SQL Migration

Migrate data between MongoDB and SQL databases with intelligent type mapping, schema generation, and bidirectional support. Use SQL tables, views, or queries as sources for importing into MongoDB, or export MongoDB collections directly to SQL databases.

SQL Source Types

Import data from SQL databases into MongoDB using these source options.

Source Type	Description	Use Case
SQL Table	Import all rows from a SQL table	Full table migration to MongoDB collection
SQL View	Import from a SQL view (pre-joined/filtered data)	Import denormalized data from multiple tables
SQL Query	Import using custom SELECT statement	Complex joins, filters, or transformations at source

SQL Target Types

Export MongoDB data to SQL databases using these target options.

Target Type	Description	Use Case
SQL Table	Export directly to a SQL database table	Live migration with Insert/Upsert/DropInsert modes
SQL Script	Generate SQL INSERT/UPDATE statements as a file	Review before execution, version control, manual apply

Supported SQL Databases

VisuaLeaf supports migration to and from major relational databases.

Database	Versions	Features
MySQL	5.7+, 8.0+	JSON columns, SSL/TLS, connection pooling
PostgreSQL	10+, 12+, 14+, 16+	JSONB columns, array types, SSL/TLS
SQL Server	2016+, 2019+, 2022+	Windows/Linux, Azure SQL, SSL/TLS
Oracle	12c+, 19c+, 21c+	JSON columns, connection pooling

Type Mapping

VisuaLeaf automatically maps MongoDB types to appropriate SQL types, with manual override options.

MongoDB to SQL Type Mapping

MongoDB Type	MySQL	PostgreSQL	SQL Server
String	VARCHAR / TEXT	VARCHAR / TEXT	NVARCHAR / NTEXT
Number (Int)	INT / BIGINT	INTEGER / BIGINT	INT / BIGINT
Number (Double)	DOUBLE	DOUBLE PRECISION	FLOAT
Boolean	TINYINT(1)	BOOLEAN	BIT
Date	DATETIME	TIMESTAMP	DATETIME2
ObjectId	CHAR(24)	CHAR(24)	CHAR(24)
Object / Array	JSON	JSONB	NVARCHAR(MAX)

SQL Field Configuration

Configure SQL column properties for each mapped field.

• Nullable - Allow NULL values in the column
• Primary Key - Set as primary key (typically _id → id)
• Auto Increment - Enable auto-increment for numeric primary keys
• Unique - Add unique constraint to the column
• Index - Create index on the column for faster queries
• Default Value - Set default value for new rows

Write Modes

Control how data is written to the target SQL table.

Mode	Behavior	Use Case
Insert	Insert new rows only, fail on duplicates	First-time migration, append-only data
Upsert	Insert new rows, update existing (by primary key)	Incremental sync, keep data up-to-date
Drop & Insert	Drop table, recreate schema, insert all rows	Full refresh, schema changes

Data Masking

Protect sensitive data during migration by applying masking transformations. Data masking allows you to export data for development, testing, or analytics while obscuring personally identifiable information (PII) and other sensitive fields.

Masking Types

Mask Type	Description	Example
Redact	Replace with fixed placeholder	john@email.com → [REDACTED]
Partial Mask	Show first/last characters only	john@email.com → j***@***.com
Hash	One-way hash (SHA-256)	john@email.com → a8f5f167...
Randomize	Replace with random value of same type	555-1234 → 382-9471
Fake	Generate realistic fake data	John Smith → Jane Doe
Null	Replace with null/empty	secret123 → null

Applying Masks

Apply masking in the field transformation section of your export job:

1. Open field mapping for your export job
2. Click on a sensitive field (email, phone, SSN, etc.)
3. In the transformation script, use built-in masking functions
4. Preview to verify masking is applied correctly

Masking Functions

// Redact completely
return '[REDACTED]';

// Partial mask email
return value.replace(/(.{1}).*@(.*)\.(.{2,})/, '$1***@***.$3');

// Hash value (consistent across runs)
return crypto.sha256(value);

// Randomize phone number
return '555-' + Math.floor(1000 + Math.random() * 9000);

// Null out field
return null;

Common Masking Patterns

Field Type	Recommended Mask	Result
Email	Partial mask	j***@***.com
Phone	Partial mask (last 4)	***-***-1234
SSN / ID	Hash or redact	***-**-6789 or hash
Credit Card	Partial mask (last 4)	****-****-****-1234
Name	Fake data	Random realistic name
Address	Fake or redact	Random address or [REDACTED]
Password	Null or exclude	Don't export passwords

Pro Tips

1. Use SQL Query source to join multiple tables before importing to MongoDB - this creates properly denormalized documents.
2. For large migrations, use batch size control to avoid memory issues. Start with 1000 rows per batch.
3. Generate a SQL Script first to review the SQL before executing against production databases.
4. When exporting nested MongoDB objects to SQL, they're automatically serialized as JSON columns.
5. Use Upsert mode for incremental syncs - it's idempotent and safe to run multiple times.
6. Apply data masking when exporting to development/test environments to protect PII.
7. Test your SQL connection before saving the job using the "Test Connection" button.

Related Features

• Export/Import Data - Full guide to creating export jobs with field mapping and transformations
• Task Manager - Organize, schedule, and monitor migration jobs
• SQL Query - Browse and query SQL tables directly
• Connection Manager - Manage MongoDB and SQL connections