Etu is a Big Data pioneer in providing Big Data solutions that are primarily concerned with helping customers discover, unlock, and connect valuable information embedded in extremely large data sets through simple steps. At Etu, our mission is to develop Big Data Solutions with Appliances centered on business development in Taipei and Beijing. Our Big Data business is dedicated to providing end-to-end solutions for customers to effectively harness the challenges of large data sets by utilizing Hadoop-related technologies, intimately working with all Hadoop distribution, and creating constant integration with third party tool vendors. Through our professionalism and expertise our core competency in Big Data is to address specific business problems for customers from diverse industries.

Big Data Processing

There is One 3V Circle for Each Big Data Processing User Case

Here is a 3-axis graph: Volume represents the size of data, Variety represents various formats of data, and Velocity represents the in-time needed for data processing.


Volume: data scale
Velocity: in-time processing
Variety: different data formats

Each processed Big Data case can be labeled on this 3-axis graph. An oval-shape shadow can be drawn. For each shadow on the above graph, each representing the following: 

 1 Relatively large volume, medium variety, and lower demand for in-time processing.

 2 Relatively small volume, less variety, and higher demand for in-time processing.

 3 Relatively small volume, more variety, and lower demand for in-time processing.


Each 3V Circle can Help Determine the Right Operating Strategy for Etu Big Data Processing

The operating strategy of Etu’s Big Data Processing Framework can be determined by basing each specific 3V circle and the formats of data and presented computing logic. After the strategy is determined, the rest is just data-in and processed data-out.

A completed Big Data End-to-End solution for solving a specific business problem.

If the data volume is increased, Worker Nodes can be scaled-out. If there is a need for in-time result increases, distributed database (non-SQL) in disparity can be applied. If there are increased formats of data then more sub-structures can be divided then processed.

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