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Two-way sync between business platforms is becoming more common as companies manage growing volumes of data across multiple systems. In 2025, organizations that rely on both customer relationship management (CRM) tools and relational databases often look for ways to keep data consistent in both places, automatically and in real-time.
This article explores the process of synchronizing data between HubSpot and PostgreSQL. It covers integration methods used by technical teams, strategies for real-time synchronization, and how to handle updates, deletions, and schema changes.
The focus is on clarity. Whether working with one-way data flows or full two-way sync, this guide outlines how data moves between systems and what technical approaches are most reliable today.
Two-way sync, also called bidirectional synchronization, is a process where changes made in one system are automatically reflected in another system, and vice versa. When a record is updated in either system, the change appears in both places.
In the context of HubSpot and PostgreSQL, two-way sync means:
When contact information changes in HubSpot, it updates in PostgreSQL
When data is modified in PostgreSQL, it updates in HubSpot
This differs from one-way sync, where data flows in only one direction (for example, from HubSpot to PostgreSQL, but not back).
Two-way sync helps teams maintain consistent data across platforms without manual updates or exports. Marketing teams can see database updates in their CRM, while engineers can access CRM data in their database environment.
Organizations use both HubSpot and PostgreSQL for different purposes. HubSpot manages customer relationships, marketing campaigns, and sales pipelines. PostgreSQL stores application data, transaction records, and supports analytics.
Without connecting these systems, teams face several challenges:
Data silos: Marketing data stays separate from operational data
Manual exports: Teams spend time downloading and uploading CSV files
Inconsistent information: Customer details may be different in each system
Delayed insights: Reports require manual data combination
When these systems sync, organizations gain several benefits:
Marketing teams can personalize campaigns using operational data
Sales teams see product usage data alongside customer records
Engineers build applications with access to current customer information
Analysts create reports combining data from both systems
This connection supports better decision-making and more efficient operations across departments.
Three common approaches exist for connecting HubSpot with PostgreSQL. Each has different requirements and benefits.
The simplest method involves exporting data from HubSpot as a CSV file and importing it into PostgreSQL manually.
To export from HubSpot:
Log in to HubSpot
Navigate to Contacts, Companies, or another section
Click Export and select CSV format
Download the file to your computer
To import into PostgreSQL:
Open your PostgreSQL client (like pgAdmin)
Select the target database and table
Use the Import tool to upload the CSV file
Map the columns and complete the import
This approach works for occasional updates or small datasets. It doesn't require programming knowledge but takes manual effort each time.
ETL (Extract, Transform, Load) scripts automate the process of moving data between systems. This approach uses code to pull data from HubSpot, format it correctly, and insert it into PostgreSQL.
A simple Python example:
import requests
import psycopg2
# Extract from HubSpot API
response = requests.get('https://api.hubapi.com/contacts/v1/lists/all/contacts/all',
headers={'Authorization': 'Bearer YOUR_TOKEN'})
data = response.json()
# Connect to PostgreSQL
conn = psycopg2.connect("dbname=yourdb user=youruser password=yourpass")
cur = conn.cursor()
# Transform and load data
for contact in data['contacts']:
cur.execute("INSERT INTO contacts (id, email) VALUES (%s, %s)",
(contact['vid'], contact['properties']['email']['value']))
conn.commit()
This method requires programming skills but provides more control over the synchronization process. It can be scheduled to run automatically and customized for specific business needs.
Several platforms offer pre-built connectors between HubSpot and PostgreSQL. These tools handle the technical details of synchronization without requiring custom code.
Popular options include:
Platform | Setup Difficulty | Real-time Capability | Best For |
|---|---|---|---|
Stacksync | Low | Yes | Bidirectional data sync |
Fivetran | Medium | No (Batch only) | Data warehousing |
Stitch | Low | No (Batch only) | Analytics pipelines |
These platforms typically offer:
Field mapping between HubSpot and PostgreSQL
Scheduling options for regular updates
Error handling and monitoring
Support for schema changes
For organizations without technical resources to build custom solutions, these platforms provide a reliable way to connect HubSpot to database systems.
Real-time synchronization keeps data current across systems with minimal delay. Several approaches can achieve this between HubSpot and PostgreSQL.
Webhooks are automated messages sent when specific events occur. HubSpot can send webhooks when records change, triggering immediate updates in PostgreSQL.
Setting up webhooks involves:
Registering webhook endpoints in HubSpot's developer settings
Creating a server to receive webhook data
Processing incoming data and updating PostgreSQL
For example, when a contact is updated in HubSpot, a webhook sends the new information to your server, which then updates the corresponding record in PostgreSQL.
Webhooks support real-time data integration by responding to events as they happen rather than checking for changes on a schedule.
When both systems can modify the same data, conflicts may occur. For example, a contact's phone number might be updated in both HubSpot and PostgreSQL at nearly the same time.
Common conflict resolution strategies include:
Last-write-wins: The most recent change takes precedence
Source priority: One system is considered the "master" for certain fields
Manual review: Conflicts are flagged for human decision
The right approach depends on your specific business processes and which system is considered authoritative for different types of data.
As data volume grows, synchronization requires more efficient approaches. These techniques help maintain performance with large datasets.
Instead of synchronizing all data every time, incremental loading focuses on records that have changed since the last sync. This reduces processing time and API usage.
HubSpot's API supports this through the modified_since parameter, which filters results to only include recently updated records.
A typical incremental sync process:
Store the timestamp of the last successful sync
Request only records modified after that timestamp
Process the changes and update the timestamp
This approach is particularly valuable for PostgreSQL database integration with large HubSpot accounts containing millions of records.
When synchronizing large volumes of data, several factors affect performance:
Batch processing: Group records into batches rather than processing one at a time
Connection pooling: Reuse database connections instead of creating new ones
Indexing: Create appropriate indexes in PostgreSQL for faster lookups and updates
Resource allocation: Ensure sufficient memory and processing power for sync jobs
These optimizations help maintain reasonable sync times even as data volumes grow.
Over time, both HubSpot and PostgreSQL schemas may change. New fields might be added in HubSpot, or table structures might be modified in PostgreSQL.
Automated schema evolution helps systems adapt to these changes without breaking synchronization. This involves:
Detecting new fields in HubSpot
Adding corresponding columns in PostgreSQL
Handling type conversions when field types change
Managing deprecated fields
For example, if a new custom property is added in HubSpot, the sync process can automatically add a matching column in PostgreSQL during the next synchronization.
HubSpot and PostgreSQL handle deletions differently. HubSpot "archives" records (soft delete), while PostgreSQL typically removes them completely (hard delete).
To keep systems consistent, consider these approaches:
Add a "deleted" flag in PostgreSQL to match HubSpot's archived status
Move deleted records to an archive table in PostgreSQL
Include archived records in HubSpot API requests with the archived=true parameter
The right approach depends on your data retention policies and reporting needs.
Data integration continues to evolve with new technologies and approaches. Several trends are shaping the future of HubSpot to PostgreSQL synchronization:
AI-assisted mapping and transformation
Event-driven architectures replacing scheduled batch jobs
Enhanced data governance and compliance features
Increased focus on data quality validation
These trends point toward more automated, real-time, and reliable integration between systems like HubSpot and PostgreSQL.
For organizations using both platforms, staying current with integration methods ensures data remains consistent, accurate, and available where it's needed.
Real-time synchronization works best with webhooks or API-based triggers that respond immediately when data changes in either system. When a record updates in HubSpot, the webhook sends that information to update PostgreSQL without delay.
Data transfers use encrypted connections (HTTPS), and both systems require authentication. HubSpot uses API keys or OAuth tokens, while PostgreSQL uses username/password or certificate authentication. Additional measures like IP restrictions and audit logging track who accesses the data.
Standard fields (like contact name, email, phone) and custom properties created in HubSpot can be synchronized. Some calculated fields or complex objects may require special handling depending on the integration method used.
Custom objects in HubSpot can be mapped to separate tables in PostgreSQL. The fields within each custom object become columns in the corresponding table, and relationships between objects can be maintained using foreign keys in the database.
For small datasets with infrequent updates, manual CSV export/import is most cost-effective. For ongoing synchronization, custom ETL scripts offer good value if you have programming resources. Integration platforms provide the best balance of cost and convenience for most organizations.
