Best Practices
📄️ Data Sync Best Practices
This guide aims to provide best practices for data synchronization using TapData Cloud. We will discuss in detail aspects like data source analysis, task configuration, and monitoring, to help you build efficient and reliable data synchronization tasks.
📄️ Handle DDL Changes During Data Sync
During data migration and synchronization with TapData Cloud, recognizing the impact of table structure modifications, such as DDL (Data Definition Language) operations, is crucial for continuous business operations. The platform seamlessly manages most DDL changes, ensuring a smooth synchronization process.
📄️ Monitor Data Synchronization with Heartbeat Tables
TapData uses heartbeat tables to write timestamp information to the source database every 10 seconds. By checking the timestamp information in the heartbeat tables, we can quickly determine the activity and health of the data source, thereby better monitoring the data synchronization path and ensuring the stability and reliability of the data synchronization path.
📄️ Sending Alert Emails via QQ Mail
TapData supports sending alert emails through SMTP protocol, enabling users to receive timely notifications in their commonly used email accounts, thus helping you promptly perceive operational anomalies and ensure the stability and reliability of task operations.
📄️ Ensure Data Migration with Breakpoint Continuation
In scenarios involving massive data migration, you can utilize TapData's full resumption from breakpoint feature to segment and migrate data, enhancing the reliability of data migration and ensuring successful execution of migration tasks.
📄️ Deploy Oracle Raw Log Parsing Service
To enhance the efficiency of capturing data changes, TapData supports not only using the native log parsing tools of databases (LogMiner) but also has developed the capability to directly parse the incremental log files of the database. This allows for more efficient event capture, achieving higher data collection performance (RPS over 20,000), reducing the impact on the source database during incremental data collection, but it requires the deployment of an additional component, which increases operational costs, making it suitable for scenarios with frequent data changes.