The Optimal Solution for Building and
Operating Your Big Data Systems

When it comes to Big Data Systems, EBIGs allows you to quickly resolve issues related to configuration, security, and operation.
Moreover, it minimizes the burden of system management by providing a consistent and secure platform for monitoring and
operating the Apache Hadoop Ecosystem.

  • System Configuration through Compatibility Verification

  • Operation Management Solution using Domestically Developed Technology

  • Effective Cost Savings

  • Provide Reliable Technical Support


EBIGs consist of an open-source Apache Hadoop ecosystem that has been built through self-compatibility verification, and a big data management platform that enables monitoring and operation.

  • Big Data System
    Configuration Management
    Compatibility maintenance and
    integrated management
  • Big Data System
    Performance Management
    Tuning and history management
    for each service
  • Data Collection and Error Detection
    Provide status information for each node
  • Hadoop Cluster Security Settings
    Consistent security policies definition / operation / management
  • Real-time Monitoring
    Provide monitoring patterns optimized for Hadoop performance management
  • Bulk File / Directory Browsing
    Manage numerous directors and files simultaneously
  • Hive Workspace
    Manage and visualize Hive database,
    table, query by step
  • Service Operation
    Management / Alarm
    Service stop / operation management, threshold setting, service status notification

Monitoring Views



This is a dedicated screen for the integrated monitoring of the Hadoop Cluster. EBIGs offers visualization for the Apache Hadoop EcoSystem, allowing users to monitor server, service, and node statuses while intuitively grasping key indicators of the Hadoop Cluster. Additionally, it provides a 3D dashboard for Hadoop Cluster Nodes.

· Resource/Node Manager, NameNode, HDFS, Hive, YARN, etc.

HDFS Monitoring

HDFS Monitoring

HDFS monitoring is dedicated to monitoring HDFS key indicators. EBIGs provides visualization for the performance and status of Hadoop Cluster, making it easy to understand the most important performance metrics of Namenode that manages the distributed file system: JVM Heap, GC, Active Namenode, HDFS Capacity, and Block Status. Also, through HDFS Browser Audit, users can trace and download work history.

Yarn Monitoring

Yarn Monitoring

Yarn Monitoring is useful when you want to understand key information such as summary and log about Yarn. EBIGs provides visualization for the performance and status of Hadoop Cluster, checks usage of Yarn Cluster, and displays execution history, making it easy to monitor applications.


Resource Monitoring

EBIGs maximizes resource availability by monitoring the real-time usage of various resources across the nodes that make up the Hadoop Cluster. All indicators of the Hadoop Cluster are collected in real-time, allowing you to easily check the current resource status.


Service List

EBIGs offers management capabilities for the Apache Hadoop ecosystem, allowing users to configure the system optimally by modifying and distributing the settings of each service. The web UI also provides access to execution history, making it easy to operate the ecosystem with its numerous installed services.

Analysis Views

Hive Editor

Hive Editor

EBIGs provides Hive Metastore for data management. Using Zeppelin, users can easily utilize Hadoop data like RDBMS. You can also manage and analyze tables and databases, while conveniently analyzing through visualization of inquiry data.

In-memory Analysis

In-memory Analysis

Spark-notebook is provided to speed up analysis work. Using Spark-notebook, data can be input through various routes, faster and more accurate analysis is possible by distributed processing in a cluster environment. In addition, EBIGs provides machine learning and graph algorithms in conjunction with Python.


Customer Cases

  • Public

    Big Data Integration Platform

    Adoption Background

    There is a need for building a big data integration platform for economic revitalization and efficient data management and utilization


    • Building big data platform infrastructure based on Apache Hadoop

    • Success in building a data hub that provides integrated data on the region

    • Provide services such as web and visualization by utilizing big data stored through EBIGs

  • Electricity

    Power Disaster
    Recovery System

    Adoption Background

    Establishment of a future-oriented Disaster Recovery System (DRS) to secure business continuity for core business systems and configure a mutual backup system


    • Building a disaster recovery system using EBIGs

    • Configuration of real-time data replication environment that guarantees data integrity and consistency

  • ICT

    Building a
    Master Platform

    Adoption Background

    Establishment of a system for efficient data management and analysis of each affiliate


    • Building big data platform infrastructure based on Apache Hadoop

    • Building data governance through data standardization

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