It offers fully managed workflow services and one-stop development management functions, empowering enterprises to build and manage big data capabilities across the entire link, enabling comprehensive data asset management across domains, and establishing private big data centers.
It provides a wealth of data processing components, enabling the entire process of data from ingestion to processing, and meeting the data development needs of complex business scenarios.
It supports collaborative development among multiple people within the same project, including code version management and code rollback.
Offline development is scheduled and executed in the dimension of workflows, generating workflow execution instances, with automatic dependency management for task nodes within the workflow.
The platform provides intelligent dependent task recommendations, intelligent scheduling, intelligent operations and maintenance, and intelligent baseline alerts. It supports batch data replenishment, re-execution of tasks, and more.
The platform integrates data quality audit components to monitor the entire process of data ingestion and processing, ensuring data accuracy and consistency.
The platform employs a dual control mechanism of functional permissions and data permissions, supports Kerberos authentication integration, and achieves fine-grained permission control.
It provides visual components for creating various types of offline development tasks, supports visual task development with WEB SQL, allows online debugging and viewing of runtime logs, and supports the incorporation of custom functions. Task configuration is simple and flexible, reducing learning costs and enhancing product usability.
It supports the creation of development workflows tailored to different business scenarios, enables automatic dependency management for tasks within the same workflow, and supports multiple DAG (Directed Acyclic Graph) configurations for tasks. It facilitates cross-workflow and cross-project task dependencies, reducing the coupling of business logic processing during data development.
It provides workflow execution and debugging capabilities, visually displaying the running status of internal task nodes. It supports periodic execution of workflows, allowing users to view the dependency relationships among internal nodes and the execution status of workflow instances.
The platform offers one-click publishing, enabling the unified deployment of tasks and their dependent objects from the development environment to the target environment. It also supports pre-task validation during the publishing process, mid-process cancellation of publication, and post-publication deletion.
With the rapid growth of enterprise data scale, the demand for data processing and storage has become more diverse. A single type of data warehouse can no longer meet actual business scenarios. The offline development platform integrates multiple computing engines, enabling the integration of various types and sources of data to construct warehouse marts for different data types. It supports the management of different clusters/engines and facilitates collaborative development and unified management by large-scale project teams.
In large-scale data development scenarios, traditional development tools require the creation of one or more complex tasks, with high coupling of code logic for handling business scenarios, leading to difficulties in operations and maintenance. The Keen TECH offline development platform adopts workflow design, allowing the creation of development workflows according to different business scenarios. By forming task flows through automatic dependency management among multiple tasks, it reduces the complexity of data processing and improves development and operations efficiency.
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