Data Transmission Service (DTS) provides the extract, transform, and load (ETL) feature to help you process streaming data in real time. The ETL feature supports visualized drag-and-drop operations and minimizes hand-coding efforts. The ETL feature is integrated with the data replication capabilities of DTS to implement streaming data extraction, data transformation and processing, and data loading. The ETL feature improves efficiency whereas lowers the development threshold and reduces the impact on business systems. The ETL feature enriches the scenarios of real-time data processing and computing, and empowers digital transformation.


  • Visualized operations: The ETL feature provides three components: Input/Dimension Table, Transform, and Output. You can drag and drop components to build stream processing tasks.
  • Diverse development components:
    • The Input/Dimension Table component supports the following data sources: self-managed MySQL database, ApsaraDB RDS for MySQL instance, and PolarDB for MySQL cluster.
    • In the Transform component, you can join tables, compute functions, and filter fields. More than 90 function compute scenarios are supported.
    • The Output component supports self-managed MySQL databases, ApsaraDB RDS for MySQL instances, PolarDB for MySQL clusters, and AnalyticDB for MySQL V3.0 clusters.
  • Industry-leading computing effectiveness: The ETL feature is integrated with the data collection capabilities of DTS. The ETL feature ensures data accuracy and has industry-leading computing effectiveness.
  • Flexible task monitoring and management: You can monitor and manage ETL tasks in the DTS console. For example, you can start a task, stop a task, and view task details.


  • Centralized management of multi-region or heterogeneous data in real time: To facilitate centralized and efficient management and decision-making, you can store heterogeneous data or data from different regions to the same database in real time.
  • Real-time data integration: The data processing capabilities of ETL greatly improves the efficiency of data integration. The low-code development mode reduces the difficulty and cost of data integration.
  • Real-time data warehousing: The ETL feature provides industry-leading streaming data processing capabilities to help you quickly build real-time data warehouses.
  • Acceleration of offline data warehouses: In streaming data processing, pre-processed data is shipped to data warehouses. Then, in-depth mining of data is performed in the data warehouses. The data warehouses can provide services without affecting your business systems.
  • Real-time reporting: To improve the efficiency of reporting and facilitate digital transformation, you can build a real-time reporting system. The system is suitable for various real-time analysis scenarios.
  • Real-time computing: You can clean the streaming data generated on the business side in real time to extract feature values and tags. Typical scenarios include online business computing models (such as profiling, risk control, and recommendations) or real-time big screens.


The ETL feature is now in public preview. During public preview, each account can create two ETL instances for free.
Notice When the public preview ends, the instances that are running will be charged. The end of the public preview will be notified in advance by announcement or SMS messages.