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Types of Data Under Big Data: A Tabular Guide with Examples ⚡

Big data is a term that describes the massive amount of data that is available to organizations and individuals from various sources and devices 📱. This data is so large and complex that traditional data processing tools cannot handle it easily 💥.

But what are the different types of data under big data? How can we classify and organize them in a tabular format? And what are some examples of each type of data? In this article, we will answer these questions and more 🚀.

We will also look at some of the benefits and challenges of each type of data under big data 🔥.

Types of Data Under Big Data 🌈

There are three main types of data under big data: structured, semi-structured, and unstructured data 📄.

Each type of data has its own characteristics, sources, formats, and uses 💯.

Let's look at each type of data in detail and compare them in a tabular format ✨.

Structured Data 💎

Structured data is data that is easily formatted and stored in relational databases, such as numbers, dates, or text. Structured data has a predefined schema and structure that can be queried using SQL (Structured Query Language) 💯.

Structured data is also called relational data because it is split into multiple tables to enhance the integrity of the data by creating a single record to depict an entity. Relationships are enforced by the application of table constraints 🔮.

Structured data is easy to enter, query, and analyze because all of the data follows the same format 💡.

However, structured data has limited flexibility and scalability because any change in the schema or structure requires updating all of the records to adhere to the new rules 🙅‍♂️.

Some examples of structured data are customer records, sales transactions, product inventory, bank accounts, etc. 💰.

Semi-Structured Data 🌟

Semi-structured data is data that is partially formatted and stored in non-relational databases, such as JSON or XML files. Semi-structured data has some elements of structure, such as tags or keys, but does not follow a rigid schema or structure 🔮.

Semi-structured data is also called non-relational or NoSQL data because it does not use tables or SQL to store or query data 💯.

Semi-structured data is more flexible and scalable than structured data because it can accommodate different types and formats of data without changing the schema or structure 💡.

However, semi-structured data is more complex and challenging to query and analyze than structured data because it requires special tools and techniques to handle the variety and variability of data 🙅‍♀️.

Some examples of semi-structured data are web logs, social media posts, email messages, sensor data, etc. 💰.

Unstructured Data 💫

Unstructured data is data that is free-form and less quantifiable, such as text, audio, video, or images. Unstructured data does not have a predefined schema or structure and cannot be easily queried using SQL 🔥.

Unstructured data is also called non-tabular or raw data because it does not use tables or columns to store or query
data 💯.

Unstructured
data is more diverse
and dynamic than structured
or semi-structured
data because it can capture
and represent
any kind
of information
without any constraints 💡.

However,
unstructured
data is more difficult
and expensive to store,
process,
and analyze than structured
or semi-structured
data because it requires more storage
space,
processing power,
and advanced analytics techniques 🙅‍♂️.

Some examples
of unstructured
data are documents,
books,
articles,
podcasts,
videos,
or photos 💰.

Tabular Comparison of Types of Data Under Big Data 📊

Type Definition Source Format Use Benefit Challenge
Structured Data that is easily formatted and stored in relational databases Databases, spreadsheets, surveys Numbers, dates, text SQL queries, BI tools Easy to enter, query, and analyze Limited flexibility and scalability
Semi-Structured Data that is partially formatted and stored in non-relational databases Web logs, social media posts, email messages JSON, XML files NoSQL queries, API calls Flexible and scalable Complex and challenging to query and analyze
Unstructured Data that is free-form and less quantifiable Documents, books, articles,podcasts,videos , photos Text,audio , video , images Machine learning,NLP , computer vision , sentiment analysis Diverse and dynamic Difficult and expensive to store , process ,and analyze

Conclusion 🎉

In this article,
we learned about the types
of data under big
data: structured,
semi-structured,
and unstructured
data 🤔.

We also learned about how to classify
and organize them in a tabular format with examples 🚀.

We also learned about some of the benefits
and challenges of each type
of data under big
data 🔥.

I hope you enjoyed this article
and learned something new 😊.

If you have any questions or feedback,
please feel free
to leave a comment below 👇.

Happy learning! 🙌

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