There are many definitions for structured data. The term structured data can be referred as the data that resides in a fixed field within a record or a file. It can be also defined as the data that has a defined length and format. Examples of the structured data include numbers, dates, strings, records in databases, spreadsheets, etc.,. This kind of data can be easily entered, stored, analysed and queried.
In reality, structured data is taking on a new role in the world of Big Data. There are two main sources of big structured data.
Computer or Machine generated Structured Data
Sensor Data : Data generated by Radio Frequency ID (RFID) tags, Smart meters, medical devices, Global Positioning System (GPS) and so on.
Web log Data : Data generated in logs by servers, applications, networks and so on. This data can be very useful, for example they were used to predict security breaches.
Point of sale data : When the cashier swipes the bar code of any product that you are purchasing, all that data associated with the product is generated.
Financial data : Lots of financial systems are now programmatic, they are operated based on predefined rules that automate processes. Lets take stock trading as an example. It contains structured data such as the company symbol and dollar value.
Human generated Structured Data
Input data : This is any piece of data that a human might input into a computer, such as name, age, income, non-free-form survey responses, and so on.
Click Stream data : Data is generated every time you click a link on a website. This data can be analyzed to determine customer behaviour and buying patterns.
Gaming Related data : Every move you make in a game can be recorded. This can be useful in understanding how end users move through a gaming portfolio.
There are many other data sources which may not be that big on its own. Above given data are the ones which generate really big structured data. Happy Learning! Happy Exploring!
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