Data Classification and Sources Quiz

Study Notes

Data comes in different forms, structured, semi-structured, and unstructured.
Structured data is organized and searchable, making it easy to find and consolidate information. Examples include databases and spreadsheets.
Unstructured data is harder to search and process, requiring more time and energy to analyze. Examples include emails, documents, images, and reports.
Semi-structured data is somewhere in between, with a degree of organization. An example is XML data.
Data is generated from many sources including social media, sensors, cell phones, GPS, purchase records, websites, emails, media streaming services, healthcare systems, and Internet of Things (IoT) devices.
70% of the data is generated by individuals, while enterprises are responsible for storing and managing 80% of it.
52% of travelers utilize social media to plan their vacations.
35% of Amazon purchases are driven by recommendations.
75% of Netflix content viewed is based on recommendations.
Businesses can leverage data effectively to make informed decisions and strategic moves.

Big data refers to massive amounts of data, both structured and unstructured, generated daily.
Its value lies in the insights gained from analyzing it, leading to better decisions and strategic business moves.
Big data is characterized by high volume, high velocity, and high variety.
It presents unique challenges for storage, analysis, capture, curation, search, sharing, transfer, visualization, querying, updating, and information privacy.
The "Vs" of big data refer to Volume, Velocity, Variety, Veracity, Variability, Visualization, and Value.
Traditional database management systems (DBMS) struggle to handle big data due to its size, variety, and velocity.
DBMS are primarily designed for structured data, making it difficult to categorize and manage unstructured data effectively.
RDBMS lacks the speed and scalability necessary to accommodate the rapid growth and high velocity of big data.
Scalability is essential for handling large volumes of data. DBMS might require additional processing units or memory to manage the growth of data.
Big data often comes from a variety of sources, making it difficult to manage with traditional methods.

Hadoop is an open-source framework that allows for distributed storage and processing of large datasets.
The Hadoop ecosystem includes a range of tools and technologies for managing and analyzing big data.
Several companies specialize in big data solutions, offering tools, services, and expertise in managing, analyzing, and extracting value from large datasets.
A career in big data can be rewarding, as companies increasingly rely on data professionals to analyze and interpret data, driving innovation and operational efficiency.
Key skills for a big data career include data analysis, programming, and knowledge of big data technologies like Hadoop.
Understanding the domain knowledge related to the industry you are interested in, will set you apart.