Big data project specialist Adata (CEO Lee Dong-hyuk) is introducing a solution that, for the first time in Korea, allows non-developers to directly carry out data collection and integration processes.

On the 27th, Adata announced the launch of its no-code-based intelligent big data collection and integration solution, "SCV" (Smart Collection Vehicle).

SCV enables users to simultaneously collect required data through various OpenAPIs. Users simply input necessary data collection information via an intuitive GUI, and pre-configured task instructions (STML, SCV Task Markup Language) execute multiple OpenAPIs concurrently to batch collect data.

With SCV, users can freely specify input/output conditions to collect desired data more conveniently and quickly, eliminating the need for developers to repeatedly build OpenAPI collection/integration software.

In addition, SCV significantly enhances data collection performance through parallel processing and employs AI technology for automatic data classification and profiling, allowing data to be automatically stored at designated locations.

Not only does SCV support data collection from APIs, but it is also designed to collect data from databases, files, and the web. It offers a dashboard feature to easily monitor collection targets and statuses, improving management efficiency.

Experts have long noted that data collection and integration can consume nearly 70% of the time and cost in big data projects. Based on extensive experience with numerous big data projects, Adata has developed specialized technology to address these issues.

Adata explained that the launch of SCV is expected to resolve the long-standing time and cost challenges associated with data collection and integration in big data projects.

Kang Sung-hoon, head of Adata’s affiliated research institute, said, "The lack of flexibility in changing data collection/integration systems due to a developer shortage has been a chronic problem in big data projects. With the launch of SCV, we will support both domestic and international big data projects and customers in building their big data infrastructures more efficiently."

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