Rooted in even basic human everyday needs to even high-scale industrial requirements, technology has revolutionized our way of living. It has wormed itself in every aspect of our lives. With this shift in paradigm, the value of data as a resource has increased tenfold. Large datasets fall under the category of Big Data which requires numerous types of Big Data for use.
With 2.5 quintillion bytes of data being generated on a daily basis via cell phones, streaming videos, social networks, and most importantly, the Internet of Things. The significant growth of data in recent years has given way to numerous types of Big Data analytics. Collecting, processing, and analyzing Big Data requires the expertise of professionals in this field who can impart the necessary information that can aid an organization in growing. With Big Data training, you can secure yourself a high-profile job in this field.
Keep reading if you want to know more about Big Data, types of big data in data analytics, types of digital data in Big Data, which type of clustering could handle Big Data, 4 types of data analytics, and more.
What is Big Data?
Big Data can be defined as a high amount of data that cannot be processed or stored with the help of standard processing equipment and data storage. A massive amount of data is produced daily, and interpreting and manually processing complex and expansive datasets are next to impossible. It requires modern tools and expert skills to interpret large volumes of data and provide them to organizations with valuable insights to help businesses grow. Let's discuss various types of big data in detail.
Different Types of Big Data
Big data types in Big Data are used to categorize the numerous kinds of data generated daily. Primarily there are 3 types of big data in analytics. The following types of Big Data with examples are explained below:-
A. Structured Data
Any data that can be processed, is easily accessible, and can be stored in a fixed format is called structured data. In Big Data, structured data is the easiest to work with because it has highly coordinated measurements that are defined by setting parameters. Structured types of Big Data are:-
Overview:
- Highly organized and easily searchable in databases.
- Follows a predefined schema (e.g., rows and columns in a table).
- Typically stored in relational databases (SQL).
Examples:
- Customer information databases (names, addresses, phone numbers).
- Financial data (transactions, account balances).
- Inventory management systems.
- Metadata (data about data).
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Merits:
- Easy to analyze and query.
- High consistency and accuracy.
- Efficient storage and retrieval.
- Strong data integrity and validation.
Limitations:
- Limited flexibility (must adhere to a strict schema).
- Scalability issues with very large datasets.
- Less suitable for complex big data types.
B. Semi-structured Data
In Big Data, semi-structured data is a combination of both unstructured and structured types of big data. This form of data constitutes the features of structured data but has unstructured information that does not adhere to any formal structure of data models or any relational database. Some semi-structured data examples include XML and JSON.
Overview:
- Contains both structured and unstructured elements.
- Lacks a fixed schema but includes tags and markers to separate data elements.
- Often stored in formats like XML, JSON, or NoSQL databases.
Examples:
- JSON files for web APIs.
- XML documents for data interchange.
- Email messages (headers are structured, body can be unstructured).
- HTML pages.
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Merits:
- More flexible than structured data.
- Easier to parse and analyze than unstructured data.
- Can handle a wide variety of data types.
- Better suited for hierarchical data.
Limitations:
- More complex to manage than structured data.
- Parsing can be resource-intensive.
- Inconsistent data quality.
C. Quasi-Structured Data
Overview:
- Loosely structured data that does not fit neatly into traditional database schemas.
- Contains some organizational properties but lacks a fixed structure.
- Often encountered in large-scale data systems and logs.
Examples:
- Log files (system logs, application logs).
- Clickstream data from web analytics.
- Sensor data streams.
- Social media feeds.
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Merits:
- Can provide valuable insights with proper analysis.
- Flexible data format suitable for big data systems.
- Facilitates real-time data processing.
- Capable of capturing a wide range of data types.
Limitations:
- Data extraction and transformation can be challenging.
- Higher storage and processing costs.
- Requires specialized tools for analysis.
D. Unstructured Data
Unstructured data in Big Data is where the data format constitutes multitudes of unstructured files (images, audio, log, and video). This form of data is classified as intricate data because of its unfamiliar structure and relatively huge size. A stark example of unstructured data is an output returned by ‘Google Search’ or ‘Yahoo Search.’
Overview:
- Data that does not conform to a predefined schema.
- Includes text, multimedia, and other non-tabular data types.
- Stored in data lakes, NoSQL databases, and other flexible storage solutions.
Examples:
- Text documents (Word files, PDFs).
- Multimedia files (images, videos, audio).
- Social media posts.
- Web pages.
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Merits:
- Capable of storing vast amounts of diverse data.
- High flexibility in data storage.
- Suitable for complex data types like multimedia.
- Facilitates advanced analytics and machine learning applications.
Limitations:
- Difficult to search and analyze without preprocessing.
- Requires large storage capacities.
- Inconsistent data quality and reliability.
Subtypes of Data
Overview:
- Different categories within the main types of big data.
- Each subtype has unique characteristics and use cases.
- Important for selecting appropriate data management and analysis tools.
Examples:
- Time-series data (financial market data).
- Spatial data (geographic information systems).
- Graph data (social networks).
- Machine-generated data (IoT sensor data).
Merits:
- Tailored analysis techniques for each subtype.
- Enhanced insights and decision-making.
- Optimized storage and processing solutions.
- Improved data relevance and context.
Limitations:
- Requires specialized tools and expertise.
- Can be resource-intensive to manage.
- Integration of multiple subtypes can be complex.
Comparison Table: Structured vs Unstructured vs Semi-Structured Data
|
Feature
|
Structured Data
|
Semi-Structured Data
|
Unstructured Data
|
|
Schema
|
Fixed schema (rows and columns)
|
Flexible schema (tags, markers)
|
No fixed schema
|
|
Storage
|
Relational databases (SQL)
|
NoSQL databases, XML, JSON
|
Data lakes, NoSQL databases
|
|
Searchability
|
High
|
Moderate
|
Low
|
|
Flexibility
|
Low
|
High
|
Very high
|
|
Ease of Analysis
|
Easy
|
Moderate
|
Difficult
|
|
Data Types
|
Numeric, categorical
|
Hierarchical, mixed types
|
Text, multimedia, complex types
|
|
Scalability
|
Moderate
|
High
|
Very high
|
|
Common Use Cases
|
Financial systems, inventory
|
Web APIs, email, HTML
|
Social media, documents, media
|
Conclusion
Data has been extensively used in recent years in every aspect of our lives and every possible sector of global industries. It is one of the most valuable resources in the market, used to optimize any operational process. As an aspirant of data science, it is imperative to have the basic skills and knowledge about fundamental aspects of data analysis and to learn about the different types of big data. You can take your first step into this lucrative career field by pursuing a reliable course or undergoing professional training. A great way to start is by taking part in KnowledgeHut’s Big Data training.
Frequently Asked Questions (FAQs)
1. What are the three types of Big Data classification?
Big Data can be categorized into three parts
- Structured Data
- Unstructured Data
- Semi-Structured Data
2. What are the 4 components of Big Data?
The 4 main components of Big Data are
- Ingestion
- Transformation
- Load, analysis
- Consumption.
3. What are the 6 characteristics of Big Data?
Big Data has the following 6 characteristics
- Volume
- Variety
- Velocity
- Value
- Veracity
- Variability
![Different Types of Big Data [with Examples]](/_next/image?url=https%3A%2F%2Fd2o2utebsixu4k.cloudfront.net%2Fmedia%2Fimages%2Fd270f647-7871-4f5b-ac21-4ce394548a47.png&w=1920&q=75)
