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Map, validate, and load millions of rows with field validation, data transformation, and secure rollback capabilities.
Drag-and-drop interface to map CSV columns to Firestore document fields with real-time preview.
Automatic data type detection and validation with custom transformation rules.
Handle millions of records efficiently with progress tracking and batch processing.
Comprehensive error reporting with line-by-line validation and skip options.
Secure rollback functionality to undo imports if needed, ensuring data integrity.
Support for complex data structures and nested object creation from flat CSV data.
Select your CSV file and let Fuego analyze its structure and data types.
Use the visual interface to map CSV columns to Firestore document fields.
Preview transformations and validate data integrity before importing (coming soon).
Execute the bulk import with real-time progress tracking.
Fuego can handle CSV files of millions of rows. The practical limit depends on your system's memory, but files up to several GB are typically handled without issues. For extremely large files, Fuego processes data in chunks to maintain performance.
Yes, Fuego provides advanced field mapping capabilities that allow you to map CSV columns to nested object structures in Firestore documents. You can create complex document hierarchies from flat CSV data.
Fuego provides comprehensive error handling with detailed logs showing exactly which rows failed and why. You can choose to skip problematic rows, fix them, or use the rollback functionality to undo partial imports.
Yes, Fuego automatically detects data types and provides validation rules. You can set custom validation criteria, handle null values, and preview data transformations before executing the import.
Start importing CSV files into Firestore with Fuego's powerful visual interface. No coding required.