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Quick Start

This article introduces the usage process of data development to help you quickly understand and use data development.

  1. Integrate multi-source data: Supports business systems, data warehouses, file data, and APIs and other heterogeneous data sources, enabling flexible synchronization of full and incremental data.
  2. Offline data development:
    1. Low-threshold visual ETL data flow orchestration capabilities, supporting Python scripts, Shell commands and other extended task types to improve development efficiency; at the same time, efficient task orchestration is achieved through loop control, conditional branching, sub-workflows and other methods. For details, see Offline Development Tasks.
    2. Has minute-level near real-time scheduling capabilities to ensure data timeliness, supports event scheduling to avoid empty task runs. For details, see Task Scheduling.
  3. Operations management: Provides visual methods such as instance running Gantt charts and workflow tree diagrams to monitor task status and quickly locate abnormal nodes; then trace and handle problems based on node log information. Supports re-run, recovery from failure and other methods to repair tasks and data, ensuring data accuracy. For details, see Task Monitoring.