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Syncing HubSpot with PostgreSQL means connecting a cloud-based CRM system to a structured, relational database. In practice, this process allows data such as contacts, deals, companies, and tickets stored in HubSpot to be copied or mirrored into tables inside a PostgreSQL database. This can be done in one direction (from HubSpot to PostgreSQL), or in both directions—known as two-way sync.
Organizations connect these systems to make HubSpot data available for analysis, reporting, and use in internal applications. This integration also allows PostgreSQL-based systems to push updates back into HubSpot, keeping both platforms aligned in real time or on a set schedule.
This guide outlines multiple integration methods, including API-based syncs, CSV imports, and direct data access using JDBC or foreign data wrappers. It also explores how data synchronization platforms provide secure, two-way synchronization between systems like HubSpot and PostgreSQL, without custom infrastructure or manual maintenance.
Two-way sync (also called bidirectional synchronization) creates a connection where data flows in both directions between HubSpot and PostgreSQL. When a record changes in either system, the update is reflected in the other system automatically.
For example, if a sales representative updates a contact's phone number in HubSpot, that change appears in the PostgreSQL database. Similarly, if an internal application updates a deal amount in the PostgreSQL database, that new value appears in HubSpot.
This differs from one-way sync, where data only moves from the source (HubSpot) to the destination (PostgreSQL) without updates flowing back.
Two-way sync helps organizations maintain consistent data across systems, allowing different teams to work with the tools they prefer while keeping information accurate everywhere.
![Diagram showing bidirectional data flow between HubSpot and PostgreSQL]
Connecting HubSpot with PostgreSQL creates several advantages for data-driven organizations:
Unified Data Access: Teams can query customer information using SQL instead of relying on HubSpot's interface or exports.
Enhanced Reporting: Analysts can combine CRM data with other business data in a single database for comprehensive reports.
Custom Applications: Developers can build internal tools that read and write CRM data through familiar database connections.
Historical Data Retention: Organizations can store complete customer history beyond HubSpot's standard retention periods.
This integration bridges the gap between marketing and sales teams using HubSpot and technical teams who work primarily with databases. It allows each group to use their preferred tools while maintaining data consistency.
There are three primary approaches to connecting HubSpot with PostgreSQL, each with different levels of complexity and automation.
Method | Complexity | Automation | Best For |
|---|---|---|---|
CSV Export/Import | Low | Manual | One-time transfers |
Custom API Scripts | High | Scheduled | Tailored solutions |
ETL/iPaaS Platforms | Medium | Automated | Ongoing synchronization |
The simplest method involves exporting data from HubSpot as CSV files and importing them into PostgreSQL. This approach requires no programming but doesn't support automatic updates or two-way sync.
To export from HubSpot:
Navigate to the contacts, companies, or deals section
Select the export option
Choose the fields to include
Download the resulting CSV file
To import into PostgreSQL:
COPY hubspot_contacts FROM '/path/to/contacts.csv' DELIMITER ',' CSV HEADER;
This method works for initial data loading or occasional updates but becomes impractical for regular synchronization.
Developers can create scripts that connect to HubSpot's API to read and write data. This approach offers complete customization but requires significant development and maintenance.
A basic integration uses these components:
HubSpot API client library
Database connector (like psycopg2 for Python)
Scheduling system (cron jobs or similar)
Error handling and logging
Here's a simplified example in Python:
import requests
import psycopg2
# Fetch contacts from HubSpot
response = requests.get(
"https://api.hubapi.com/crm/v3/objects/contacts",
headers={"Authorization": "Bearer your_token"}
)
contacts = response.json()["results"]
# Update PostgreSQL
conn = psycopg2.connect("dbname=mydb user=user")
cur = conn.cursor()
for contact in contacts:
cur.execute(
"INSERT INTO contacts (id, email, name) VALUES (%s, %s, %s) ON CONFLICT (id) DO UPDATE SET email = %s, name = %s",
(contact["id"], contact["properties"]["email"], contact["properties"]["name"],
contact["properties"]["email"], contact["properties"]["name"])
)
conn.commit()
For two-way sync, additional code tracks changes in PostgreSQL and sends them back to HubSpot.
ETL (Extract, Transform, Load) platforms and iPaaS (Integration Platform as a Service) solutions provide pre-built connectors for both HubSpot and PostgreSQL. These tools offer visual interfaces for mapping fields and configuring sync rules without extensive coding.
Popular platforms include:
Skyvia
Hevo Data
Fivetran
Airbyte
These solutions handle complexities like:
Field mapping between systems
Data type conversions
Incremental updates
Error handling and retries
Monitoring and logging
Most platforms support both one-way and two-way synchronization patterns, though two-way sync typically requires additional configuration to prevent update loops.
When implementing bidirectional synchronization between HubSpot and PostgreSQL, several technical factors require attention.
HubSpot and PostgreSQL use different data structures. HubSpot organizes information as objects with properties, while PostgreSQL uses tables with columns. Creating a clear mapping between these structures is essential.
Common mapping challenges include:
Data Types: Converting HubSpot's text, number, and date fields to PostgreSQL's VARCHAR, INTEGER, and TIMESTAMP types
Custom Fields: Handling HubSpot's custom properties that may change over time
Relationships: Representing HubSpot's associations between objects as foreign keys in PostgreSQL
A well-designed schema includes tables for each HubSpot object type (contacts, companies, deals) with columns for standard and custom properties. It also tracks the last update time to support incremental synchronization.
When the same record is updated in both systems before synchronization occurs, a conflict happens. Resolving these conflicts requires a defined strategy:
Last-Write Wins: The most recent update takes precedence
Source Priority: Changes from one system always override the other
Field-Level Rules: Different fields may have different priority rules
Manual Resolution: Some conflicts require human review
Without conflict handling, data may become inconsistent or updates may be lost. Most integration platforms include configurable conflict resolution settings.
Two-way sync can happen in real-time (immediately after changes) or on a schedule (hourly, daily, etc.).
Real-time synchronization offers:
Immediate data consistency across systems
Reduced risk of conflicts
Up-to-date information for all users
Scheduled synchronization provides:
Lower system load
Reduced API usage
Batch processing efficiency
For real-time updates from HubSpot, webhooks can notify the integration when records change. For updates from PostgreSQL to HubSpot, triggers or change data capture mechanisms can identify modified records.
Syncing data between systems introduces security considerations, especially when handling customer information.
Authentication: Access to both HubSpot and PostgreSQL requires secure credentials. HubSpot uses API keys or OAuth tokens, while PostgreSQL uses username/password or certificate authentication.
Data Protection: Information transferred between systems should use encrypted connections (HTTPS for API calls, SSL/TLS for database connections).
Compliance: Organizations subject to regulations like GDPR or CCPA must ensure their data synchronization processes maintain compliance, including:
Data minimization (syncing only necessary fields)
Appropriate retention periods
Secure access controls
Audit logging of data access and changes
Organizations implementing two-way sync between HubSpot and PostgreSQL often encounter these challenges:
HubSpot restricts how many API requests can be made in a given time period. Exceeding these limits causes errors and failed synchronization.
Solutions include:
Batch processing records instead of individual updates
Implementing incremental sync to reduce data volume
Scheduling syncs during off-peak hours
Using exponential backoff when rate limits are reached
As business needs change, both HubSpot and PostgreSQL schemas evolve. New fields appear in HubSpot, or column types change in PostgreSQL.
Effective sync solutions:
Detect schema changes automatically
Add new columns to PostgreSQL when HubSpot properties appear
Log warnings when incompatible changes occur
Provide mechanisms to update mapping configurations
CRM data often contains inconsistencies that can disrupt synchronization:
Missing required fields
Duplicate records
Inconsistent formatting
Invalid values
Integration processes can include validation and transformation steps to clean data before syncing, preventing errors and ensuring consistency.
Two-way sync creates a bidirectional connection where changes in either system update the other. When a record changes in HubSpot, the integration detects this change and applies it to PostgreSQL. Similarly, when data changes in PostgreSQL, the integration sends those updates to HubSpot through its API.
Most common data types synchronize effectively, including text fields, numbers, dates, boolean values, and picklists. Binary data like files and images typically require special handling and are often referenced rather than directly synchronized.
Synchronization frequency depends on business needs and data volume. Organizations with time-sensitive operations may sync every few minutes, while others find hourly or daily updates sufficient. Consider API rate limits, database performance, and how quickly users need updated information.
Yes, most integration methods support filtering by record properties. For example, you might sync only active contacts, deals above a certain value, or companies in specific industries. This reduces data volume and focuses the integration on relevant information.
Custom objects in HubSpot can be synchronized by creating corresponding tables in PostgreSQL. The integration needs access to the custom object API and must map the object's properties to database columns. This typically requires additional configuration compared to standard objects.
Effective two-way sync between HubSpot and PostgreSQL creates a foundation for data-driven operations. Marketing and sales teams can continue using HubSpot's interface while technical teams access the same information through PostgreSQL. Updates flow in both directions, maintaining consistency across systems.
Whether using manual exports, custom code, or integration platforms, the key principles remain the same: clear data mapping, thoughtful conflict resolution, appropriate sync frequency, and ongoing monitoring.
By connecting these systems, organizations break down data silos and enable more comprehensive analysis and automation. Teams gain the flexibility to use the right tool for each task while working from a single, consistent view of customer data.
