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Big Data
Dr. Marwan Al-Tawil
Introduction
▪What is the difference between Big Data, Data Science and Data
analytics ?
Big data: refers to significant volumes of data that cannot be processed
effectively with the traditional applications that are currently used. The
processing of big data begins with raw data that isn’t aggregated and is most
often impossible to store in the memory of a single computer.
Data science: Dealing with unstructured and structured data, data science is
a field that comprises everything that is related to data cleansing,
preparation, and analysis.
Data analytics: is the science of examining raw data to reach certain
conclusions.
Data types
▪Data are raw facts that have not been processed to produce meaning.
▪Under the notion of Big Data, data can be one of three types:
▪Structured Data: Data stored in Tabular format (rows and columns that
are related). It is clearly defined and stored in a pre-defined data model.
E.g. Excel files or SQL (relational) databases.
- Two sources of structured data: Machine generated and Human
generated data
Around 20% of data are structured. Proper view and understanding of data
(tables represent related data, and links between table shows relationships)
Data types
▪Unstructured Data: have not pre-defined structure (it has no Data
Model).
- It is described as irregular and ambiguous.
- around 80% of data are unstructured.
- Examples, satellite images, human generated videos, Audio and Text ,
PDF documents (Social Media Data).
- Two sources of structured data: Machine generated and Human
generated data
▪Semi-structured data: difficult to categorise, and we can’t store this type
of data in traditional database models
- examples include XML, JSON
Example of structured data
Examples of Unstructured data
Example of Semi-structured Data
▪An example of a JSON object is:
{
"ID": "22222",
"name": {
"firstname: "Albert",
