Fix Data Type Mismatch During Migration
Resolve data type compatibility errors instantly with AI-powered automated conversion. Fix VARCHAR to INT, DATE formats, DECIMAL precision, and BLOB issues in minutes, not days.
Common Data Type Mismatch Errors
The 7 most frequent data type issues and how AI fixes them automatically
Error:
ERROR: invalid input syntax for type integer: "N/A"AI Solution:
- Detect non-numeric values
- Convert or set to NULL
- Log conversion issues
Error:
ERROR: date format not recognized: "12/31/2024"AI Solution:
- Auto-detect format patterns
- Convert to target format
- Handle timezone conversion
Error:
ERROR: numeric field overflow DECIMAL(10,2) cannot store 12345678.999AI Solution:
- Analyze value ranges
- Adjust precision/scale
- Prevent data truncation
Error:
ERROR: value too long for type character varying(255)AI Solution:
- Detect max field lengths
- Upgrade to TEXT/CLOB
- Handle large objects
Error:
ERROR: invalid input for type boolean: "Y"AI Solution:
- Map Y/N, 1/0, T/F to boolean
- Handle NULL values
- Standardize representation
Error:
ERROR: invalid input value for enum: "pending_review"AI Solution:
- Map source to target values
- Add missing enum values
- Convert to VARCHAR if needed
Error:
ERROR: invalid JSON structure in TEXT fieldAI Solution:
- Validate JSON/XML syntax
- Convert to native JSON type
- Parse and restructure
AI-Powered Automated Fix Process
Resolve data type mismatches in 4 automated steps
AI scans source and target schemas to identify all data type incompatibilities before migration starts.
- Schema comparison
- Data profiling
- Compatibility check
AI automatically creates conversion logic for each mismatch, handling edge cases and NULL values.
- Type casting rules
- Format transformations
- Error handling
Test conversions on sample data to ensure 100% accuracy before applying to production dataset.
- Sample data testing
- Edge case validation
- Accuracy verification
Execute conversions during migration with real-time monitoring and automatic rollback if issues occur.
- Batch processing
- Real-time monitoring
- Auto-rollback safety
Common Data Type Conversions
Automated mapping between database systems
| Source Type | Target Type | Conversion Logic | Success Rate |
|---|---|---|---|
| VARCHAR(n) | INTEGER | Parse numeric, set NULL for non-numeric | 98% |
| DATETIME | TIMESTAMP | Convert with timezone handling | 99% |
| DECIMAL(10,2) | DECIMAL(15,4) | Increase precision, preserve values | 100% |
| CHAR(1) | BOOLEAN | Map Y/N, T/F, 1/0 to true/false | 97% |
| TEXT | JSON | Validate and parse JSON structure | 95% |
| BLOB | BYTEA | Binary data transfer with encoding | 99% |
| ENUM | VARCHAR(50) | Convert enum values to strings | 100% |
| FLOAT | DOUBLE | Increase precision, no data loss | 100% |
People Also Ask
Data type mismatches occur when source and target databases use different data types for the same logical data. Common causes include: different database vendors (Oracle NUMBER vs PostgreSQL NUMERIC), schema evolution over time (VARCHAR expanded to TEXT), implicit type conversions (string "123" to integer 123), and incompatible formats (MM/DD/YYYY vs YYYY-MM-DD dates). AI migration tools detect these mismatches automatically by profiling source data and comparing against target schema constraints.
Prevent data loss by: 1) Profiling source data to understand actual value ranges and formats, 2) Choosing target types with sufficient precision (DECIMAL(15,4) instead of DECIMAL(10,2) if source has larger values), 3) Testing conversions on sample data before full migration, 4) Implementing validation rules to catch conversion errors, 5) Logging all conversion issues for review. AI migration automatically analyzes 100% of source data to recommend optimal target types that preserve all values without truncation or precision loss.
Yes, but it's more complex. If you discover type mismatches mid-migration, you can: 1) Pause the migration, 2) Add conversion logic for the problematic fields, 3) Re-migrate affected tables, or 4) Apply post-migration transformations. However, this extends timeline and risks data inconsistency. AI migration prevents this by detecting ALL type mismatches during the planning phase (before migration starts) and generating conversion logic upfront. This eliminates mid-migration surprises and ensures smooth execution.
Manual fixes typically take 2-5 days per table: 1 day to identify the issue, 1-2 days to write conversion logic, 1 day to test, 1 day to re-migrate. For a 100-table database, that's 200-500 days of work. AI migration fixes all type mismatches automatically in 2-4 hours total: AI profiles all tables simultaneously, generates conversion logic for all mismatches, validates on sample data, and applies during migration. This 100x speed improvement is why AI migration completes in weeks instead of months.
NULL handling depends on target column constraints. If target allows NULL, source NULLs transfer directly. If target requires NOT NULL, you must provide default values or conversion rules. Common scenarios: empty strings ("") to NULL for numeric types, "N/A" or "Unknown" to NULL, invalid dates to NULL. AI migration automatically detects NULL constraints and generates appropriate handling: preserving NULLs where allowed, converting to sensible defaults where required, and flagging cases needing business logic decisions.
Stop Fighting Data Type Errors
Let AI automatically detect and fix all data type mismatches in hours, not days