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DataDam

DataDam Overview

Unlike the data collection lake (like Lake), it is the storage for analysis with clear objectives (like Dam). It is based on the standard Hadoop eco and provides data collection, storage, processing functions for big data analytics. And it supports nonstop scale-out as data expand. It is proposed as a big data repository for use without building a separate big data lake, for a link with existing big data system, or for separate analytics.

DataDam Features

  Data Interface



Optimize data capacity space by supporting various compression formats when collecting corporate data on a time scale.


Get operation easy by supporting the Job Process based on the workflow when collecting data.


Support various types - structured, unstructured, semistructured - of data collection, storage, and processing.

  Optimum Performance Support


Respond to errors of hardware or software by providing system redundancy.


Support easy Scale-Out without system disruption when data is expanded (added).

Support optimal analytics connectivity with Spark for AI analytics by supporting Parquet format

  Accept Apache Eco



Support future technology scalability by configure 100% open source-based big data platform.


Support open structure independent from specific companies.


Provide usability enhancement by supporting standard SQL and linkage with various visualization function.

  Optimum Performance Support

 Respond to errors of hardware or software by providing system redundancy.


 Support easy Scale-Out without system disruption when data is expanded (added).


 Support optimal analytics connectivity with Spark for AI analytics by supporting Parquet format.

  Accept Apache Eco

 Support future technology scalability by configure 100% open source-based big data platform.


 Support open structure independent from specific companies.


 Provide usability enhancement by supporting standard SQL and linkage with various visualization function.

AgileSoDA


AgileSoDA supports companies to transform AI capabilities and output into internal intellectual assets and to resolve data quality problems. It also provides algorithm-based technologies and services including reinforcement learnings for optimal business decision.

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