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Many teams using HubSpot reach a point where they want more control over their data. They want to run custom reports, track historical trends, or combine HubSpot data with other systems like finance or product usage. HubSpot offers reporting tools, but they are limited in scope and flexibility.
PostgreSQL is a widely adopted open-source database that supports complex queries, large datasets, and data modeling. When HubSpot data is stored in PostgreSQL, it becomes easier to analyze alongside other business data. This setup is often used to support reporting, dashboards, and data science workflows.
The integration process involves extracting data from HubSpot, transforming it to fit a PostgreSQL schema, and then loading it into a warehouse environment. Some teams also implement two way sync to keep CRM and operational data consistent in near real-time.
Two way sync (also called bidirectional synchronization) is a data integration method where changes made in either system are reflected in the other. Unlike one-way sync where data flows only from a source to a destination, two way sync creates a continuous data flow between both systems.
In the context of HubSpot and PostgreSQL integration, two way sync means that:
When a record is updated in HubSpot, the change appears in PostgreSQL
When data is modified in PostgreSQL, the change is pushed back to HubSpot
This bidirectional flow ensures that both systems contain the same up-to-date information, creating a consistent view of customer data across platforms.
![Diagram: Two way sync between HubSpot and PostgreSQL showing bidirectional data flow]
Two way sync is particularly valuable when different teams work with the same data in different systems. For example, marketing teams might update contact information in HubSpot while data analysts make changes based on their findings in PostgreSQL.
Integrating HubSpot with PostgreSQL creates a powerful foundation for data-driven decision making. This combination allows organizations to maintain the user-friendly interface of HubSpot while gaining the analytical capabilities of PostgreSQL.
Expanded analysis capabilities: PostgreSQL supports complex SQL queries that go beyond HubSpot's built-in reports, enabling deeper customer insights and trend analysis.
Historical data retention: While HubSpot may archive older data, PostgreSQL can store complete historical records for long-term analysis and compliance.
Unified data view: By combining HubSpot data with information from other systems in PostgreSQL, teams can create a comprehensive view of customer interactions and business performance.
Breaking down data silos between marketing, sales, and customer service teams helps create a more cohesive customer experience. When everyone works from the same dataset, messaging becomes more consistent and customer interactions more informed.
There are three primary approaches to implementing two way sync between HubSpot and PostgreSQL. Each method offers different levels of complexity, automation, and maintenance requirements.
Method | Complexity | Technical Skills | Best For | Maintenance |
|---|---|---|---|---|
Manual Import/Export | Low | Basic SQL, spreadsheets | Small datasets, infrequent updates | High (manual effort) |
Custom API Scripts | High | Programming, API knowledge | Tailored solutions, specific business logic | Medium (code maintenance) |
Integration Platforms | Medium | Configuration skills | Ongoing syncs, non-technical teams | Low (managed service) |
The simplest approach involves manually exporting data from HubSpot as CSV files and importing them into PostgreSQL using commands like:
COPY contacts FROM '/path/to/file.csv' DELIMITER ',' CSV HEADER;
For the reverse direction, data is exported from PostgreSQL and uploaded to HubSpot through its import tools. This method works for occasional updates but quickly becomes time-consuming for regular synchronization.
Developers can build custom scripts that connect to both HubSpot's API and PostgreSQL to transfer data automatically. This approach typically uses programming languages like Python with libraries for API access and database connections.
Custom solutions offer maximum flexibility but require ongoing maintenance to handle API changes, error conditions, and scaling requirements. They're best suited for organizations with specific requirements that off-the-shelf solutions can't address.
Integration platforms provide pre-built connectors for both HubSpot and PostgreSQL, allowing non-technical users to configure two way sync through visual interfaces. These tools handle authentication, field mapping, and synchronization scheduling without custom code.
While integration platforms simplify setup and maintenance, they may involve subscription costs based on data volume or sync frequency. They're ideal for teams seeking reliable synchronization without dedicated development resources.
Two way sync between HubSpot and PostgreSQL follows a structured process to ensure data consistency while avoiding endless update loops. Understanding this process helps in troubleshooting and optimizing the integration.
The core components of a two way sync include:
Change detection: Identifying which records have been modified in either system
Conflict resolution: Determining which version to keep when both systems have changes
Data transformation: Converting between HubSpot's format and PostgreSQL's schema
Error handling: Managing failed synchronizations and retry logic
Change detection typically uses timestamps or version tracking to identify records that need synchronization. In HubSpot, the updatedAt field indicates when a record was last modified, while PostgreSQL can use update timestamps or change tracking tables.
Conflict resolution policies determine which system "wins" when conflicting changes occur. Common strategies include:
Last-write-wins: The most recent change takes precedence
Source-of-truth: One system is always considered authoritative
Field-level rules: Different fields may have different authoritative sources
For example, a company might decide that contact email changes in HubSpot always override PostgreSQL values, while custom scoring data in PostgreSQL takes precedence over HubSpot.
Detecting changes in HubSpot is essential for efficient two way sync. Rather than processing all records during each synchronization, change detection focuses only on modified data.
HubSpot offers two primary methods for change detection:
Timestamp filtering: Using the updatedAt field to request only records changed after a specific time
Webhooks: Receiving real-time notifications when records are created, updated, or deleted
Timestamp filtering works well for scheduled synchronization jobs. When querying the HubSpot API, the updatedAfter parameter filters results to include only recently changed records:
GET /crm/v3/objects/contacts?updatedAfter=2023-07-15T00:00:00Z
This approach reduces API calls and processing time by focusing only on changed records.
Webhooks provide near real-time synchronization by notifying an endpoint whenever changes occur in HubSpot. After configuring webhooks in the HubSpot developer portal, a receiving system processes these notifications and updates PostgreSQL accordingly.
![Image: HubSpot webhook configuration screen showing event subscription options]
For high-volume HubSpot accounts, combining these approaches often works best: webhooks handle real-time updates while scheduled jobs using timestamp filtering ensure no changes are missed.
When implementing two way sync between HubSpot and PostgreSQL, maintaining data consistency becomes crucial. Without proper conflict resolution, data can become inconsistent or stuck in update loops.
The timestamp-based approach is common for conflict resolution. Each record tracks when it was last updated in each system, and the more recent change takes precedence. This requires storing additional metadata alongside the actual business data.
In PostgreSQL, this might look like:
CREATE TABLE contacts (
id BIGINT PRIMARY KEY,
email TEXT,
first_name TEXT,
last_name TEXT,
hubspot_updated_at TIMESTAMP,
postgres_updated_at TIMESTAMP
);
When syncing from HubSpot to PostgreSQL, the system compares hubspot_updated_at with postgres_updated_at. If the HubSpot timestamp is newer, the record is updated in PostgreSQL. The reverse comparison happens when syncing from PostgreSQL to HubSpot.
To prevent infinite update loops, the synchronization process must be careful to:
Update timestamps only when actual data changes occur
Avoid triggering updates when syncing in from the other system
Implement circuit breakers that detect and stop excessive update cycles
Field-level synchronization offers more granular control by specifying which system is authoritative for each field. For example, contact demographic information might be managed in HubSpot, while lead scoring data comes from PostgreSQL.
As data volumes grow, maintaining efficient two way sync between HubSpot and PostgreSQL requires attention to performance factors. Poorly optimized synchronization can lead to excessive API usage, slow database performance, or outdated information.
Batch processing: Rather than syncing records individually, batch processing groups multiple records into a single operation. This reduces overhead from network connections and database transactions. Optimal batch sizes typically range from 100-1000 records depending on complexity.
Incremental synchronization: Only processing changed records significantly reduces the workload. This requires reliable change tracking in both systems and careful management of synchronization state.
Optimized database operations: In PostgreSQL, using bulk operations and proper indexing improves sync performance:
-- Creating appropriate indexes
CREATE INDEX idx_contacts_email ON contacts(email);
CREATE INDEX idx_contacts_updated_at ON contacts(hubspot_updated_at);
-- Using efficient bulk upsert
INSERT INTO contacts (id, email, first_name, last_name, hubspot_updated_at)
VALUES
(1, 'contact1@example.com', 'Jane', 'Doe', '2023-07-15 10:00:00'),
(2, 'contact2@example.com', 'John', 'Smith', '2023-07-15 11:30:00')
ON CONFLICT (id) DO UPDATE
SET email = EXCLUDED.email,
first_name = EXCLUDED.first_name,
last_name = EXCLUDED.last_name,
hubspot_updated_at = EXCLUDED.hubspot_updated_at;
For very large datasets, partitioning tables by date ranges can improve query performance by limiting the scope of operations to relevant data partitions.
When implementing two way sync between HubSpot and PostgreSQL, data security becomes a critical consideration. The synchronization process involves transferring potentially sensitive customer information between systems.
Data security in two way sync encompasses several key areas:
Encryption: All data transferred between HubSpot and PostgreSQL should use encrypted connections (TLS/SSL) to prevent interception.
Authentication: API keys and database credentials must be securely stored using environment variables or secrets management systems, never in code repositories.
Access control: PostgreSQL roles and permissions should limit data access to authorized users and processes following the principle of least privilege.
Audit logging: Tracking who accessed or modified data helps with compliance and troubleshooting. PostgreSQL's logging capabilities or extensions like pgaudit can record database activity.
Organizations handling personal data must also consider compliance requirements like GDPR or CCPA. This may include implementing data retention policies that automatically archive or delete old records after a specified period.
A well-implemented two way sync between HubSpot and PostgreSQL forms the foundation of a unified data architecture. This approach combines the strengths of specialized systems while maintaining data consistency across the organization.
In this architecture, HubSpot continues to serve as the primary interface for marketing and sales teams, while PostgreSQL provides the analytical capabilities needed for in-depth reporting and data science.
The benefits of this unified approach include:
Marketing and sales teams work with familiar HubSpot interfaces
Data analysts and scientists access the same data through SQL in PostgreSQL
Changes made in either system propagate to the other, maintaining consistency
Historical data is preserved in PostgreSQL even if archived in HubSpot
Customer data can be enriched with information from other systems
For organizations looking to implement this architecture, Stacksync offers data synchronization capabilities that connect HubSpot, PostgreSQL, and over 200 other systems without complex infrastructure or API development.
One way sync transfers data in a single direction from a source to a destination system. Two way sync transfers data bidirectionally, with changes in either system reflected in the other. One way sync is simpler but can lead to data inconsistencies if the destination system is modified.
Synchronization frequency depends on business requirements for data freshness. Real-time or near real-time sync (minutes) works best for operational data needed for immediate decisions. Daily or weekly sync may be sufficient for analytical purposes where slight delays are acceptable.
Common challenges include handling conflict resolution when both systems change the same record, managing API rate limits in HubSpot, preventing infinite update loops, and maintaining performance as data volumes grow. Proper planning and monitoring help address these challenges.
Deletion handling varies by implementation. Options include: soft deletes (flagging records as inactive rather than removing them), hard deletes (removing records from both systems), or archiving (moving deleted records to archive tables). The approach should align with data retention policies.
Yes, two way sync can handle custom fields in HubSpot. The implementation needs to map these custom fields to corresponding columns in PostgreSQL tables. When HubSpot's schema changes with new custom fields, the PostgreSQL schema and synchronization logic need updating to accommodate them.
