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Cloud-native POS platform for seamless omnichannel customer experience.
A single hub for all promotions campaigns.
The most advanced synchronization solution for databases and file systems.
Data configuration and batch automation across different disparate systems and vendors.
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Cloud-native POS platform for seamless omnichannel customer experience.
A single hub for all promotions campaigns.
The most advanced synchronization solution for databases and file systems.
Data configuration and batch automation across different disparate systems and vendors.
The new release of SymmetricDS 2.0 will help you:
SymmetricDS 2.0 includes architectural changes that bring dramatically better replication performance and powerful new routing features, while remaining compatible with the original runtime data model. By separating the Data Routing from the Change Data Capture system, the routing can be moved outside of the database and the two systems can run in parallel. With a streamlined data capture system, the overhead on the database is minimized and contention on the runtime data model is reduced. Instead of using the procedural language of the database, data is routed with dynamic scripting languages like BeanShell and Groovy to provide a richer and more expressive environment.
Change column names, add new columns, and transform data using a simple XML configuration file. The changes can target some nodes or all of them. Simple configuration is all that is needed for many operations, or include an embedded dynamic language script for even more powerful transformations. Filter or encrypt sensitive data while it is being loaded or decrypt data that is replicating to a protected zone. Several filter classes are included for immediate use, or extend the interface and write your own filter that matches your unique requirements.
Finding the resolution to a replication error, especially one in production, can take time to understand and correct. The new SymmetricDS 2.0 release improves diagnostic logging messages with more detail that covers more error scenarios. The log shows the table, column name, and data type producing an error to help find the issue quickly. The runtime data model was also enhanced with statistics along with the status of a batch so that information about a batch is more visible.