Data management platform - The evolution of enterprise business transformation
It is possible to support various sources other than S3/Azure, FTP, Kafka and Oracle, and easily develop user-defined connectors. It is also possible to process data real time by providing functions to process data based on Spark Streaming and analyze data.
It is possible to not only collect and convert structured/unstructured data but easily collect batch/real-time data by providing templates to collect and process data depending on users' situation.
It is possible to merge, process and convert data and efficiently search for big data with the tagging function that automatically extracts the meta-information of the dataset.
Users can effectively figure out the work monitoring with the visualizing function through dashboard.
It is possible to collect/implement/manage data in fast and convenient manner on various source systems and provide functions to automatically convert semi-structured log data as structured ones through the cognitive technology for data area and pattern recommendation technology.
To efficiently search for big data, the meta-information of the dataset can be automatically extracted and tagged. Through Filtered Histogram, it is possible to easily and quickly implement data merge, processing and conversion.
It is possible to effectively operate and manage data by providing the data access control schemes by data worker and department that utilize the data and visualizing data in a way to figure out the work monitoring through dashboard.
x86 server with 3 or more nodes
-CPU: 16 cores/Node
-Operating system: CentOS, RHEL 7.x
- Browser : Chrome (Version 50.0 or higher)
- Screen resolution: 1280 x 900 (recommended)
SDS Cloud, Amazon Web Service, MS Azure, Google Cloud, etc.
Brightics DL is a platform for unstructured data analysis, which provides technologies for accelerating AI developments. Through Brightics DL, enterprises can quickly and easily apply deep learning analytics services to their businesses through automatic labeling and distributed machine learning (DML).