Real-Time Synchronisation for Data That Cannot Wait
For records where a delay causes a real problem, such as stock or payment status, we build synchronisation that moves data the moment it changes, while less urgent data can still run on a schedule.
Real Time Is a Choice, Not a Default
Not every piece of data needs to move the instant it changes. Real-time synchronisation costs more to build and run than a scheduled batch, so the decision is made record by record: what actually gets worse if this update takes an hour instead of a second, and what does not change the outcome either way.
Where Real Time Usually Matters
Stock Availability
Selling the last unit twice because stock updated an hour late is the kind of mistake real-time sync exists to prevent.
Payment and Order Status
A customer support call about a payment that already went through, or an order marked pending after it was actually confirmed, both trace back to a delay that mattered.
Access and Permission Changes
Revoking someone's access or updating a credit hold needs to take effect immediately, not at the next scheduled batch.
Where Scheduled Batches Work Fine
Master Data Updates
Item lists, price lists and customer masters usually change in planned batches anyway, so syncing them on a schedule matches how they are actually maintained.
Reporting and Analytics Feeds
Data feeding dashboards or reports is rarely needed to the second, and batching it reduces load on the source systems.
Historical Reconciliation
Comparing yesterday's totals across two systems is naturally an end-of-day task, not a real-time one.
How the Choice Gets Made
- What decision or action depends on this data being current
- How often the underlying value actually changes in practice
- What load real-time updates would place on the source system, and whether it can handle that
- Whether the destination system can even process updates as fast as they would arrive
Frequently asked questions
Is real-time sync always better than scheduled sync?
No. Real time costs more to build, uses more infrastructure, and is harder to troubleshoot when something goes wrong. It earns its cost only for data where a delay causes a genuine problem; for everything else, a scheduled batch is simpler and cheaper to maintain.
How real-time is 'real-time', exactly?
It depends on the method. Event-driven updates through webhooks typically move within seconds. Polling-based approaches check on an interval, which might be every few seconds or every few minutes depending on what the source system's API allows and what the use case actually needs.
Can some data be real-time while other data on the same system is scheduled?
Yes, and that is usually the right approach. Stock and payment status might sync instantly while price lists and reports sync overnight, all within the same overall integration.
What happens if the source system cannot support real-time updates?
Then scheduled polling at the shortest practical interval becomes the fallback, and we say so upfront rather than promising real-time behaviour a system's API cannot actually deliver.
What drives the cost of building real-time synchronisation?
How many record types need it, whether the source system supports event-driven notification or has to be polled, and how much infrastructure, such as queues and workers, is needed to handle the expected volume.
What sets the timeline for real-time sync?
Mostly how well each source system's real-time or webhook capability is documented, since undocumented or inconsistent event data is what usually extends this kind of build.
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