Product Features
Powerful Real-time Processing Capabilities
Tasks support million-level throughput with second-level latency. Automatic fault tolerance ensures high availability, and a single cluster can scale to thousands of nodes.
Data Security
A comprehensive message tracking framework and sophisticated transactional processing ensure that data computations are neither duplicated nor lost.
One-Stop Managed Service Capabilities
One-stop, fully managed streaming data processing capabilities, supporting full-link streaming data computation, as well as monitoring and alerting for task health.
Rich and Flexible
Offers job development through SQL and JAR upload methods; provides custom UDF function capabilities, supporting horizontal and vertical configuration adjustments for cluster task resources.
Rich and Flexible
Provides SQL-based data analysis and processing capabilities, lowering the barrier for streaming data analysis and processing.
Comprehensive Monitoring
Offers comprehensive task operation and maintenance monitoring functions, enabling timely warnings and adjustments to the health status of tasks.
Features
Rich Visual Task Editing Capabilities
It supports automatic recognition of Flink SQL keywords, auxiliary compilation functions such as data preview and one-click data source reference, code version rollback, custom UDF functions, automatic code structure recognition, and provides standalone data debugging features for jobs. You can view debugging results and runtime logs online without affecting online data.
Task Monitoring
It provides comprehensive monitoring of all job runtime information, enabling engineers to promptly assess job health status and perform tuning operations through the analysis of task runtime status and topology diagrams.
Visual Cluster Dashboard Monitoring
The real-time computing platform offers extensive Kafka cluster data monitoring, including cluster monitoring dashboards, topic management, consumer applications, cluster monitoring, and alerting functions. This aids administrators in monitoring and managing Kafka dynamics and promptly alerting on data backlog issues for consumer applications.
Application Scenarios
Real-time Data Warehouse Construction
The real-time computing platform is used to clean, merge, structure, compute, and layer model data generated during business operations. The real-time computation results are output and stored in the analytical database Keen ADB, providing service support for applications and being suitable for the construction of enterprise real-time data warehouses.
Real-time Data Analysis/Real-time Metrics Monitoring
The real-time computing platform continuously computes and writes user data collected from the frontend into a message queue, and continuously outputs the computation results to the enterprise's real-time data dashboard for real-time display and tracking of sales GMV. This is applicable to enterprise real-time data dashboards, real-time risk control systems, vehicle anomaly monitoring, and industrial equipment anomaly detection.
Real-time Recommendations
By computing relevant business metrics in real-time and integrating real-time business data, the platform achieves near-real-time data flow of business data. Combined with actual business production scenarios, it enables real-time business dashboard analysis and monitoring, supporting enterprises in real-time operations, analysis, and decision-making. This is suitable for real-time recommendations, advertising placements, and geographical location analysis.
Success Cases
Conch Profiles
As a key high-tech enterprise in traditional industrial manufacturing, Conch Profiles faces issues 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 in their digital transformation journey. Based on these challenges, they needed to build an enterprise data middle platform to construct a large-scale data-driven and data-intelligent production service system.
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AEON
AEON is a leading comprehensive retail and service enterprise group in Asia, with over 500 member companies. In recent years, as AEON's business continues to grow, the role of data in promoting business has become increasingly prominent. The group fully recognizes the importance of data platforms for business development and has initiated a data middle platform project to establish a unified data platform connecting the group's front and back-end core business systems. Through the data middle platform, business sector data is aggregated, enabling data interconnection, data standardization, and forming data assets, 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, surface 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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