Big Data includes huge volume, high velocity, and extensible variety of data. These are 3 types: Structured data, Semi-structured data, and Unstructured data.
- Structured data –
Structured data is data whose elements are addressable for effective analysis. It has been organized into a formatted repository that is typically a database. It concerns all data which can be stored in database SQL in a table with rows and columns. They have relational keys and can easily be mapped into pre-designed fields. Today, those data are most processed in the development and simplest way to manage information. Example: Relational data.
- Semi-Structured data –
Semi-structured data is information that does not reside in a relational database but that have some organizational properties that make it easier to analyze. With some process, you can store them in the relation database (it could be very hard for some kind of semi-structured data), but Semi-structured exist to ease space. Example: XML data.
- Unstructured data –
Unstructured data is a data that is which is not organized in a pre-defined manner or does not have a pre-defined data model, thus it is not a good fit for a mainstream relational database. So for Unstructured data, there are alternative platforms for storing and managing, it is increasingly prevalent in IT systems and is used by organizations in a variety of business intelligence and analytics applications. Example: Word, PDF, Text, Media logs.
Differences between Structured, Semi-structured and Unstructured data:
|Properties||Structured data||Semi-structured data||Unstructured data|
|Technology||It is based on Relational database table||It is based on XML/RDF||It is based on character and binary data|
|Transaction management||Matured transaction and various concurrency technique||Transaction is adapted from DBMS not matured||No transaction management and no concurrency|
|Version management||Versioning over tuples,row,tables||Versioning over tuples or graph is possible||Versioned as whole|
|Flexibility||It is sehema dependent and less flexible||It is more flexible than structuded data but less than flexible than unstructured data||it very flexible and there is abbsence of schema|
|Scalability||It is very difficult to scale DB schema||It’s scaling is simpler than sstructured data||It is very scalable|
|Robustness||Very robust||New technology, not very spread||—|
|Query performance||Structured query allow complex joining||Queries over anonymous nodes are possible||Only textual query are possible|
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- Large objects(LOBs) for Semi Structured and Unstructured Data
- What is Unstructured Data?
- What is Structured Data?
- What is Semi-structured data?
- Difference between Structured Query Language (SQL) and Transact-SQL (T-SQL)
- Difference between Structured and Object-Oriented Analysis
- Difference between Data Scientist, Data Engineer, Data Analyst
- Difference between Data Warehousing and Data Mining
- Difference between Data Lake and Data Warehouse
- Difference Between Data Science and Data Visualization
- Difference between a Data Analyst and a Data Scientist
- Difference Between Data Science and Data Mining
- Difference between Data Scientist and Data Engineer
- Difference Between Data Visualization and Data Analytics
- Difference Between Data Science and Data Analytics
- Difference Between Data Mining and Data Visualization
- Difference between Data Privacy and Data Security
- Difference between Data Warehouse and Data Mart
- Difference Between Data Mining and Data Analysis
- Difference between Data Privacy and Data Protection
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