BIG DATA
Big Data meaning a data that is huge in size. Big data is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Its examples include stock exchanges, social media sites, Facebook accounts of the world, google maps, etc.
CHARACTERISTICS OF BIG DATA
The important characteristics of big data are:(i) Volume: The name Big Data itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, 'Volume' is one characteristic which needs to be considered while dealing with Big Data solutions.
(ii) Variety: Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining and analyzing data.
(iii) Velocity: The term 'velocity' refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous. (iv) Variability: This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
Types of big data:
Based upon form, the big data can be classified into following three types:1. Structured: Any data that can be stored, accessed and processed in the form of fixed format is termed as a 'structured' data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. However, nowadays, we are foreseeing issues when a size of such data grows to a huge extent, typical sizes are being in the rage of multiple zettabytes.
2. Unstructured: Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Now day organizations have wealth of data available with them but unfortunately, they don't know how to derive value out of it since this data is in its raw form or unstructured format. For examples the output returned by 'Google Search' is unstructured data.
3. Semi-structured: Semi-structured data can contain both the forms of data. We can see semi- structured data as a structured in form but it is actually not defined with e.g. a table definition in relational DBMS. Example of semi-structured data is a data represented in an XML file.
Advantages of Big Data Processing
1. Ability to process Big Data in DBMS brings in multiple benefits, such as, the businesses can utilize outside intelligence while taking decisions.2. Access to social data from search engines and sites like Facebook, twitter are enabling organizations to fine tune their business strategies and improved customer service.
3. Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
4. Early identification of risk to the product/services, is helpful for business purpose if any
5. Analysis of big data provides better operational efficiency.
6. Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data.
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