Provides solutions for large scale database archiving

Data Cube Cloud Computing All-in-One Machine is a big data processing platform, using Intel E5 family series CPUs and Intel server components, with ultra-high cost performance, ultra-high processing performance, ultra-high reliability and other characteristics, providing hundreds of billions of record-level massive data real-time storage, real-time retrieval and other functions.

Data Cube cloud computing all-in-one machine is a cloud processing platform that processes massive amounts of data with efficient distributed software and hardware integration, which can mine useful information from terabytes and even petabytes of data, and process these massive information quickly and efficiently. The platform supports real-time indexing of data streams of more than 100Gbps, responds to customer requests in seconds, and completes data processing, query, and analysis in seconds. The platform can index the ingress data in real time, analyze, clean, and segment the data, and store it on the cloud storage system, which not only has very high performance advantages in storage and retrieval, but also supports services such as data deep mining and business intelligence analysis.

Since the first use of computers in the late fifties of the twentieth century, the world of data and information has been constantly evolving. Over time, it became clear that data, like many entities, has a lifecycle, and is specific to each point in the lifecycle, a different set of characteristics, storage and access needs. The concept of a data warehouse has evolved from the need for reliable, unified, and integrated data reporting and analysis between different data sources based on different points in their lifecycle.

In general, a data warehouse is simply a repository for an organization to store data electronically; It's important to realize that any repository will be limited by the process of finding and accessing items in the repository, as well as moving items in and out of the repository. For data, the main components of a data warehouse system include the ability to selectively store data, the ability to retrieve and analyze data regardless of where it is, and the ability to manage data dictionaries. In order to operate a data warehouse effectively, organizations need to understand the inherent differences in the information stored in the data warehouse at different points in the data lifecycle.

As the data gets old:

1.The likelihood of accessing the data is reduced. Simply put, the older the data becomes, the less often it is used.

2.The data structure changes. As software evolves to become more complex and more efficient to process more data, database architectures must change. This can be seen through the steady release of a series of software releases that take advantage of increasingly powerful hardware and software technologies.

3.The amount of data stored is growing exponentially. Due to industry and regulations, data must be stored and kept accessible for years.

Then only the value of the first year's data is effectively utilized, and maintaining historical data can easily explode data storage to 20 times more than the current production database.

Implementing robust archiving technology will provide the best and cost-effective archiving infrastructure for data warehouses:

1.Maintain data integrity across multiple formats.

2.Enable easy on-demand access to archived data.

3.Provides universal connectivity and integrates with multiple archiving platforms to ensure superior and cost-effective scalability and performance.

4.Efficiently store archived data to conserve storage capacity while facilitating rapid data retrieval.