Nested fields from dotted headers
Column headers with dots, such as address.city, become nested map fields in each Firestore document.
Data ops
Import CSV into Firestore with a powerful GUI
Map, validate, and load millions of rows with field validation, data transformation, and secure rollback capabilities.
Capabilities
Key features for CSV import
Data type validation
Automatic data type detection and validation with custom transformation rules.
Bulk operations
Handle millions of records efficiently with progress tracking and batch processing.
Error handling
Comprehensive error reporting with line-by-line validation and skip options.
Rollback support
Secure rollback functionality to undo imports if needed, ensuring data integrity.
Nested objects
Support for complex data structures and nested object creation from flat CSV data.
Workflow
How it works
01
Upload CSV
Select your CSV file and let Fuego analyze its structure and data types.
02
Set options
Choose the delimiter, the document ID column, the write mode and the columns to import or ignore.
03
Validate
Preview transformations and validate data integrity before importing (coming soon).
04
Import
Execute the bulk import with real-time progress tracking.
Why Fuego
Why choose Fuego for CSV import?
- No coding required - visual interface for all operations
- Handle massive datasets with efficient batch processing
- Comprehensive error handling and data validation
- Real-time preview of data transformations
- Cross-platform support (macOS, Windows, Linux)
FAQ
Frequently asked questions
What is the maximum file size for CSV import?
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.
Can I map CSV columns to nested Firestore fields?
Yes. Name the column with a dotted path, such as address.city, and Fuego writes the value as a nested field. For any other reshaping, a transform script can rewrite each row before it is written.
What happens if the import fails partway through?
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.
Does Fuego support data validation during CSV import?
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.
Get started
Ready to import your CSV data?
Start importing CSV files into Firestore with Fuego's powerful visual interface. No coding required.