Big Data Modelling and Analysis: Databases and Spreadsheets | Year 9 Computing (Cambridge) PDF Download

Introduction to Big Data

Big data refers to extremely large datasets that cannot be easily managed or analyzed using traditional data processing tools. It includes structured, semi-structured, and unstructured data, often generated at high velocity from various sources such as social media, sensors, and transactions.

Characteristics of Big Data

• Volume: The amount of data is vast.
• Velocity: Data is generated rapidly and continuously.
• Variety: Data comes in different formats (e.g., text, images, videos).
• Veracity: The quality and accuracy of the data can vary.
• Value: The potential insights and benefits derived from analyzing the data.

Example:

• Imagine a social media platform like Twitter. Every second, millions of tweets are posted. These tweets contain text, images, videos, and links, generating a massive volume of data at high speed.

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Data Modelling Techniques

Data modelling is the process of creating a visual representation of a system or database to show how data is stored and accessed. There are different techniques used in data modelling, each serving a specific purpose.

Entity-Relationship (ER) Model

The ER model is used to describe the data and the relationships between different entities in a database.
It uses:

• Entities: Objects or things that can be distinctly identified (e.g., Student, Course).
• Attributes: Properties or details of an entity (e.g., Student Name, Course ID).
• Relationships: Connections between entities (e.g., a Student enrolls in a Course).

Example

• For a school database, entities might include Student, Teacher, and Course. Attributes for a Student entity could be ID, Name, and Age, while attributes for a Teacher entity could be ID, Name, and Subject. Relationships could include a Student enrolling in a Course and a Teacher teaching a Course.

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Normalization

• Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. 
• It involves dividing a database into two or more tables and defining relationships between them.
• Example: Consider a table containing student information and their courses. Before normalization, all information might be stored in one large table. After normalization, student information might be stored in one table, course information in another, and a third table might record which students are enrolled in which courses.

Using Databases for Data Management

• Databases are essential for storing, organizing, and managing large amounts of data. They enable efficient data retrieval, manipulation, and updating.

Relational Databases

• Relational databases store data in tables that are related to each other. They use Structured Query Language (SQL) for managing and querying data.
• Example: A library database might have tables for Books, Members, and Loans. The Books table contains information about each book, the Members table contains information about each member, and the Loans table records which member has borrowed which book.

NoSQL Databases

• NoSQL databases are designed to handle unstructured data and are scalable. They include document databases, key-value stores, wide-column stores, and graph databases.
• Example: A NoSQL document database like MongoDB might store data in a JSON-like format, where a book record includes the title, author, and a list of copies with their statuses.

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Conclusion

Understanding big data, data modelling techniques, and using databases for data management are essential skills in today's data-driven world. These concepts help in organizing, managing, and analyzing large