/
Many organizations use HubSpot to manage customer relationships and PostgreSQL to store operational data. These systems handle different parts of the business but often hold overlapping or related information.
When systems are not connected, data becomes inconsistent. Teams rely on manual updates or batch uploads, which can introduce delays and errors.
This article outlines how to connect HubSpot and PostgreSQL using a modern integration architecture. It explains how two-way sync works, how to structure data, and what best practices support real-time, secure synchronization.
HubSpot is a customer relationship management (CRM) platform used for marketing, sales, and customer service. PostgreSQL is an open-source relational database used for storing structured data in rows and tables.
These platforms serve different purposes, but both store important business data. HubSpot holds contact records, deal pipelines, and engagement history, while PostgreSQL may store order data, inventory, or account status. Without integration, data between them can drift out of sync.
Connecting HubSpot and PostgreSQL enables a two-way sync between systems. This allows updates in one system to reflect in the other—automatically and in near-real-time. For example, when a sales rep updates a contact's status in HubSpot, that change can sync to PostgreSQL. When an order is fulfilled and logged in PostgreSQL, that information can sync back to HubSpot to update the customer's profile.
Data Approach | Siloed (Not Integrated) | Integrated (Two-Way Sync) |
|---|---|---|
Data consistency | Outdated or mismatched across systems | Consistent and updated in real time |
Operational efficiency | Manual updates and duplicate entry | Automated sync reduces repetitive tasks |
Decision-making | Delayed due to incomplete information | Faster with accurate, centralized data |
Customer experience | Disconnected touchpoints | Unified view across teams |
Two-way sync (also called bidirectional synchronization) is a data integration method where changes made in either system are automatically reflected in the other. Unlike one-way sync where data flows in a single direction, two-way sync creates a continuous data loop between 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 a record is updated in PostgreSQL, the change appears in HubSpot
Both systems remain consistent with the most current information
This bidirectional flow requires careful planning to avoid data conflicts or infinite update loops. For example, if a contact's email is updated in both systems simultaneously, the integration needs rules to determine which version to keep.
Two-way sync typically uses timestamps, version tracking, or designated "source of truth" rules to resolve conflicts when they occur.
![Diagram: Two-way sync between HubSpot and PostgreSQL showing bidirectional data flow]
Extracting data from HubSpot involves using its REST API, which allows external systems to request information from HubSpot accounts in a structured way.
HubSpot's API requires authentication to access account data. There are two primary methods:
OAuth: Used for user-level access and delegated permissions
Private app tokens: Used for server-to-server integrations with fixed scopes
Private app tokens are generally preferred for database integrations because they don't expire as frequently and don't require user interaction.
HubSpot limits how many API requests can be made in a given time period. When these limits are exceeded, the API returns an HTTP 429 response. Effective strategies include:
Exponential backoff: Waiting longer after each failed attempt
Batch processing: Grouping multiple data changes into fewer requests
Request optimization: Using filters to request only updated records
API endpoints are URLs that map to specific data objects in HubSpot. Common endpoints include:
/crm/v3/objects/contacts for contact records
/crm/v3/objects/companies for company records
/crm/v3/objects/deals for deal records
For large datasets, HubSpot uses pagination tokens to break responses into manageable chunks.
PostgreSQL requires a structured schema to store data from HubSpot effectively.
Each HubSpot object can be represented as a table in PostgreSQL. For example:
CREATE TABLE hubspot_contacts (
id BIGINT PRIMARY KEY,
firstname TEXT,
lastname TEXT,
email TEXT,
created_at TIMESTAMP,
updated_at TIMESTAMP
);
Primary keys identify each record uniquely. Foreign keys create links between objects, allowing queries that join related data.
HubSpot allows users to create custom properties. These can be stored in a JSONB column:
ALTER TABLE hubspot_contacts ADD COLUMN custom_properties JSONB;
This approach allows PostgreSQL to store custom fields without changing the schema for every new property.
Data from HubSpot comes in various formats that must be mapped to PostgreSQL types:
HubSpot Type | PostgreSQL Type |
|---|---|
string | TEXT |
number | NUMERIC |
boolean | BOOLEAN |
datetime | TIMESTAMP |
enumeration | TEXT |
object (JSON) | JSONB |
Timestamps from HubSpot are usually in ISO 8601 format with timezone information. Converting them to UTC ensures consistent time calculations across systems.
Real-time synchronization between HubSpot and PostgreSQL involves keeping data consistent across both systems as updates happen.
Incremental updates transfer only new or changed data between systems. This approach:
Reduces data volume: Only modified records are processed
Improves performance: Sync jobs complete faster
Lowers resource usage: Less CPU and network traffic required
One method of detecting changes is using HubSpot's lastmodifieddate property. By storing the most recent timestamp from the last sync, systems can request only records with a later timestamp in the next cycle.
Two-way synchronization moves data in both directions—HubSpot to PostgreSQL and PostgreSQL to HubSpot. A typical architecture includes:
[HubSpot API] ←→ [Sync Engine] ←→ [PostgreSQL Database]
The sync engine compares records from both systems based on unique identifiers. When a difference is found, the engine decides which version to keep using conflict resolution strategies:
Source of truth: One system is always prioritized
Last write wins: The most recent change is kept
Field-level merging: Selected fields are updated based on rules
Monitoring ensures the synchronization process works correctly. Important metrics include:
Number of records synced per cycle
Sync duration and latency
Number of failed API requests
Number of records skipped due to validation errors
Error handling patterns detect and log issues without stopping the entire sync. For example, if a single contact record causes a validation error, the system logs the issue and continues processing other records.
Data quality means the accuracy, completeness, and consistency of data during transfer between systems. Common validation rules include:
Email format validation for contacts
Numeric validation for deal amounts
Required fields like names or stages
Data cleansing includes removing whitespace, normalizing date formats, and converting nulls to defaults. These processes ensure consistency across both systems.
CRM data often contains personal information regulated under laws such as GDPR and CCPA. Key considerations include:
Data minimization: Sync only the fields required for operations
Encryption: Use HTTPS/TLS for data in transit
Access control: Assign least-privilege roles in both systems
Audit logging: Track who accessed or modified data
Performance tuning improves sync speed and system efficiency. Effective strategies include:
Creating indexes on frequently queried fields
Using batch operations for multiple records
Implementing connection pooling for database operations
Tracking changed records rather than performing full table scans
No-code data integration platforms offer an alternative to manual coding by providing visual interfaces and prebuilt connectors.
No-code platforms typically reduce implementation time compared to custom development:
Custom-coded integration: 2-4 weeks
No-code solution: 1-5 days
This efficiency comes from prebuilt connectors, visual field mapping, and automated error handling.
As data volumes grow, integration systems may face performance challenges. Warning signs include:
Sync jobs taking significantly longer to complete
Frequent rate limit errors
Data mismatches due to schema drift
Increased manual intervention to resolve failures
Platforms that support schema evolution, retry logic, and load balancing can better support long-term scalability.
Selecting a no-code platform involves evaluating:
Support for two-way sync between HubSpot and PostgreSQL
Field-level mapping and transformation options
Incremental sync capabilities
Built-in error handling and monitoring
Compliance with security standards
Reverse ETL moves information from PostgreSQL back into HubSpot. This process allows insights and calculated data to become available where sales and marketing teams already work.
Reverse ETL turns database insights into actionable information inside HubSpot. Examples include:
Product usage scores calculated in PostgreSQL displayed on contact records
Churn risk flags based on support ticket trends shown on company pages
Billing status from financial systems reflected in deal stages
These enriched records help teams make informed decisions without switching between systems.
Implementing reverse ETL requires:
Selecting data from PostgreSQL
Transforming it to match HubSpot's expected format
Sending updates through HubSpot's API
Monitoring for successful completion
Each update includes the record's unique ID and the properties to modify. The process respects HubSpot's API rate limits and includes retry logic for failed requests.
An integration between HubSpot and PostgreSQL is one part of a larger data ecosystem. Many organizations connect multiple systems including CRMs, databases, ERPs, and analytics platforms.
A comprehensive data strategy includes:
Identifying core systems and data types
Designing consistent schemas across platforms
Establishing sync logic and frequency
Implementing monitoring and error handling
Reviewing security and compliance requirements
Tools like Stacksync support this architecture by providing managed connectors for two-way sync between systems like HubSpot, PostgreSQL, and other business applications.
Two-way sync updates data in both directions when changes occur in either system, while one-way sync only updates the destination when the source changes. Two-way sync maintains consistency across both systems but requires conflict resolution strategies.
Sync frequency depends on the integration method and business requirements. Options range from near-real-time (every few minutes) to scheduled batch updates (daily or hourly). Real-time sync provides the most current data but uses more API resources.
Conflicts occur when the same record is modified in both systems before synchronization. Resolution methods include designating a system as the "source of truth," using timestamps to determine the most recent change, or applying field-level rules where specific fields take priority from different systems.
