Heterogeneous system data sync with a unified view of multi-source data
For enterprises running multiple systems (CRM, ERP, mall, HR, finance, etc.), it provides a data sync and integration solution. Supporting multiple sync strategies and exception retries, it makes data truly "flow".
A sync-task dashboard, with multi-channel goods/stock/orders visualized in one place
Complete capabilities across the entire business flow
Multiple sync channels — API, database, message queue and file.
Data verification before and after sync, with discrepancies reported automatically.
Automatic retry on network exceptions, with resumable transfer losing no data.
Visual configuration of field mapping and data transformation — goodbye hard-coding.
Monitoring of sync-task running status, success rate and performance.
Data-quality analysis, cleansing rules and master-data management.
Flexibly adapted to different business models
Bidirectional sync of multiple business objects — customers, orders and products.
Store POS data is fed back to headquarters on schedule for analysis.
Aggregate orders/stock from multiple e-commerce platforms into one unified middle-platform.
From channel setup to long-term operation, every step is safeguarded
Identify source and target endpoints, and configure authentication, network allowlists and read/write permissions
Select entities, field mapping, filter conditions, trigger frequency and conflict strategy
Based on CDC streaming listening + task scheduling, sensing data changes in seconds
Automatic retry on failure + dead-letter queue fallback + WeCom/SMS alerts, losing not a single record
A distributed data-sync foundation, rock-solid even with massive data
Incremental listening based on Binlog/WAL logs, with millisecond-level latency and zero intrusion on the source database
A watermark + checkpoint mechanism enables resumable transfer with no duplicates or omissions, supporting smooth onboarding of ten-million-row tables
Three conflict-resolution schemes — timestamp + field version number + custom merge strategy — preventing mutual data overwrites
Failed data automatically enters a dead-letter queue, with one-click compensation and replay via a visual interface — goodbye manual reconciliation
Multi-dimensional monitoring of latency/throughput/success rate, with threshold alerts auto-pushed and faults located within 5 minutes
20+ source endpoints work out of the box, including MySQL/Oracle/SQLServer/PostgreSQL/MongoDB/Kafka/Redis
Real, tangible business value
The sync process is traceable, with discrepancies auto-alerted.
Visual configuration + standard templates cut the new-integration cycle by 60%.
Resumable transfer + automatic retry, worry-free for long-term operation.
A sync dashboard + monitoring alerts surface exceptions at the first moment.
Questions you may care about
Three strategies can be flexibly configured: ① timestamp-based "last write wins" (suitable for most business scenarios); ② field-level version-number-based difference merging (suitable for key fields such as order status / stock quantity); ③ custom merge functions (e.g. take the max amount, concatenate remarks). All conflict details are logged, and operators can re-confirm them in the console.
Measured: in scenarios with tens of millions of orders / hundreds of millions of records per day, end-to-end latency is kept within 1 second (P99 ≤2 seconds). The underlying layer is based on Flink CDC + Kafka peak shaving, with linearly scalable throughput, a single-node peak of 50K TPS, and the cluster can scale out horizontally to handle Double 11/618 big promotions.
It provides a full + incremental "two-stage cutover" scheme: first do full initialization with DataX/batched SELECT (rate can be specified to avoid impacting production) while starting CDC listening to record increments; after the full load completes, switch to incremental replay to catch up the time window, and switch traffic once finally consistent. The whole process is zero-downtime for the business, with historical billion-row data migrated within 24 hours.
No. The architecture uses a bypass async design, physically isolating sync tasks from the main business database; failed data enters a dead-letter queue awaiting retry or manual intervention, so the main business flow is unaffected; monitoring alerts sync to the ops group within 30 seconds, with an average fix within 15 minutes.
Relational databases: MySQL/Oracle/SQLServer/PostgreSQL/DM/KingbaseES; NoSQL: MongoDB/Redis/Elasticsearch; messaging: Kafka/RocketMQ/RabbitMQ; files: CSV/Excel/JSON/Parquet; SaaS: DingTalk/WeCom/Youzan/Taobao Open Platform; self-built interfaces: REST/GraphQL/WebHook all work.
Yes. It supports both X86 and ARM64 architectures, with full-stack adaptation completed for Kunpeng/Phytium CPUs + Kylin/UOS OS + DM/KingbaseES databases, passing the Xinchuang Alliance product-compatibility test, so finance, government-enterprise and military-industry customers can deploy with confidence.
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