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Real-time data synchronization ensures that changes made in one system are immediately reflected in other connected systems. This maintains data consistency across your enterprise architecture, particularly important when connecting operational systems like CRMs, ERPs, and databases.
Consider the following scenario:
// Stacksync connection between Salesforce and PostgreSQL
{
"source": {
"type": "salesforce",
"object": "Account"
},
"target": {
"type": "postgres",
"table": "accounts"
},
"sync_type": "bidirectional",
"field_mappings": [
{"source": "Id", "target": "sf_id"},
{"source": "Name", "target": "account_name"},
{"source": "Industry", "target": "industry_type"}
]
}
This configuration creates a real-time connection that might be visualized as:
When properly configured, any update to an account in Salesforce will automatically update the corresponding record in PostgreSQL, and vice versa. This bi-directional flow ensures that both systems maintain the same data state at all times.
One-way synchronization pushes changes from a source system to a target system without reflecting changes made in the target back to the source.
{
"sync_type": "one_way",
"source_to_target": true,
"target_to_source": false
}
This is useful when you have a clear "system of record" that should propagate changes to downstream systems, but those systems shouldn't push changes back upstream.
Example use case: Syncing customer data from your CRM (master record) to your marketing automation platform.
Bi-directional synchronization allows changes to flow in both directions, keeping two systems perfectly in sync regardless of where changes originate.
{
"sync_type": "bidirectional",
"conflict_resolution": "last_write_wins"
}
With Stacksync, bi-directional sync includes sophisticated conflict resolution to handle scenarios where the same record is modified in both systems simultaneously.
Example use case: Keeping customer information consistent between your CRM and ERP, allowing updates from either sales or finance teams.
For complex enterprise architectures, Stacksync supports synchronizing data across multiple systems in a hub-and-spoke model.
{
"sync_hub": "postgres_data_hub",
"connected_systems": [
{"system": "salesforce", "objects": ["Account", "Contact"]},
{"system": "netsuite", "objects": ["Customer", "Contact"]},
{"system": "zendesk", "objects": ["Organization", "User"]}
]
}
This pattern uses a central database as the coordination point, ensuring changes propagate correctly across all connected systems.
Example use case: Maintaining consistent customer records across CRM, ERP, support ticketing, and data warehouse systems.
The key differences are:
Unlike the MySQL cascading changes discussed in the original article, real-time synchronization with Stacksync is highly recommended for production environments:
When customer data exists in multiple systems (CRM, ERP, support), keeping it consistent manually is nearly impossible. Real-time sync automatically propagates updates, ensuring everyone works with accurate information.
Custom API integrations require constant maintenance as systems change. Stacksync's connector-based approach adapts to schema changes and API updates automatically.
Customer service representatives can view and update information in their native tools while changes flow to other departments' systems instantly.
Marketing and sales teams get immediate insights when customer data from operational systems flows in real-time to analytics platforms.
Before implementation, document which fields need to be synchronized between systems and define the mapping logic:
{
"field_mappings": [
{
"source": "CustomerID",
"target": "client_id",
"transformation": "none"
},
{
"source": "CustomerName",
"target": "full_name",
"transformation": "none"
},
{
"source": "PhoneNumber",
"target": "phone",
"transformation": "format_phone"
}
]
}
Consider:
When both systems update the same record simultaneously, you need rules to resolve conflicts:
{
"conflict_resolution": {
"strategy": "field_level_priority",
"field_rules": [
{"field": "email", "priority_system": "crm"},
{"field": "billing_address", "priority_system": "erp"},
{"field": "phone", "priority_system": "last_updated"}
]
}
}
Common strategies include:
Configure how to handle synchronization errors:
{
"error_handling": {
"retry_strategy": {
"max_attempts": 5,
"backoff_factor": 1.5,
"initial_delay_seconds": 30
},
"notification": {
"channels": ["slack", "email"],
"threshold": "critical"
},
"fallback": {
"action": "queue_for_manual_review"
}
}
}
Recommendations:
Begin with non-critical data to validate your implementation:
For large datasets, consider these performance optimizations:
{
"performance_settings": {
"batch_size": 1000,
"parallelism": 5,
"change_detection": "database_triggers",
"index_recommendation": true
}
}
Key strategies:
Problem: Synchronizing every field between systems creates unnecessary network traffic and increases conflict potential.
Solution: Only sync the fields that truly need to be shared between systems. For Salesforce to Postgres sync, consider:
{
"field_selection": "explicit",
"include_fields": [
"Id", "Name", "Email", "Phone", "LastModifiedDate"
],
"exclude_fields": [
"CreatedById", "Internal_Notes__c", "SystemModstamp"
]
}
Problem: Invalid data from one system propagates to others, potentially causing cascading errors.
Solution: Implement validation rules within your sync configuration:
{
"validation_rules": [
{
"field": "email",
"rule": "email_format",
"on_error": "reject_record"
},
{
"field": "phone",
"rule": "regex",
"pattern": "^\\+[1-9]\\d{1,14}$",
"on_error": "standardize_format"
}
]
}
Problem: Synchronization issues go undetected until they cause significant problems.
Solution: Set up comprehensive monitoring:
{
"monitoring": {
"metrics": ["sync_latency", "error_rate", "throughput"],
"alerting": {
"latency_threshold_ms": 5000,
"error_rate_threshold": 0.01,
"consecutive_failures_threshold": 3
},
"logging": {
"level": "info",
"retention_days": 30
}
}
}
Problem: Updates trigger endless synchronization loops between systems.
Solution: Configure Stacksync to detect and prevent circular updates:
{
"loop_prevention": {
"enabled": true,
"detection_window_seconds": 60,
"max_update_count": 2
}
}
Here's a step-by-step example of implementing bi-directional sync between Salesforce and PostgreSQL:
First, ensure your PostgreSQL database has appropriate tables:
CREATE TABLE accounts (
id SERIAL PRIMARY KEY,
sf_id VARCHAR(18) UNIQUE,
account_name VARCHAR(255),
industry_type VARCHAR(100),
annual_revenue DECIMAL(18,2),
last_modified TIMESTAMP,
last_sync TIMESTAMP
);
CREATE INDEX idx_accounts_sf_id ON accounts(sf_id);
Set up the connection in Stacksync's interface:
{
"connection_name": "SF_Postgres_Accounts",
"source": {
"type": "salesforce",
"credentials": {
"auth_type": "oauth2",
"instance_url": "https://yourinstance.salesforce.com"
},
"object": "Account"
},
"target": {
"type": "postgres",
"credentials": {
"host": "your-postgres-host.example.com",
"port": 5432,
"database": "customer_data",
"schema": "public"
},
"table": "accounts"
}
}
Map the fields between systems:
{
"field_mappings": [
{"source": "Id", "target": "sf_id"},
{"source": "Name", "target": "account_name"},
{"source": "Industry", "target": "industry_type"},
{"source": "AnnualRevenue", "target": "annual_revenue"},
{"source": "LastModifiedDate", "target": "last_modified"}
],
"mapping_options": {
"handle_nulls": "preserve",
"timestamp_format": "iso8601"
}
}
Define how the synchronization should behave:
{
"sync_settings": {
"sync_type": "bidirectional",
"initial_sync": {
"strategy": "full_load"
},
"ongoing_sync": {
"mode": "real_time",
"polling_interval_seconds": 30
},
"conflict_resolution": {
"strategy": "last_write_wins",
"timestamp_field": "LastModifiedDate"
}
}
}
Ensure you'll be notified of any issues:
{
"error_handling": {
"retry_strategy": {
"max_attempts": 3,
"backoff_factor": 2.0
},
"notifications": {
"email": ["data-team@yourcompany.com"],
"slack": "#data-sync-alerts"
}
},
"monitoring": {
"dashboard_enabled": true,
"log_level": "info",
"metric_collection": ["latency", "throughput", "error_rate"]
}
}
Real-time data synchronization with Stacksync provides significant benefits for enterprises needing to maintain consistent data across multiple systems. Unlike database-specific features like MySQL cascading changes, Stacksync's cross-platform approach offers greater flexibility, control, and visibility while simplifying the complexity of connecting diverse applications.
By following the best practices outlined in this article and avoiding common pitfalls, you can implement robust synchronization that delivers immediate business value through improved data consistency, reduced manual effort, and more reliable operations.
