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Connecting HubSpot to a database like PostgreSQL is common in data engineering workflows. It allows teams to move data between systems, keep it in sync, and support analytics or application development.
This article outlines a technical framework for syncing HubSpot with PostgreSQL. It focuses on schema design, data extraction, update handling, and secure access.
The goal is to support two-way sync between HubSpot and PostgreSQL in a way that is reliable, scalable, and compliant with platform limitations.
Two-way sync, also called bidirectional synchronization, is a data integration pattern where changes made in either system are reflected in the other. Unlike one-way sync that only copies data from a source to a destination, two-way sync creates a continuous data flow between both systems.
In the context of HubSpot and PostgreSQL, two-way sync means that:
When a contact is updated in HubSpot, the change appears in PostgreSQL
When a record is modified in PostgreSQL, the change is pushed back to HubSpot
This creates a consistent view of data across both platforms, regardless of where changes originate.
Two-way sync requires special handling for conflict resolution. If the same record is changed in both systems before synchronization occurs, the system needs rules to determine which value to keep.
HubSpot excels at marketing, sales, and customer service operations, while PostgreSQL provides powerful data storage and analysis capabilities. Connecting them combines operational CRM data with analytical database functions.
This integration supports several key business needs:
Centralized data analysis: PostgreSQL allows complex SQL queries that can analyze HubSpot data alongside other business information.
Custom application development: Developers can build internal tools using PostgreSQL as the backend while keeping HubSpot data current.
Historical data retention: PostgreSQL can store complete customer history beyond HubSpot's standard retention periods.
The connection also enables real-time sync between systems, ensuring that customer information is consistent across platforms.
When implementing a two-way sync between HubSpot and PostgreSQL, certain objects and fields require special attention. The most commonly synchronized HubSpot objects include:
HubSpot Object | PostgreSQL Table | Key Fields | Considerations |
|---|---|---|---|
Contacts | hubspot_contacts | email, firstname, lastname | Handle multiple email addresses |
Companies | hubspot_companies | name, domain | Parent-child relationships |
Deals | hubspot_deals | amount, stage | Currency conversion |
Each object contains standard fields that store specific information. When mapping these fields to PostgreSQL columns, maintain consistent data types and relationships.
For contacts, important fields include unique identifiers, contact information, and engagement data. Company records contain organizational details and relationship mappings. Deal records include financial information and pipeline stages.
Secure connections between HubSpot and PostgreSQL require proper authentication and encryption. For HubSpot, this means obtaining API credentials through either developer API keys or OAuth 2.0 integration.
OAuth 2.0 is generally preferred for production environments because it supports:
Scoped permissions that limit access to specific data
Token expiration for improved security
Refresh tokens for maintaining access without storing passwords
For PostgreSQL, connection security involves:
User authentication with strong passwords
Role-based access control to limit permissions
Network security through IP restrictions or VPN
Encrypted connections using SSL/TLS
When transferring data between systems, protect information in transit using secure tunneling methods such as SSH tunnels or TLS encryption.
Creating an effective PostgreSQL schema for HubSpot data requires careful planning. Each HubSpot object typically maps to a dedicated table with columns that correspond to HubSpot properties.
A basic contacts table might look like:
CREATE TABLE hubspot_contacts (
id BIGINT PRIMARY KEY,
firstname VARCHAR(255),
lastname VARCHAR(255),
email VARCHAR(255),
company VARCHAR(255),
created_at TIMESTAMP,
updated_at TIMESTAMP,
archived BOOLEAN DEFAULT FALSE
);
For custom properties that change frequently, consider using a JSONB column:
ALTER TABLE hubspot_contacts ADD COLUMN custom_properties JSONB;
This approach provides flexibility while maintaining structure for standard fields.
When designing schemas for two-way sync, include tracking fields that help with synchronization:
Last sync timestamp
Source of last update
Conflict resolution flags
These fields help manage the bidirectional data flow and resolve conflicts when they occur.
Several approaches exist for moving data between HubSpot and PostgreSQL. Each has different advantages depending on data volume, update frequency, and technical resources.
Manual exports involve downloading HubSpot data as CSV files and importing them into PostgreSQL. This approach works well for:
Initial data loads
One-time migrations
Small datasets with infrequent changes
The process involves exporting from HubSpot's interface and then using PostgreSQL's COPY command to load the data:
COPY hubspot_contacts(email, firstname, lastname)
FROM '/path/to/hubspot_contacts.csv'
DELIMITER ','
CSV HEADER;
While simple, this method doesn't support real-time updates or automated synchronization.
Custom scripts provide more control over the extraction process. Using HubSpot's API, scripts can:
Fetch only changed records
Transform data during transfer
Handle error conditions
Run on automated schedules
A basic Python script might look like:
import requests
import psycopg2
API_KEY = 'your-hubspot-api-key'
BASE_URL = 'https://api.hubapi.com/crm/v3/objects/contacts'
HEADERS = {'Authorization': f'Bearer {API_KEY}'}
# Connect to PostgreSQL
conn = psycopg2.connect("dbname=mydb user=myuser password=mypass")
cur = conn.cursor()
# Fetch contacts from HubSpot
response = requests.get(BASE_URL, headers=HEADERS, params={'limit': 100})
data = response.json()
# Process and insert data
for contact in data['results']:
# Insert into PostgreSQL
cur.execute("""
INSERT INTO hubspot_contacts (id, firstname, lastname, email)
VALUES (%s, %s, %s, %s)
ON CONFLICT (id) DO UPDATE SET
firstname = EXCLUDED.firstname,
lastname = EXCLUDED.lastname,
email = EXCLUDED.email
""", (
contact['id'],
contact['properties'].get('firstname', ''),
contact['properties'].get('lastname', ''),
contact['properties'].get('email', '')
))
conn.commit()
cur.close()
conn.close()
This approach requires programming knowledge but provides flexibility for custom requirements.
Integration platforms offer pre-built connectors for HubSpot and PostgreSQL. These tools handle:
Authentication and connection management
Field mapping and transformations
Scheduling and monitoring
Error handling and retries
When evaluating integration tools for two-way sync, consider:
Sync direction: Confirm the tool supports bidirectional synchronization
Conflict resolution: Check how the tool handles conflicting updates
Customization: Evaluate options for field mapping and transformations
Performance: Assess the tool's ability to handle your data volume
For efficient synchronization, focus on transferring only changed data rather than complete datasets. This approach, called incremental updating, significantly reduces processing time and API usage.
HubSpot provides the updatedAt timestamp on most objects. By tracking this value, you can identify records that have changed since the last synchronization.
When extracting data from HubSpot, filter by the updatedAt field:
GET /crm/v3/objects/contacts?updatedAfter=1626345600000
In PostgreSQL, use UPSERT operations to efficiently update existing records or insert new ones:
INSERT INTO hubspot_contacts (id, firstname, lastname, email, updated_at)
VALUES (12345, 'Jamie', 'Lee', 'jamie@example.com', '2025-07-15 12:34:56')
ON CONFLICT (id) DO UPDATE SET
firstname = EXCLUDED.firstname,
lastname = EXCLUDED.lastname,
email = EXCLUDED.email,
updated_at = EXCLUDED.updated_at;
This approach minimizes data transfer while ensuring all changes are captured.
Real-time synchronization keeps HubSpot and PostgreSQL data consistent with minimal delay. This approach uses webhooks and event-driven architecture to trigger updates immediately when changes occur.
When implementing two-way sync, conflicts occur when the same record is modified in both systems before synchronization happens. Common resolution strategies include:
Last-write-wins: The most recent change takes precedence
Source priority: One system is designated as authoritative for specific fields
Merge rules: Combine values based on predefined logic
Manual resolution: Flag conflicts for human review
The chosen strategy depends on business requirements and data importance.
HubSpot webhooks send immediate notifications when data changes. By configuring webhooks to trigger database updates, you can achieve near real-time synchronization.
The process works as follows:
Register webhook endpoints in HubSpot for specific events
When an event occurs, HubSpot sends a notification to your endpoint
Your endpoint processes the notification and updates PostgreSQL
For two-way sync, changes in PostgreSQL trigger updates to HubSpot via the API
This approach enables rapid data propagation between systems without constant polling or scheduled jobs.
Maintaining efficient and secure two-way sync requires attention to several key areas:
Track synchronization performance and reliability through:
Sync logs that record operation details
Status dashboards showing success rates
Alerts for failed synchronizations
Performance metrics for sync duration
These monitoring tools help identify and resolve issues before they impact business operations.
When implementing two-way sync between HubSpot and PostgreSQL:
Store API credentials securely using environment variables or secrets management
Encrypt data in transit using HTTPS and SSL/TLS
Implement proper access controls in PostgreSQL
Follow data privacy regulations like GDPR and CCPA
Regular security audits help ensure ongoing protection of sensitive customer data.
As data volumes grow, optimization becomes increasingly important:
Use batch processing for large data transfers
Create appropriate indexes on frequently queried fields
Implement connection pooling for database efficiency
Consider partitioning large tables by date or category
These techniques help maintain performance even as datasets expand to millions of records.
One-way sync copies data from a source system to a destination without sending changes back. Two-way sync creates a bidirectional flow where changes in either system are reflected in the other, maintaining consistency across both platforms.
Synchronization frequency depends on how quickly data needs to be consistent across systems. For operational data that changes frequently, hourly or real-time sync may be appropriate. For analytical purposes, daily synchronization is often sufficient.
Common challenges include handling conflicting updates, working within API rate limits, maintaining data integrity during failures, and designing effective schema mappings that accommodate differences between systems.
Custom properties can be mapped to dedicated columns in PostgreSQL tables if they are stable and frequently used. For properties that change often or vary widely between records, storing them in a JSONB column provides flexibility while maintaining queryability.
Key security considerations include protecting API credentials, encrypting data in transit, implementing proper access controls, maintaining audit logs of data changes, and ensuring compliance with relevant data protection regulations.
