How Qomon Syncs With Your Systems
When connecting Qomon to your internal databases or CRMs, the goal is simple: ensure your data remains accurate, up-to-date, and consistently synchronized across systems.
There are several ways systems can exchange data, including Webhooks and API Polling. While webhooks are often used for real-time notifications in some architectures, Qomon integrations are designed around API polling.
Polling provides a more controlled, reliable, and resilient approach to database synchronization, allowing your systems to regularly retrieve the latest data while maintaining stability and consistency across your infrastructure.
📬 The difference: Push vs. Pull
The easiest way to understand the difference is to think about how you receive your mail.
Webhooks (Push)
Imagine the mail carrier ringing your doorbell every time a single piece of mail arrives, 24/7. Each time an event happens in Qomon (for example, a contact being updated), a webhook immediately pushes that individual update to your system.
API Polling (Pull)
Now imagine checking your mailbox on a set schedule (for example, every hour or once per day) and collecting all of your mail at once. With API polling, your system pulls updates from Qomon by asking:
"Give me all contacts that have been updated since the last time I checked."
This approach allows systems to retrieve updates in organized batches, making synchronization easier to monitor, retry, and maintain over time.
🛡️ Why API Polling is better for database synchronization
While real-time updates can be useful in some scenarios, synchronizing entire databases requires stability, consistency, and fault tolerance. For this reason, many data integrations rely on API polling as a reliable synchronization method.
Here are a few key advantages of using polling for database synchronization.
Handles Bulk Updates Safely
Large data operations (such as importing or updating thousands of contacts) can generate a very high number of events in a short period of time.
With event-driven systems, this can result in a sudden surge of incoming requests that your infrastructure must process immediately.
With API polling:
Your system retrieves updates at its own pace, typically in paginated batches (for example, 50 or 100 records at a time). This makes it easier to process large volumes of updates without overwhelming your servers.
Resilient to Downtime
Systems occasionally experience downtime due to maintenance, deployments, or temporary outages.
With API polling:
Your system can simply request all updates since the last successful synchronization. This makes it easy to recover from interruptions and ensures that your databases remain consistent over time.
More Controlled Data Processing
When synchronizing data across multiple systems (such as a CRM, marketing tools, or data warehouses) processing order and consistency are important.
With API polling:
Your system retrieves a structured list of updates, allowing you to process them sequentially and apply changes to your databases in a controlled and predictable way.
✅ The bottom line
Webhooks are great for sending a single welcome email the second a user signs up. But for heavy-duty data synchronization, API Polling provides the stability, error-handling, and scalability your databases need.
By setting up a scheduled pull using our Contact Search API, you guarantee that your internal systems are always perfectly matched with your Qomon data - without the stress of dropped data or server overloads.
⚙️ Ready to build? Check the developer documentation
To implement this polling mechanism, your engineering team will need to use our REST API. We provide comprehensive documentation to help your developers get authenticated and start querying data in minutes.
You can find everything you need here: 👉 Developer Hub
What your developers will find there:
Authentication: How to generate and pass your API keys securely.
The Contacts Endpoint: Full details on the
POST /contacts/searchendpoint used for this polling setup.Advanced Search Parameters: A complete breakdown of how to format the date range queries and filter by other attributes.
Pagination Rules: Best practices for looping through pages of data to ensure no updated contacts are missed.