COLUMBUS, Ohio – November 5, 2025 – 54% of North American retailers surveyed say they …
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Cloud-native POS platform for seamless omnichannel customer experience.
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A single hub for all promotions campaigns.
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The most advanced synchronization solution for databases and file systems.
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Data configuration and batch automation across different disparate systems and vendors.
COLUMBUS, Ohio – November 5, 2025 – 54% of North American retailers surveyed say they …
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Overview Organizations today face the challenge of consolidating data from on-premise and cloud-based systems into …
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After nearly a century in business, the legendary Canadian fashion retailer is retooling to streamline …
The Solution Addresses Modern Retail Challenges with Innovative Promotions to Captivate Inflation-Weary Shoppers NRF 2024, …
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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 3.5 will help you:
The Sybase Adaptive Server Enterprise (ASE) database 12.5 and newer, as well as Sybase Adaptive SQL Anywhere (ASA) database 9.0 and newer are fully supported in this release. Capture changes and load data with these databases and synchronize with any of the other databases supported by SymmetricDS. Leverage the rich feature set of SymmetricDS, including guaranteed data delivery, filtered synchronization, data transformation, and offline mode.
Copy files and folders in both directions between nodes using familiar configuration. Use a single solution to replicate both databases and filesystems between multiple nodes. Configure base directories to synchronize and use filtering to match the files and folders needed. Schedule the job to continuously synchronize or run periodically. Automatically recover from network outages and track the progress of batches. Call scripts during file sync events to customize the behavior.
The initial load for a table can now be spanned across multiple batches. The user can specify either a total batch count or a maximum record size per batch. A large table can be split into multiple batches, which can improve reliability of the transfer across slow or intermittent networks. Using multiple batches can also make progress more visible and easy to track. Batches are extracted in the background before a network connection is made, reducing the wait time during synchronization.