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UDAS
Big Data-Based "Unified Data Analytics Suite"
UDAS (Unified Data Analytics Suite) is a cloud-based Big Data analytics platform that integrates data collection, cleansing (ETL), statistical analysis, and visualization. Through its Data Cleansing Process Designer, UDAS shortens analysis time, enables process reuse, and provides an accessible analytical environment for both data scientists and business domain experts.
UDAS Architecture
1. Cleansing Process
Cleansing Process
Manages and reuses the process of transforming raw source data into analysis-ready data. Reduces data cleansing costs and preparation time by more than 50% while supporting reverse and forward engineering through integrated data modeling tools.
Cleansing Process - ETL
The ETL engine operates based on mapping metadata and loads query results directly into target databases. Users can execute immediate loads in the Query Designer or schedule automated batch jobs via the ETL Agent, complete with execution logging and error management.
2. Analysis Process
Analysis Process
Analysis templates enable the reuse of databases, queries, mappings, and analytical model metadata generated throughout the Big Data analysis lifecycle.

Analysis Process - Engine Server Service
When dataset volume or model complexity exceeds the processing capacity of a personal PC, tasks are seamlessly executed by the high-performance Analysis Engine Server Service.

3. Visualization Process
Visualization Process
Publishes statistical analysis results and interactive visualization views to the analytics portal for seamless sharing across the enterprise.

Frequently Asked Questions (FAQ)
Q. What kind of analytics environment does UDAS provide?
UDAS (Unified Data Analytics Suite) is a cloud-based Big Data analytics platform that integrates data collection, cleansing (ETL), statistical analysis, and portal visualization for both data scientists and business experts.
Q. What are the benefits of the Data Cleansing Process Designer?
By standardizing and reusing data cleansing processes and ETL mapping metadata, enterprises can reduce data preparation costs and processing time by more than 50%.
Q. How does UDAS handle large-scale Big Data that exceeds personal PC capacity?
When dataset sizes or complex analytical models exceed local PC limits, UDAS offloads execution to its high-performance Analysis Engine Server Service and publishes the results to the visualization portal.