Database Reference
In-Depth Information
Figure 1.2
Examples of what can be learned through genotyping, from
As illustrated by the examples of social media and genetic sequencing, individuals
and organizations both derive benefits from analysis of ever-larger and more
complex datasets that require increasingly powerful analytical capabilities.
1.1.1 Data Structures
Big data can come in multiple forms, including structured and non-structured
data such as financial data, text files, multimedia files, and genetic mappings.
Contrary to much of the traditional data analysis performed by organizations,
most of the Big Data is unstructured or semi-structured in nature, which requires
different techniques and tools to process and analyze. [2] Distributed computing
environments and massively parallel processing (MPP) architectures that enable
parallelized data ingest and analysis are the preferred approach to process such
complex data.
With this in mind, this section takes a closer look at data structures.
Figure 1.3
shows four types of data structures, with 80-90% of future data growth
coming from non-structured data types. [2] Though different, the four are
commonly mixed. For example, a classic Relational Database Management System
(RDBMS) may store call logs for a software support call center. The RDBMS may
