Transwarp is committed to building enterprise-level big data infrastructure software, providing enterprises with infrastructure software and supporting around the whole data lifecycle to build a data world of the future.
Transwarp Data Transporter synchronizes or integrates data in various formats scattered in various places and on various platforms to the big data platform, and quickly configures the data flow process through a simple and unified visual interface to realize data flow between heterogeneous platforms and data sources.
8 Reasons to Choose Transporter
Rich data sources
The transporter supports almost all common relational databases as well as Transwarp Digital Warehouse, which can be used as input data sources.
It guides users to configure the source side, target side and conversion rules.Thus the correctness of configuration will be verified through the list display, and the usability and readability of the product will beimproved.
It supports the interconnection of data between different clusters and tenants on the Transwarp Big Data platform.
Various data synchronization methods
In addition to the database logs (Canal, OGG), it also provides the data synchronization methodbased on incremental data timestamp query, which is compatible with two common database table structure designs of physical deletion and logical deletion.
It conducts version management of the configuration related to published data synchronization or loading tasks. It supports version viewing and rollback, and it can accurately locate the configuration version used for each execution of the taskfrom the operation and maintenance level , accurately located the configuration issues by restoring.
The loading and synchronization of data supports complete transaction characteristics, and all errors encountered during the import process can be rolled back, ensuring data consistency between the source side and the target side.
Based on the powerful data processing capability provided by Transwarp Inceptor/ArgoDB, compared with the traditional ETL architecture, its ability of complex data conversion is greatly improved. At the same time, a piece of data is reused many times, which improves the utilization efficiency of data and reduces the access pressure to the source end.
Concurrent Policy Control
It provides fine-grained concurrency control for the data loading of large data volume, and supports two concurrent loading strategies for large tables by field and fixed row split, which can achieve load-balanced high-performance concurrent loading in both cases of uniform data distribution and data skewing.