Financial Lake Warehouse Integrated Solution

Need to support rapidly growing data scale

The increase in data applications, new business expansion, and deeper data mining have all led to the need to store a large amount of data, which is playing a greater role in enterprise operations. With the continuous expansion of data scale, higher requirements have been put forward for data storage.

Big data infrastructure and infrastructure upgrade

HDFS is a native storage system for Hadoop, but the storage layer is gradually becoming hierarchical and specialized. The computing layer no longer needs to rely on open-source HDFS storage and can switch to data lake storage with better reliability and utilization, and richer enterprise level features. In order to achieve the goal of processing massive amounts of data with low latency, data warehouses have also turned to Massive Parallel Processing (MPP) technology. By implementing a strategy of separating computation and storage, the system's concurrent processing capability and scalability can be improved.

Real time enhancement of data processing

Driven by cloud computing, AI, and the Internet of Things, global enterprises are moving towards digitization, and when it comes to backing up data, companies are shifting their focus from simple storage to availability. At the same time, these three technologies also call for real-time or near real-time processing of data.

Massive elasticity

Ecological openness

High cost-effectiveness