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Main que:Briefly describe each of the four classifications of Big Data structure types (i.e. Structured to Unstructured) and provide an example of each , using real-world organizations.
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shivan-Data Science & Big Data Analy (ITS-836-14) – Second Bi-Term
Classification of Big Data Structure Data Types
Big data refers to huge amounts of data that are too large to process and analyze by the use of traditional tools hence the data is not stored or managed efficiently. Terabytes of data are being uploaded onto different Apps daily and this poses a real threat to data analysis. There are three classifications of big data structure types, the unstructured, structured and semi-structured.
Structured Data Type
Structured data is the data stored in databases and is ordered. The two main sources of structured data type are humans and the machine. Machine-generated data include those obtained from sensors, financial systems and weblogs. Human-generated structured data type includes those input by the user, such as the name and location
Semi-Structured Data Type
The semi-structured data type is that which are in the database but are not classified as structured and still have some value that can be processed and analyzed. NoSQL documents are considered as semi-structured since they contain keywords that can be used in document processing.
Unstructured Data Type
The unstructured data type has no clear format in storage. They can either be machine-generated or human-generated. Machine-generated data include those from satellites like images and the scientific researches obtained from experiments, (Rebentrost 2014). The human-generated unstructured data type is those that have found mostly on mobile apps, social media and website contents.
Automated Data Type
The automated data type is that which is collected without human intervention from real-world systems. These systems include X-Rays and Life support systems.
shivk-There are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data:
It refers to a massive amount of data that keeps on growing exponentially with time.
It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
It includes data mining, data storage, data analysis, data sharing, and data visualization.
The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data
Structured
By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner.
Unstructured
Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data.
Semi-structured
Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data
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