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If your enterprise struggles with fragmented data, database real-time synchronization solves it by instantly reflecting changes across CRMs, ERPs, and databases. With Stacksync, you ensure accuracy, reduce manual fixes, and keep every system aligned for smoother operations.
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:
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"]
}
}
In 2025, enterprises can’t rely on outdated ETL or triggers alone. Database real-time synchronization with Stacksync ensures every system stays accurate, consistent, and ready for decision-making. By applying these best practices, you’ll reduce manual work, prevent costly errors, and unlock true operational efficiency. Try Stacksync to streamline your enterprise data sync today.
Q1. What is database real-time synchronization?
Database real-time synchronization is the process of instantly updating data across multiple systems so every change is reflected everywhere at once. This ensures consistent, accurate information in CRMs, ERPs, databases, and other connected tools.
Q2. Why is Stacksync better than traditional database triggers?
Unlike triggers that only work within a single database, Stacksync provides cross-platform synchronization. It offers no-code setup, 200+ connectors, built-in conflict resolution, and monitoring features designed for enterprise operations.
Q3. What are the main benefits of real-time synchronization in 2025?
Q4. What pitfalls should companies avoid in database real-time synchronization?
The most common pitfalls include syncing unnecessary fields, ignoring data validation, lacking monitoring, and creating circular loops. Using Stacksync best practices helps prevent these issues.
Q5. How do you implement Stacksync for Salesforce and PostgreSQL?
Implementation involves:
