Data Migration Patterns

Consensus Best Practices

Data Import Strategies

Pattern 1: Data Import Wizard

When to use: Small datasets (up to 50,000 records), simple imports, non-technical users.

Implementation approach:

Why it’s recommended: Data Import Wizard is simple and doesn’t require code. It’s ideal for small, one-time imports.

Key Points:

Pattern 2: Data Loader Patterns

When to use: Larger datasets, automated imports, command-line operations.

Implementation approach:

Why it’s recommended: Data Loader handles larger volumes and can be automated. It’s ideal for regular imports and larger datasets.

Key Points:

Pattern 3: Bulk API for Migration

When to use: Very large datasets (millions of records), programmatic imports.

Implementation approach:

Why it’s recommended: Bulk API is designed for large volumes and provides better performance than REST API. It’s essential for large-scale migrations.

Key Points:

Data Transformation Patterns

Pattern 1: Field Mapping Strategies

When to use: Mapping fields from source system to Salesforce.

Implementation approach:

Why it’s recommended: Field mapping ensures data is correctly transformed and imported. This is essential for system migrations.

Key Points:

Pattern 2: Data Cleansing Patterns

When to use: Cleaning data before or during migration.

Implementation approach:

Why it’s recommended: Data cleansing improves data quality and reduces errors during migration. This is essential for successful migrations.

Key Points:

Pattern 3: Data Enrichment Patterns

When to use: Enriching data during migration with additional information.

Implementation approach:

Why it’s recommended: Data enrichment adds value during migration and ensures complete records. This improves data quality.

Key Points:

Data Validation During Migration

Pattern 1: Pre-Migration Validation

When to use: Validating data before starting migration.

Implementation approach:

Why it’s recommended: Pre-migration validation catches issues early and prevents failed migrations. This saves time and reduces errors.

Key Points:

Pattern 2: During-Migration Validation

When to use: Validating data during migration process.

Implementation approach:

Why it’s recommended: During-migration validation catches issues as they occur and allows processing to continue. This improves migration success rates.

Key Points:

Pattern 3: Post-Migration Validation

When to use: Validating data after migration completes.

Implementation approach:

Why it’s recommended: Post-migration validation ensures migration success and data integrity. This is essential for production migrations.

Key Points:

Rollback Strategies

Pattern 1: Backup Strategies

When to use: Creating backups before migration.

Implementation approach:

Why it’s recommended: Backups enable rollback in case of migration failures. This is essential for production migrations.

Key Points:

Pattern 2: Rollback Procedures

When to use: Rolling back data after migration failures.

Implementation approach:

Why it’s recommended: Rollback procedures enable recovery from migration failures. This is essential for production reliability.

Key Points:

Pattern 3: Data Recovery Patterns

When to use: Recovering data after migration issues.

Implementation approach:

Why it’s recommended: Data recovery patterns enable recovery from migration issues. This ensures data integrity.

Key Points:

Migration Best Practices

Migration Planning

Testing Strategies

Migration Monitoring

Q&A

Q: What is the best tool for importing data into Salesforce?

A: The best tool depends on data volume and requirements: Data Import Wizard for small datasets (up to 50,000 records) and simple imports, Data Loader for larger datasets and automated imports, Bulk API for very large datasets (millions of records) and programmatic control, ETL tools (MuleSoft, Boomi) for complex transformations and multi-system migrations.

Q: How do I handle record relationships during migration?

A: Use External IDs to map relationships. Create External ID fields on parent objects, populate them with source system IDs, then reference those External IDs in child records. This enables idempotent operations and allows you to migrate parent records first, then child records referencing parent External IDs.

A: Validate data before migration by: (1) Checking data quality (completeness, accuracy, format), (2) Validating relationships (parent records exist), (3) Checking business rules (validation rules, required fields), (4) Testing with sample data first, (5) Using staging objects for complex validation.

Q: How do I implement rollback strategies for data migrations?

A: Implement rollback by: (1) Backing up data before migration, (2) Logging all operations (what was created/updated/deleted), (3) Using External IDs to track source records, (4) Creating rollback scripts that can reverse operations, (5) Testing rollback procedures in sandbox before production.

Q: What is the difference between upsert and insert/update operations?

A: Upsert uses External IDs or a specified field to match existing records and update them, or create new records if no match is found. Insert always creates new records. Update requires existing record IDs. Use upsert for idempotent operations where you want to update existing records or create new ones based on External IDs.

Q: How do I handle large data migrations (millions of records)?

A: For large migrations: (1) Use Bulk API or Bulk API 2.0 for high-volume operations, (2) Batch processing - split into smaller batches, (3) Parallel processing - run multiple batches concurrently, (4) Monitor progress - track batch completion and failures, (5) Error handling - log errors and retry failed records, (6) Test with sample data first.

Q: What should I consider when migrating data between Salesforce orgs?

A: When migrating between orgs: (1) Field mapping - map source fields to target fields, (2) Data transformation - transform data to match target org structure, (3) Relationship preservation - maintain relationships using External IDs, (4) Permission considerations - ensure user has access to create/update records, (5) Validation rules - understand target org validation rules, (6) Test in sandbox first.

Q: How do I ensure data migration is idempotent?

A: Make migrations idempotent by: (1) Using External IDs for record matching, (2) Using upsert operations instead of insert/update, (3) Checking for existing records before creating, (4) Logging operations to track what was done, (5) Testing with re-runs to ensure same results.

Edge Cases and Limitations

Edge Case 1: Large Data Volumes (Millions of Records)

Scenario: Migrating millions of records that exceed Bulk API job limits or require extended processing time.

Consideration:

Edge Case 2: Complex Relationship Dependencies

Scenario: Migrating records with complex parent-child relationships where child records must reference parent records that don’t exist yet.

Consideration:

Edge Case 3: Data Type Mismatches and Format Issues

Scenario: Source system data types don’t match Salesforce field types, or data formats are incompatible.

Consideration:

Edge Case 4: Validation Rule Failures During Migration

Scenario: Records fail validation rules during migration, causing partial failures.

Consideration:

Edge Case 5: Concurrent Migration Operations

Scenario: Multiple migration jobs running concurrently, causing lock contention or data conflicts.

Consideration:

Limitations