Significance of Data:

Data is one of the most commonly used words nowadays. Let’s discuss what it exactly means. You can define data very shortly as raw form of knowledge. The basic things that you may receive on something is Data.

This details on itself doesn’t carry any meaning or significance. Data needs to be analysed or also need to be organised in some cases to gain knowledge from the available data. Data can be numbers, characters, values and also be symbols. Computer performs operations on it to interpret. Then it can also be stored in a storage media or can be operated further.

Now let’s look at Big Data.

What is Big Data?

Big Data, as its’ name suggests, means a huge collection of available data. This huge data set can yet be exponentially growing over time. We need to apply additional management systems or tools to store or process such large sized complex data set.

Example:

Let’s look at some examples,

Netflix has a big data set for all their subscribers. Approximately 220 million subscribers’ data is collected. Whether they are pausing a video or playing a particular scene multiple time. Well, why so?

Feeding this big data also into their algorithm can let them improve their user experience. Suggesting you more videos, TV shows, series that you would be loving to watch. All data is collected to help and decide the next step.

According to statistics, Facebook deals everyday with about 4 petabytes of data. Similarly this data generated over the exchanged photos, videos, messages, comments everything that is uploaded.

One aircraft can record over 300,000 parameters with its’ engine data. An average commercial flight like a Boeing 737 can generate up to 20 terabytes of data per hour. Thus, This can sum upto 240 terabytes of data per each and every engine hour.

Types of Big Data:

These are the types of big data,

1. Structured
2. Unstructured
3. Semi-Structured
   

Structured:

The accessible data set that can be arranged in a structure and thus processed or operated, is termed as ‘Structured Data’. In years of Development amid new technological advancement and technical improvements, greater solutions are achieved while working with this kind of data.

The key point of working with structured data is that we know in advance about the structure of how the data is organised. However, foreseeing the issues is common in such huge databases. This extended data sets can range zettabytes in size.

Example:

A table containing all the parameters and reading from a health monitoring device is a good example of Structured Data.

Unstructured:

Data without any mentioned or defined structure is called an unstructured data. Besides of its’ huge size, Unstructured data faces many problems for deriving any information from it. Let’s have a simple example.

A heterogeneous data source that contained combination of images, text, video files. This is a very unfortunate thing if an organisation with such big data available cannot derivate the values from there.

Example:

Google Search results are a good example of Unstructured Data.

Semi-Structured:

It can be both types, Structured and Unstructured Data. Semi-Structured data can be seen as structured at first glance but it isn’t.

Example:

Data stored as in XML files.

Characteristics of Big Data:

Big data has these following points as characteristics:

(i) Volume:

Big data itself is enormous as we spoke of its’ size earlier. So, while determining value out of a huge data set, Size matters. Volume or in other words, size of the particular data also determines if it is considered as a big data or it’s not. Hence, the ‘Volume’ of the data must be considered as crucial to be dealt with.

(ii) Variety:

It simply signifies the nature of data that we are dealing with whether it’s a structured or unstructured data. From a long time, Spreadsheets and databases are thought to be the only sources of data. But now data stored in monitoring devices, documents, email, all are analysed while analysing data. This plenty of unstructured data have issues regard the storing and mining and analysation of data.

(iii) Velocity:

Velocity refers to the speed of how fast the data is generated. It also determines how fast the data will be process to fill the demands. Thus, It valuates the potential in the data. This massive and non-stop flow of data deals with data flows from the sources like networks, mobile devices, Social Media sites etc.

(iv) Variability:

The inconsistency shown by data patterns is a concern and yet can be found in the dataset time to time. Thus, it can harm the process of handling data with smart management skills.

Advantages:

Processing Big Data in DBMS include many benefits like:

• Customer Service Improvement
• Better Efficiency achieved by the presented model to the algorithm.
• Early identification to possible mishaps
• Outside impact of the product can be predicted to better the model and relaunching the product.