited file (Product,Categories\nLaptop,"Electronics|Computers|Hardware")—needs parsing logic to extract and normalize. Multi-line records where a single logical record spans multiple physical lines (common in address fields or comments) must be reassembled before processing. Date and time format inconsistencies are particularly troublesome, as files may contain dates in various formats (MM/DD/YYYY, DD-MM-YYYY, YYYY-MM-DD) or ambiguous formats (01/02/2024 could be January 2nd or February 1st depending on regional settings), requiring explicit date parsing with format masks in Pentaho to avoid misinterpretation.

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