Manual Sync

1. Manual sync is initiated by a user or administrator from the Appian interface, typically through the Record Type Designer or the Sync page.
2. It performs a full synchronization, pulling all records from the source system into Appian.
3. This approach is best suited for occasional or scheduled data refreshes, such as a nightly or weekly sync.
4. Manual syncs are generally used when a complete data reload is required or after major source changes.

Sync Records Smart Service

1. The Sync Records smart service enables programmatic synchronization of records within a process model.
2. It supports syncing specific record IDs or batches, allowing incremental and selective updates instead of a full sync.
3. This is ideal for on-demand synchronization, such as after an external system update or within a workflow to ensure data freshness.
4. It works with all synced record types and integrates seamlessly into Appian automation processes.

Example : Manual Sync (Full Refresh)

Scenario

A banking application has a Customer Record Type synced from an external CRM system.

What happens

1. The CRM team performs a bulk data cleanup (duplicates removed, old customers archived).
2. Appian data is now out of sync with the source system.

Action

1. An admin goes to Record Type → Sync → Run Manual Sync.
2. Appian performs a full sync, pulling all customer records from the CRM.

Why Manual Sync?

1. A complete data refresh is required.
2. Changes affect most or all records.
3. This is usually done off-hours (e.g., nightly) to avoid performance impact.

Example : Sync Records Smart Service (Selective Sync)

Scenario

A loan application workflow updates a customer’s credit score in an external system.

What happens

1. Only one customer record is updated externally.
2. Appian must reflect this change immediately.

Action

1. After the external API call, a process model triggers the
2. Sync Records Smart Service.
3. The service syncs only the updated Customer Record ID.

Why Sync Records Smart Service?

1. Avoids full data reload.
2. Keeps data real-time and accurate.
3. Improves performance and scalability.