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Heroku Connect provides data synchronization between Salesforce and Heroku Postgres, allowing developers to build applications using familiar database interfaces while Salesforce remains the system of record. For basic scenarios, this works. But as data volumes grow and operational complexity increases, the architecture creates performance bottlenecks that pull engineering teams into constant troubleshooting instead of building features that drive business value.
The core issue is architectural. Heroku Connect relies on polling intervals that check Salesforce for changes every 10 minutes. When your application needs current data, not data from 10 minutes ago, this built-in latency becomes a liability. Add API quota consumption, heavy maintenance cycles, and degraded performance with large tables, and you have a platform that forces reactive fixes rather than enabling proactive operations.
The 10-minute polling interval means your Heroku Postgres database always contains stale data. For reporting dashboards, this might be acceptable. For operational applications where users expect current information, the delay creates friction.
Consider a sales team updating opportunities in Salesforce. Their internal portal, built on Heroku Postgres, shows outdated pipeline data until the next sync cycle completes. Users learn not to trust the numbers. They start checking Salesforce directly, defeating the purpose of the integrated portal.
The business impact compounds. Analytics based on delayed data produce unreliable insights. Automated workflows fire based on outdated conditions. Customer-facing applications display information that no longer reflects reality.
Heroku Connect consumes Salesforce API calls with every polling cycle. Each interval queries mapped objects to detect changes. As your dataset grows or update frequency increases, API usage scales accordingly.
Salesforce enforces daily API limits based on your license tier. When Heroku Connect's polling exhausts these limits, synchronization pauses until the quota resets. Data stops flowing. Applications relying on fresh data fail silently or display stale information without warning.
Organizations tracking this issue often discover their integration tool consumes 40-60% of available API calls before any custom integrations run. The solution becomes either upgrading Salesforce licenses, reducing sync frequency, or finding an alternative approach that uses API resources more efficiently.
Schema changes in Salesforce trigger table alterations in Heroku Postgres. When mapped tables contain millions of rows, these alterations lock the synchronization pipeline. Heavy maintenance cycles can extend for hours or days, bringing data flow to a standstill.
The pattern repeats whenever business requirements evolve. Adding a custom field to a frequently-used Salesforce object requires planning around potential downtime. Removing or renaming fields carries similar risk. Development teams learn to batch schema changes and schedule them during low-activity periods, adding coordination overhead to routine operations.
Engineering resources shift from building features to managing integration stability. The hidden cost accumulates in delayed roadmaps and developer frustration.
Heroku Connect generates queries against your Postgres database during each sync cycle. As tables grow, these queries become increasingly expensive. Database CPU spikes during synchronization. Application queries slow as resources compete.
Some organizations implement table partitioning to mitigate performance issues. Others increase database instance size. Both approaches add complexity and cost without addressing the underlying architectural mismatch between polling-based sync and real-time operational needs.
Sync failures become more common as table size increases. Failed records require manual investigation. Retry logic helps but cannot eliminate the fundamental constraint: polling architectures scale poorly against large datasets.
The alternative to polling is event-driven synchronization. Rather than checking for changes on a schedule, event-driven systems capture changes as they occur and propagate them immediately.
Stacksync uses this approach. When a record changes in Salesforce, a webhook triggers near-instant synchronization to your connected database. The data arrives in milliseconds instead of minutes. Your applications access current information without waiting for polling cycles.
Event-driven sync removes the fixed interval constraint. Changes propagate as they happen. Your Heroku Postgres replica stays current with Salesforce rather than perpetually lagging behind.
For operational applications, this shift transforms user experience. Sales portals show live pipeline data. Customer service tools display current account information. Internal dashboards reflect reality rather than a 10-minute-old snapshot.
The architectural difference also eliminates the unpredictability of batch windows. With polling, you never know exactly when your data will refresh. With event-driven sync, updates arrive consistently within milliseconds of the source change.
Event-driven architectures consume API resources only when data actually changes. Instead of repeatedly querying unchanged records, the system responds to actual events. API usage becomes proportional to business activity rather than fixed polling overhead.
Stacksync adds intelligent rate limit management that optimizes every Salesforce interaction. The platform automatically adjusts sync behavior based on data traffic and your API resource budget. This prevents quota exhaustion during peak activity while maintaining reliable data flow.
Organizations running both Heroku Connect and custom Salesforce integrations often find the combination pushes them against API limits. Replacing the polling-based component with event-driven sync frees capacity for other integration needs.
Schema changes should not halt data synchronization. Stacksync handles field additions, modifications, and deletions without entering maintenance states that lock your pipeline.
When Salesforce schema evolves, the platform adapts. New fields become available in your database. Changed data types map correctly. The synchronization continues without requiring scheduled maintenance windows or manual intervention.
This approach removes the coordination overhead of planning schema changes around downtime. Development teams modify Salesforce objects based on business requirements, and the sync layer adjusts automatically.
Event-driven sync handles large datasets efficiently because it processes changes rather than scanning entire tables. The system captures what changed and applies those specific updates. Database load remains proportional to change volume, not total table size.
Stacksync processes up to 1 million Salesforce records per minute. The architecture handles growing datasets without the query-based bottlenecks that plague polling systems. Table size stops being a performance constraint.
Organizations synchronizing millions of records find consistent performance regardless of dataset growth. The platform scales with data volume without requiring database upgrades or architectural workarounds.
Heroku Connect provides write-back capability, but it operates as a separate mechanism with its own latency and limitations. True bidirectional sync treats both systems as equal participants in a unified data layer.
Many applications need to write data back to Salesforce from Postgres. Internal tools that create or update records. Integration workflows that enrich Salesforce data from external sources. Customer portals that accept input and need it reflected in the CRM.
With unidirectional sync plus write-back, you manage two distinct data flows with different behaviors. Changes from Salesforce arrive on polling intervals. Changes from Postgres return through a separate mechanism. Consistency becomes difficult to guarantee.
Bidirectional sync unifies these flows. Changes in either system propagate to the other in real-time. Your database and Salesforce maintain consistency without requiring application logic to manage synchronization timing.
Salesforce objects have relationships. Accounts relate to Contacts. Opportunities link to Products. Custom objects reference standard objects in hierarchical structures.
Heroku Connect handles basic relationships, but complex hierarchies create synchronization challenges. Parent records must exist before child records reference them. Update order matters. Failure to sequence correctly creates orphaned records or sync errors.
Stacksync maintains internal mappings of record relationships across systems. The platform automatically sequences record creation and association. Complex hierarchies synchronize correctly without requiring application-level orchestration.
Real-world architectures rarely involve just Salesforce and Postgres. CRM data flows to ERPs. Database records sync to data warehouses. Multiple SaaS applications share customer information.
Stacksync supports chained synchronization across multiple systems. A central database can serve as a hub, with changes propagating to Salesforce, NetSuite, Snowflake, and other connected platforms. Data consistency extends across your entire ecosystem rather than stopping at one integration pair.
This approach simplifies architectures that previously required multiple point-to-point integrations. Instead of managing separate connections between each system pair, you establish a synchronized data layer that maintains consistency across all participants.
Understanding the specific differences helps evaluate which approach fits your operational requirements.
| Capability | Heroku Connect | Stacksync |
|---|---|---|
| Sync Latency | 10-minute polling intervals; data always lags behind source | Event-driven millisecond sync; data stays current |
| API Consumption | Continuous polling burns quota regardless of changes | API calls only when data changes; intelligent rate management |
| Schema Changes | Heavy maintenance locks sync for hours or days | Graceful adaptation without service interruption |
| Large Table Performance | Query-based sync degrades as tables grow | Change-based processing maintains consistent performance |
| Sync Direction | One-way with separate delayed write-back mechanism | Native bidirectional real-time synchronization |
| Hosting Requirement | Requires Heroku hosting environment | Works with any cloud database or hosting provider |
| Error Resolution | Limited visibility into sync failures and causes | Issue management dashboard with record-level controls |
Event-driven sync eliminates the latency tax that polling architectures impose on operational applications.
API quota exhaustion and heavy maintenance cycles create unpredictable downtime that compounds as data volumes grow.
Evaluate whether your use case requires current data or can tolerate ten minute delays before choosing an approach.
The shift from polling to event-driven synchronization delivers measurable improvements across technical and operational dimensions.
Organizations using Heroku Connect report that data engineers spend 50-60% of their time troubleshooting sync issues and pipeline failures. This maintenance tax consumes resources that could drive product development or operational improvements.
Event-driven sync with automatic error handling shifts this balance. When issues occur, the platform provides visibility into specific failed records and the ability to retry or revert without interrupting overall service. Engineering teams monitor rather than constantly repair.
Polling-based sync creates unpredictable API consumption patterns. Monthly costs fluctuate based on sync frequency and data volume. Organizations struggle to forecast integration expenses or allocate API quota budgets.
With event-driven sync tied to actual data changes, API usage becomes proportional to business activity. Busy months consume more; quiet months consume less. But the pattern follows business operations rather than arbitrary polling schedules.
Heroku Connect requires your database to run on Heroku. This constraint limits architectural options and may not align with existing cloud investments.
Stacksync works with any Postgres deployment: Heroku Postgres, AWS RDS, Google Cloud SQL, Azure Database for PostgreSQL, or self-hosted instances. Your integration layer does not dictate your hosting strategy.
Data synchronization platforms must meet security requirements that increase with organizational scale. Stacksync provides SOC 2 Type II, HIPAA BAA, GDPR, ISO 27001, and CCPA compliance certifications.
The platform does not store your data. Information passes through during synchronization but is not persisted. Encryption protects data in transit. Role-based access controls, multi-factor authentication, and single sign-on integrations support enterprise security policies.
Replacing an existing integration requires planning, but the transition does not require extended downtime or complex data migration.
Before migration, map your current Heroku Connect configuration:
This inventory informs the Stacksync configuration and helps identify potential improvements beyond simple replacement.
Stacksync configuration uses a no-code interface. Connect your Salesforce org and database, select objects to synchronize, and map fields. The platform suggests automatic mappings based on field names and types.
Many organizations run Stacksync in parallel with Heroku Connect during transition. Both platforms synchronize data while you validate that the new configuration meets requirements. This approach minimizes risk by maintaining the existing integration as a fallback.
Once validation confirms correct behavior, redirect applications to use the Stacksync-synchronized data. Decommission the Heroku Connect integration.
Post-migration, explore capabilities that were not available with the previous platform. Implement bidirectional sync for workflows that previously required separate write-back handling. Add connected systems to extend data consistency across your architecture. Configure workflow automation to respond to data events.
Heroku Connect served a purpose when batch synchronization met operational needs. For organizations where 10-minute data delays create business friction, the polling architecture becomes a constraint rather than a solution.
The symptoms are recognizable: users who do not trust application data, API quota warnings that arrive mid-month, schema changes that require scheduled maintenance, and engineers who spend more time fixing integrations than building features.
Event-driven synchronization addresses these issues architecturally. Data arrives in milliseconds instead of minutes. API resources are consumed efficiently. Schema changes flow through without disruption. Engineering focus shifts from maintenance to innovation.
Ready to see how real-time sync transforms your Salesforce-to-Postgres integration? Book a Stacksync demo to discuss your specific use case and see the platform in action.
