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Synchronizing data between HubSpot and PostgreSQL involves more than copying information from one system to another. It includes structuring data, detecting updates, handling differences between platforms, and managing synchronization over time.
This article explains the key strategies and technical considerations for syncing HubSpot with PostgreSQL. It introduces two-way sync, covers manual and automated methods, and outlines best practices for secure and scalable data integration.
The goal is to support teams working with CRM data who require accurate, real-time information in a database environment for analytics, operations, or application development.
Syncing HubSpot and PostgreSQL means automatically transferring and updating data between the HubSpot CRM system and a PostgreSQL relational database.
This connection allows CRM data to be used in a database environment, where it can support custom queries, reporting tools, data pipelines, and internal applications.
Common use cases include building advanced analytics dashboards, supporting business intelligence workflows, enabling custom application development, and consolidating data from multiple systems into a central location.
Improved reporting: Access to CRM data in a database format enables more flexible and detailed reporting
Reduced manual work: Automation eliminates the need to manually transfer data between systems
Consistent information: Teams across departments work with the same up-to-date data
Real-time updates: Changes in either system can be reflected in the other promptly
Two-way sync (also called bidirectional synchronization) is a data integration method where changes made in either system are automatically reflected in the other. When data is updated in HubSpot, it updates in PostgreSQL, and vice versa.
Unlike one-way sync, which only moves data in a single direction, two-way sync creates a continuous data loop between systems. This approach ensures both platforms contain the same information at all times.
Two-way sync requires more complex setup than one-way sync because it must track changes in both systems, determine which updates to apply, and resolve conflicts when the same record is changed in both places.
![Diagram showing bidirectional data flow between HubSpot and PostgreSQL]
There are three common methods for connecting HubSpot and PostgreSQL, each with different complexity levels and use cases.
Method | Complexity | Cost | Best For |
|---|---|---|---|
Manual Export | Low | Low | Small, one-time migrations |
Custom Scripting | High | Medium | Technical teams, custom needs |
Integration Platforms | Medium | Varies | Ongoing, scalable integrations |
The manual approach involves downloading data from HubSpot and importing it into PostgreSQL. This process includes:
Exporting data from HubSpot as CSV files
Cleaning and formatting the data in a spreadsheet program
Importing the prepared files into PostgreSQL using database tools
This method works for simple, infrequent data transfers but becomes time-consuming and error-prone when done regularly. It doesn't support real-time updates or automated workflows.
Custom scripts use programming to connect HubSpot's API with PostgreSQL. Developers write code that:
Retrieves data from HubSpot using API calls
Transforms the data to match PostgreSQL's structure
Inserts or updates records in the database
Handles errors and logs the results
This approach offers complete control but requires programming skills and ongoing maintenance. Changes to either system might require script updates.
Integration platforms provide ready-made connectors between HubSpot and PostgreSQL. These tools offer:
Visual interfaces for mapping fields between systems
Scheduled or trigger-based sync operations
Built-in error handling and monitoring
Support for complex data transformations
Platforms like Stacksync, Fivetran, and Stitch reduce the technical complexity while supporting enterprise-scale data volumes.
Two-way synchronization between HubSpot and PostgreSQL creates a continuous data loop where changes in either system update the other. This bidirectional HubSpot PostgreSQL sync requires specific technical approaches to work reliably.
Real-time HubSpot data sync can be implemented through two methods:
API polling: Regularly checking HubSpot's API for changes (every few minutes)
Webhooks: Receiving instant notifications when records change in HubSpot
Both methods face technical constraints like API rate limits (HubSpot restricts how many requests you can make) and latency (delays between when changes happen and when they're processed).
When the same record is changed in both HubSpot and PostgreSQL before synchronization occurs, a conflict happens. HubSpot data conflict management requires predefined rules to resolve these situations.
Conflict Scenario | Common Resolution Approach |
|---|---|
Same field changed in both systems | Use most recent change |
Different fields changed | Merge all changes |
Record deleted in one system, updated in other | Requires specific handling rule |
The most common resolution strategies include:
Using the most recent change (timestamp-based)
Designating one system as the "master" for certain fields
Creating alerts for manual review of conflicts
When records are deleted or archived in HubSpot, the synchronization process needs to reflect these changes in PostgreSQL. Common approaches include:
Using a "soft delete" flag in PostgreSQL (marking records as inactive)
Implementing an archive table for removed records
Creating logs of deletion events for audit purposes
These methods ensure that sync deleted HubSpot records are properly managed in the database without losing historical information.
Enterprise HubSpot PostgreSQL integration involves larger data volumes and more complex requirements. Organizations handling thousands or millions of records benefit from these practices:
Implementing incremental syncs that only process changed records
Optimizing database schema design for efficient storage and retrieval
Setting up monitoring to track sync performance and detect issues
Testing changes in a staging environment before applying to production
A well-designed HubSpot database schema maps CRM objects to appropriate PostgreSQL tables. This typically includes:
HubSpot Object | PostgreSQL Table | Common Fields |
|---|---|---|
Contacts | contacts | id, email, first_name, last_name |
Companies | companies | id, name, domain, industry |
Deals | deals | id, amount, stage, close_date |
Each table contains columns that match HubSpot properties, with appropriate data types and constraints. Foreign keys connect related records (like contacts to companies).
Incremental HubSpot data sync processes only records that have changed since the last synchronization. This approach:
Reduces the amount of data transferred in each sync cycle
Decreases processing time and resource usage
Minimizes API calls to stay within rate limits
The implementation typically uses timestamps or version numbers to track changes and only processes records modified after the last successful sync.
Data integration monitoring helps ensure sync processes run smoothly. Key metrics to track include:
Success/failure rates of sync operations
Processing time and throughput
Error types and frequencies
Data volume transferred
When issues occur, having error logs and recovery procedures helps restore normal operation quickly and minimize data inconsistencies.
Secure HubSpot data integration protects sensitive information throughout the synchronization process. This includes:
Encrypting data during transfer between systems
Securing stored data in the PostgreSQL database
Limiting access to only authorized users and applications
Maintaining audit trails of data access and changes
Data security relies on two key protection methods:
Encryption in transit: Using HTTPS/TLS to protect data as it moves between HubSpot and PostgreSQL
Encryption at rest: Securing stored data in the database using PostgreSQL's encryption features
PostgreSQL access control further protects data by:
Creating specific database roles with appropriate permissions
Limiting access to sensitive tables or columns
Implementing row-level security for multi-tenant databases
GDPR compliant data integration addresses requirements from regulations like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and others.
Compliance measures include:
Documenting data flows and processing activities
Implementing data retention and deletion policies
Providing mechanisms for data subject access requests
Securing personal data with appropriate controls
Organizations handling regulated data benefit from integration approaches that support these compliance requirements.
Syncing HubSpot and PostgreSQL connects your CRM data with database capabilities, enabling advanced analytics and custom applications. The right approach depends on your technical resources, data volume, and specific requirements.
For simple, one-time transfers, manual exports work well. Custom scripts offer flexibility for technical teams with specific needs. Integration platforms provide the most scalable solution for ongoing synchronization without extensive development.
Two-way sync creates a complete data loop between systems, ensuring both HubSpot and PostgreSQL contain the same up-to-date information. This approach requires careful planning for conflict resolution and deleted record handling.
Large organizations benefit from incremental sync methods, optimized schema design, and comprehensive monitoring. Security and compliance considerations ensure data remains protected throughout the process.
Stacksync offers a HubSpot PostgreSQL integration solution that handles these complexities with automated workflows and built-in monitoring. Book a demo to learn more about streamlining your data integration.
Integration platforms can map data from multiple HubSpot accounts into a single PostgreSQL database by using unique identifiers and namespaces to prevent conflicts between records from different sources.
Apply filters in your integration workflow based on contact properties like lifecycle stage, list membership, or custom fields to selectively sync only the contacts that meet your criteria.
Most integration tools and custom scripts support time-based scheduling, allowing you to run sync operations during nights or weekends when system usage is lower.
PostgreSQL version 10 or higher is typically recommended, with sufficient storage for your data volume and appropriate indexes on frequently queried fields to maintain performance.
Two-way synchronization uses timestamps or change tracking to identify updates in both systems, applies changes according to predefined rules, and resolves conflicts when the same record is modified in both places.
