中文Real-Time Lakehouse
Data is written in real time, updated in real time, and can be seen when written. Integrated with native Flink, it supports real-time warehouse development with high throughput, low latency, and models to meet the real-time needs of business insights.
It supports the interaction of massive data at the second level, without the need for precomputation. Support multi-dimensional analysis, impromptu analysis, and exploratory analysis to meet the WYSIWYG analysis experience.
The platform integrates Flume, Kafka, Presto, Doris, StarRocks and other components, covering real-time acquisition, real-time development, real-time analysis and other real-time data warehouse full-link components, one-stop to meet real-time analysis needs.
Real-time data warehouse integrated with flow batch is built based on native components, which realizes the unification of the computing engine. It solves the problem that SQL logic cannot be reused between stream processing and batch processing in the traditional real-time data warehouse, improves data consistency, code consistency, and greatly reduces code maintenance costs.
Through the integration of Flink Session and Kubernetes, the Flink Kubernetes Session mode is built to provide advantages such as resource sharing, fast job submission, and long-term operation, so as to optimize resource utilization, improve job execution efficiency, and strengthen the flexibility and overall performance of real-time data stream processing.
Provide full-link job operation information monitoring to help developers quickly determine the health status of the job and adjust the operation by analyzing the task operation status and topology diagram of the task.
Real-time data warehouse integrated with flow batch is built based on native components, which realizes the unification of the computing engine. It solves the problem that SQL logic cannot be reused between stream processing and batch processing in the traditional real-time data warehouse, improves data consistency, code consistency, and greatly reduces code maintenance costs.
Business Pain Points
In traditional offline data warehouse environments, enterprises often need to import and process batches of data on a daily basis, which results in the inability to keep up to date with the state of the data in real time. By establishing real-time data warehouses, enterprises can capture and analyze data in real time. By adopting the stream batch integrated real-time data warehouse architecture, enterprises can achieve a set of code to complete stream batch processing, so as to efficiently develop data, help real-time insight into data and fully tap its potential value.
Business Value
Real-time data analysis: Real-time data warehouse can collect and analyze business data in real time, so that enterprises can obtain the latest data information in time and make more accurate decisions.
Real-time business response: Real-time data warehouse can monitor business data in real time, and key nodes can respond in real time to improve service quality and process efficiency.
Real-time monitoring and warning: Through real-time monitoring of business operations, real-time data warehouses can help enterprises quickly find abnormalities and trigger real-time alarms, helping to promptly troubleshoot problems and improve business stability.
Business Pain Points
In traditional offline data warehouse environments, enterprises often need to import and process batches of data on a daily basis, which results in the inability to keep up to date with the state of the data in real time. By establishing real-time data warehouses, enterprises can capture and analyze data in real time. By adopting the stream batch integrated real-time data warehouse architecture, enterprises can achieve a set of code to complete stream batch processing, so as to efficiently develop data, help real-time insight into data and fully tap its potential value.
Business Value
Real-time data analysis: Real-time data warehouse can collect and analyze business data in real time, so that enterprises can obtain the latest data information in time and make more accurate decisions.
Real-time business response: Real-time data warehouse can monitor business data in real time, and key nodes can respond in real time to improve service quality and process efficiency.
Real-time monitoring and warning: Through real-time monitoring of business operations, real-time data warehouses can help enterprises quickly find abnormalities and trigger real-time alarms, helping to promptly troubleshoot problems and improve business stability.
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