Batch Data Synchronization

Batch data synchronization is used when you want to synchronize database tables, views virtual tables, or saved queries. The selection of the pair of objects meant for synchronization is done by the user.

For example, you are moving your installment loan implementation to FintechOS and want to extract, transform, and load customer billing data into the FintechOS platform from a remote system each week (ongoing).

Batch data synchronization allows you to import and export data into and out of the FintechOS platform. While using this integration, you must take into consideration that these synchronizations can involve large amounts of data and can interfere with the end user operations during business hours.

The below table shows you how to store data, when to refresh it, if the data supports primary business processes, and if there are any reporting requirements that are impacted by the data availability in FintechOS.

Procedure Fit Description
Data Pipes replication Good Data Pipes extract data from external data sources and replicates it in the FintechOS database or other data management systems.

By replicating and synchronizing data from outside sources, you can work with external data sets as if they were native FintechOS database records.

Data Pipes can also be used when updating data in the remote systems. It extracts and transforms the data from the FintechOS database and updates the remote system.
Remote call-in Suboptimal FintechOS enables you to generate clients using WebAPI import capabilities:
  • Generate REST client using OpenAPI specifications of the client’s API.
  • Generate SOAP client by importing the client’s WSDL.
The generated code provides the necessary logic to call the remote service. It can be used in a server automation script
FintechOS Callout Suboptimal It’s possible for FintechOS to call into a remote system and perform updates to data as they occur.
However, this causes considerable on-going traffic between the two systems.

A greater emphasis should be placed on error handling and locking. This pattern has the potential for causing continual updates, which has the potential to impact performance for end users.