It offers a fully visual, user-friendly, high-throughput, highly fault-tolerant, one-stop streaming data computation and processing platform. It supports SQL for real-time data cleaning, data analysis, and data synchronization, along with comprehensive monitoring mechanisms to ensure the accuracy of stream processing. It can support various enterprise real-time data processing scenarios such as the construction of real-time enterprise data warehouses, real-time data dashboards, real-time reporting, and more.

Product Features

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  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.

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  >Data Security

  A comprehensive message tracking framework and sophisticated transactional processing ensure that data computations are neither duplicated nor lost.

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  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.

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  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.

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  Rich and Flexible

  Provides SQL-based data analysis and processing capabilities, lowering the barrier for streaming data analysis and processing.

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  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

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 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, 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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