Docking with multiple data sources

Through a star-shaped and pluggable technical architecture, Wasp supports multiple data sources such as Oracle, DB2, MySQL, SQL Server, PostgreSQL, HDFS, Kafka, adapts to different scenarios and user needs, and efficiently integrates and shares massive data.

Continuous Data Synchronization

Based on the continuous synchronization mode of the stream processing framework, Wasp not only supports traditional ETL scenarios, but also effectively meets the continuous data synchronization requirements, keenly captures high-value information, and lays a solid foundation for real-time analysis and dynamic response.

Support Incremental Synchronization

In addition to supporting full data synchronization, Wasp also provides incremental data continuous synchronization services, and supports CDC synchronization of multiple data sources, reducing system load, data redundancy and data synchronization time-consuming.

Integrated Batch Data Processing

Whether it is batch processing or stream processing, Wasp can use a unified template for data processing, avoiding maintaining a set of processing tasks for batch and stream, and improving the efficiency and accuracy of data processing. Users can also drag and drop data processing templates with zero code, lowering the threshold for use.

High Performance Data Synchronization

Wasp supports high-speed batch data synchronization and second-level real-time data synchronization. By expanding concurrency, it can achieve higher data throughput and meet data application scenarios that require low latency and real-time response.

Unattended

Wasp supports unattended tasks such as automatic/scheduled synchronization, restart synchronization, and continuous synchronization. It supports data fault-tolerant processing, automatic fault recovery, and breakpoint resume transmission to ensure that data is not lost or repeated, and data consistency is achieved.