Product Features
Rich Data Processing Components
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.
Collaborative Development
It supports collaborative development among multiple people within the same project, including code version management and code rollback.
Workflow Scheduling
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.
Intelligent Operations and Maintenance
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.
Data Quality
The platform integrates data quality audit components to monitor the entire process of data ingestion and processing, ensuring data accuracy and consistency.
Data Security
The platform employs a dual control mechanism of functional permissions and data permissions, supports Kerberos authentication integration, and achieves fine-grained permission control.
Features
Visual Task Development
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.
Multi-Scenario Workflow Development
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.
Workflow Operations and Maintenance
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.
Multi-level Linked Task Publishing
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.
Application Scenarios
Multi-Cluster/Engine Data Warehouse Construction
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.
Large-Scale Data Workflow Development
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.
Success Cases
Conch Profiles
Conch Profiles is a key high-tech enterprise in traditional industrial manufacturing. In recent years, with enterprise development and the national "14th Five-Year Plan," there are issues in enterprise digital transformation such as poor horizontal scalability of existing systems, traditional databases unable to support massive data, unclear boundaries between cloud and edge layers, and insufficient edge data storage capacity. Based on these problems and difficulties, there is a need to build an enterprise data middle platform, constructing a large-scale data-driven and data-intelligent production service system.
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Yinhua Fund
With rapid growth in Yinhua Fund Group's business data scale and business development, higher requirements are placed on the data warehouse system, mainly reflected in low development efficiency of the original Oracle data warehouse, high maintenance costs, single service form, relying on increased manpower to meet business needs, unclear data structure in core business marketing, investment research, and anti-money laundering, and huge bottlenecks in big data calculation and storage. There is an urgent need to meet business requirements for high-performance data processing, efficient data services, complex business calculations, massive data queries, metadata and data quality management. Under this business background, the company proposed building a fund data middle platform in 2020.
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AEON
AEON is a leading integrated retail and service group in Asia, with over 500 member companies. In recent years, as AEON's business continues to grow, the importance of data to business development has become increasingly prominent. The group fully recognizes the importance of a data platform for business development and has launched a data middle platform project to establish a unified data platform connecting the group's front-end and back-end core business systems. Through the data middle platform, business data is aggregated, enabling data interconnection and standardization, forming data assets, and providing reusable data services and capabilities for business and data development teams based on technology and big data capabilities, creating a data support platform for AEON's digital transformation.
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A Petrochemical Company
The petroleum industry is a knowledge and technology-intensive industry with multiple disciplines and specialties configured, penetrated, and collaboratively researched. The main business of upstream enterprises involves oil and gas exploration, oil and gas field development, drilling engineering, downhole operations, ground construction, material management, business management, water and electricity, communications, medical and health, and more. Information technology in the petroleum industry has consistently accompanied the development of petroleum enterprises and played a huge role. Petroleum industry data has several significant characteristics: extremely dispersed data distribution, massive data volume, complex data structure, high data utilization value, multiple storage media, complex formats, and complex software usage environments...
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