The 5 vs of big data mean volume, velocity, variety, veracity, and value (Anuradha, 2015). Volume means the amount of data that can be processed to provide detailed information about any situation. The proposed enterprise should ensure that it gathers the right information (Anuradha, 2015). Velocity infers to the speed that can be used to process the same large amount of data. An Enterprise Splunk Solution for this situation is to filter the data that are only relevant for the business to cut down the processing period (Anuradha, 2015). Variety is a condition of big data since most of it is unstructured data implying that the time for processing such information will take longer (Anuradha, 2015). Thus, the proposed enterprise needs to understand the contents of the data that should be collected as well as the update rate of the same information. Veracity is the condition that determines whether the source of the data is authentic (Anuradha, 2015). This factor can be easily proved using various search protocols to ensure that the data collected is accurate and authentic. The value of the information is critical since computing such vast amounts of information should have some lucrative advantage (Anuradha, 2015). The value of the data should be synonymous with the reason why the Enterprise Splunk Solution was created in the first place.
The key value log in format is more complex for a system that analyzes big data (Oborski, 2016). Big data takes a longer period to process binary information. However, the human-readable log in format significantly reduces the processing time. Thus, for a system or software that relies on big data analysis, the human-readable log in is preferable to the key value format (Oborski, 2016).
The monitoring of the pre-production systems is more efficient than monitoring the production system (Oborski, 2016). The pre-production system consists of integration and development. This system offers the programmers the opportunity to simulate and review the different features and programs that can best fit the functionality of the intended product (Oborski, 2016). The reliance on pre-production systems also cuts down the cost since the product that will be finally developed would have been tried and tested to ensure it operates at optimum performance (Oborski, 2016).
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