Brightics Data Preparation

Data management platform - The evolution of enterprise business transformation

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Gather data from multiple points, and manage it intuitively

Brightics Data Preparation is the platform that collects and pre-processes various corporate data while managing the data lake. By providing various data collection environment, it provides effective data operation management services.

Major Services

Gather scattered data with speed, and process it with ease

  • 16 more templates for field practitioners
  • Process 200k distributed, real-time data per second (at each node)

  • Support various data source & process data real time

    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.

  • Provide various data flow templates

    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.

  • Easily process/convert data and prepare for automated data

    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.

  • Effective work monitoring

    Users can effectively figure out the work monitoring with the visualizing function through dashboard.

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Use Cases

    • Detect malfunctioning equipment
      It is possible to secure the preventive equipment systems depending on the equipment conditions and minimize component costs due to the replacement of the most optimized components through reliability management real time by component, while minimizing the productivity loss thanks to the early detection of equipment malfunctioning.

    • Analyze error cause of conveyance equipment
      Conveyance logistics and equipment operation loss will be minimized by reducing the response time to operation errors through the equipment event and the automatic cause analysis of errors based on log and reducing the reoccurrence rate of errors through the management for frequent errors.
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    Features

    • Support the environment of collecting various data in large volume

      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.

    • Intuitive and automated data preparation

      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.

    • Effective data operation management

      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.

    Specifications

    Recommended specifications
    • Deep learning analytics platform

      x86 server with 3 or more nodes
      -CPU: 16 cores/Node
      -Memory: 64GB/Node
      -Operating system: CentOS, RHEL 7.x

    • PC for platform users

      - Browser : Chrome (Version 50.0 or higher)
      - Screen resolution: 1280 x 900 (recommended)

    • Appropriate cloud environments

      SDS Cloud, Amazon Web Service, MS Azure, Google Cloud, etc.

    Documentations

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