Technology Author:EqualOcean News , Boying Ji Editor:Tao Ni Mar 19, 2022 12:36 AM (GMT+8)

The new functions can reduce the average length of time for constructing data warehouses from more than a month to less than a week for industries like video games, significantly boosting efficiency

Tencent cloud

A spokesperson of Tencent Cloud, the cloud computing arm of the tech giant, recently said that Tencent had unveiled two new agile data warehouse products called DataModel and DataExplore, Jiemian, a business news website, reported Thursday.

The products are based on the latest agile data warehouse modeling methodology. Through visual, reusable and deposited data modeling, as well as the newly released agile data warehouse, DataModel and DataExplore can cut the time of traditional enterprise data warehouse construction from “months” to “days.”

Using this method increases delivery speed, and businesses can see incremental results, building a more robust data-driven solution over time.

The core idea of Tencent Cloud’s DataModel and DataExplore is to adopt a top-down approach to connect data throughout the whole process, from design and production to analysis, mining and application. 

This is for the purpose of building an enterprise data decision center, according to Jiemian.

The two products released this time, DataModel and DataExplore, are based on Tencent's TBDS (Tencent Big Data Suite), which combine such features as self-service, scenario-based, and intelligence to build a one-stop data warehouse construction and management platform.

At present, the two new products have been applied in industries such as finance, industry, medical care, and games.

In finance, these two new products can make more efficient use of data, while considerably reducing operational and maintenance costs. 

In industry, medical and other industries with a large amount of data, complex data processing, and high data precision requirements, these two new products can also quickly connect the processes of data design, data development, mining analysis, and application, so as to avoid massive data relocation and standardize the tools, said Jiemian.