Salesforce API limits restrict data synchronization to PostgreSQL databases, causing integration failures when daily quotas exhaust. Organizations with Enterprise Edition receive 1,000 API calls daily plus 1,000 per user license, making frequent synchronization impossible for large datasets. Change Data Capture, Bulk API 2.0, and database-centric platforms like Stacksync enable continuous synchronization without consuming REST API quotas.

Understanding Salesforce API Limit Constraints

Salesforce enforces strict daily API call limits that impact integration architecture decisions.

API Quota Breakdown by Edition

Daily API call allocations: - Developer Edition: 15,000 calls per 24 hours - Enterprise Edition: 1,000 base + 1,000 per user license - Unlimited Edition: 1,000 base + 1,000 per user license - Performance Edition: 1,000 base + 1,000 per user license

Common consumption patterns: - Single record query: 1 API call - Batch query (2,000 records): 1 API call - Composite API request: 1 API call per subrequest - Bulk API query: Does not consume REST API quota

Why Traditional Sync Methods Fail

Organizations attempting hourly Salesforce to Postgres synchronization face three limiting factors:

1. Polling frequency constraints - Querying 50 objects hourly consumes 1,200 daily API calls (50 objects × 24 hours)
2. Record volume scaling - Large datasets require multiple paginated queries, each consuming quota
3. Concurrent integration conflicts - Multiple systems sharing the same API pool create resource competition

Method 1: Salesforce Change Data Capture

Change Data Capture provides near-real-time event streams for record changes without consuming REST API limits.

CDC Implementation Steps

Enable Change Data Capture in Salesforce

• Navigate to Setup → Integrations → Change Data Capture
• Select standard objects (Account, Contact, Opportunity, etc.)
• Enable custom objects requiring synchronization
• Salesforce generates change events automatically

Configure Event Subscription

• Use CometD protocol to subscribe to change event channels
• Implement long-polling connection for event delivery
• Process ChangeEventHeader containing operation type, entity ID, and changed fields
• Handle event replay using ReplayId for guaranteed delivery

PostgreSQL Data Pipeline

• Parse incoming change events in JSON format
• Map Salesforce field names to PostgreSQL column names
• Execute INSERT, UPDATE, or DELETE operations based on event type
• Maintain replication lag monitoring to ensure synchronization freshness

CDC Advantages and Limitations

Benefits

• Zero REST API quota consumption
• Sub-second change notification latency
• Selective field change tracking reduces processing overhead
• Automatic retry through replay mechanism

Constraints

• Requires Salesforce Enterprise Edition or higher
• 24-hour event retention window limits recovery options
• Custom CDC implementation requires significant development effort
• Long-polling connections need robust error handling

Method 2: Bulk API 2.0 Optimization

Bulk API 2.0 processes large data volumes through asynchronous jobs that operate outside REST API limits.

Bulk API Best Practices

Job Configuration Strategy

Create optimized bulk query jobs:

Job parameters:
- Operation: query
- Object: Account
- Content type: CSV
- Column delimiter: COMMA
- Line ending: LF

SOQL Query Optimization

Design queries to minimize result size: - Select only required fields instead of wildcards - Apply filter criteria to reduce record counts - Use indexed fields in WHERE clauses for performance - Avoid SOQL queries with cross-object relationships when possible

Batch Processing Schedule

Implement daily synchronization windows: 1. Submit bulk query job during off-peak hours (midnight - 4 AM) 2. Poll job status every 30 seconds until completion 3. Download result CSV files in parallel 4. Parse CSV and bulk insert to PostgreSQL using COPY command 5. Update synchronization metadata with last run timestamp

Bulk API Performance Metrics

Expected Throughput

• 10 million records: 30-45 minutes processing time
• CSV download bandwidth: 50-100 MB/s depending on region
• PostgreSQL COPY insertion: 100,000-500,000 rows/second

Resource Consumption

• Bulk API quota: 15,000 batches per rolling 24 hours
• File storage: Temporary CSV files require local disk space
• Memory overhead: Stream processing recommended for large files

Method 3: Database-Centric Sync with Stacksync

Stacksync eliminates API limit concerns through native database synchronization architecture.

How Stacksync Bypasses API Constraints

Core architectural differences:

1. Direct database replication - Stacksync maintains bidirectional sync channels that replicate changes without API intermediaries
2. Consolidated API usage - Single connection per Salesforce org regardless of integration count
3. Intelligent change detection - Monitors Salesforce modification timestamps to pull only updated records
4. Built-in rate limiting - Automatic throttling prevents quota exhaustion across all connected systems

Implementation Timeline

Day 1: Initial Setup

• Connect Salesforce org with OAuth authentication
• Configure PostgreSQL database connection
• Map Salesforce objects to database tables
• Select synchronization direction (one-way or bidirectional)

Day 2-3: Historical Data Migration

• Stacksync performs initial bulk transfer using optimized API patterns
• Progress monitoring through built-in dashboard
• Automatic retry handling for transient failures
• Zero downtime for existing Salesforce operations

Day 4+: Continuous Synchronization

• Real-time change detection activated
• Sub-minute synchronization latency achieved
• Comprehensive audit logging for compliance
• Built-in data validation and transformation

Hybrid Approach for Optimal Results

Organizations requiring maximum flexibility implement multi-method synchronization strategies.

Recommended Architecture

Real-Time Critical Data (CDC or Stacksync)

• Opportunity stage changes
• Case status updates
• Contact information modifications
• Account ownership transfers

Batch Analytics Data (Bulk API)

• Historical trend analysis datasets
• Data warehouse full refreshes
• Compliance audit exports
• Archived record transfers

Configuration Objects (Manual/Scheduled)

• Picklist value definitions
• Custom field metadata
• Workflow rules documentation
• User permission sets

Monitoring and Alerting

Implement comprehensive observability across synchronization methods:

Key Metrics to Track

• API quota utilization percentage (alert at 80% threshold)
• Synchronization lag time (alert if exceeds 5 minutes for critical objects)
• Failed record count (alert on any failures for financial data)
• Data consistency validation results (daily checksum comparisons)

Alert Escalation Workflow

1. Warning notification at 80% API quota consumption
2. Critical alert at 95% with automatic rate limiting
3. Emergency failover to Bulk API if quota exhausted
4. Executive notification if synchronization lag exceeds SLA

Getting Started Recommendations

Organizations should evaluate synchronization requirements before selecting implementation methods.

Choose Stacksync If

• API limit management creates operational burden
• Multiple integrations share Salesforce API quota
• Sub-minute synchronization latency required
• Engineering resources limited for custom development
• Compliance audit trails needed

Choose Custom CDC If

• Real-time requirements mandate sub-second latency
• Engineering team available for long-term maintenance
• Selective object synchronization reduces scope
• Existing event-driven architecture in place

Choose Bulk API If

• Daily batch synchronization meets business requirements
• Large historical datasets need periodic refresh
• Engineering resources available for job orchestration
• Cost optimization prioritized over real-time access

Stacksync provides 14-day free trials enabling teams to validate synchronization architecture before committing to custom development timelines.