BIg Data Analysis Life Cycle | DSBDA | TE IT | viresh chapte
Big Data analysis differs from traditional data analysis primarily due to the volume, velocity and variety characteristics of the data being processes. To address the distinct requirements for performing analysis on Big Data, a step-by-step methodology is needed to organize the activities and tasks involved with acquiring, processing, analyzing and repurposing data. The upcoming sections explore a specific data analytics lifecycle that organizes and manages the tasks and activities associated with the analysis of Big Data. From a Big Data adoption and planning perspective, it is important that in addition to the lifecycle, consideration be made for issues of training, education, tooling and staffing of a data analytics team.
The Big Data analytics lifecycle can be divided into the following nine stages, as shown in Figure 3.6:
Business Case Evaluation
Data Identification
Data Acquisition & Filtering
Data Extraction
Data Validation & Cleansing
Data Aggregation & Representation
Data Analysis
Data Visualization
Utilization of Analysis Results