In the realm of data synchronization, employing the most effective and efficient methodologies is crucial for maintaining data integrity and performance. Here we outline some best practices that can be adopted for optimal data synchronization.
Initial Data Synchronization
Recommended Approach: For the initial data synchronization, it is advised to use file-based data syncing methods.
Reasoning: The primary synchronization typically involves a substantial amount of historical data, which can be time-consuming to process. File-based synchronization is more suitable in handling large volumes of data in a batch process, ensuring a more stable and efficient initial data transfer.
Incremental Data Updates
Daily Incremental Updates: For daily incremental updates, the use of an API is recommended when dealing with a smaller volume of data.
Handling Large Data Volumes: In scenarios where the daily incremental data is substantial, reverting to file-based methods is advised. This approach is more efficient in managing larger data loads, reducing the risk of performance bottlenecks that might occur with API for high-volume data.
Query Data from CareCloud
Paging Queries: When retrieving incremental data from CareCloud via the API, it is essential to implement a paginated query approach.
Data Volume Per Page: Ensure that each page of data contains no more than 1000 records. This limitation is crucial to prevent overwhelming the system and to ensure smooth data retrieval and synchronization.
Benefits of Pagination: Pagination helps in managing server load, reducing latency, and improving the overall efficiency of data synchronization processes. It also ensures that the API's response times remain consistent, which is particularly important for applications that require real-time or near-real-time data updates.