Data complexity is another factor to consider. Many people think of big
data primarily as consisting of “unstructured” data from the web and
social sources, machine logs, or Internet of Things (IoT) sensor and
tracking output. This type of data may not be amenable to conventional
database methods. Big data projects frequently include data of multiple
types from both traditional and nontraditional sources. Data scientists may
incorporate data from relational databases, such as OLTP systems, along
with unstructured data sources. The situation becomes complex quickly.
As important as these elements are, for many of us in IT, the central
challenge of big data comes down to how best to tackle the infrastructure
needs of big data projects—i.e., the “significant logistical challenges”
recognized by the OED and the inability to process big data using “traditional
database management tools” pointed out by Merriam Webster
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