A Glance at Big Data Analytics

What is Big Data?

Big Data is a body of information that is enormous in volume and is constantly expanding exponentially. No traditional data management tools can effectively store or process this data because of its size and complexity. Big data is an extremely large type of data.

Practical Examples

With a daily production of one terabyte of new trade data, the New York Stock Exchange is an example of big data.

According to the statistic, Some top social media companies' databases receive data of more than 500 terabytes daily. This information is primarily produced through uploading images and videos, messaging, leaving comments, etc.

Thirty minutes of flight time can produce tens of terabytes of data from one jet's engine. The amount of data generated by the daily thousands of flights can amount to Petabytes.

Examples of Big Data Types

These are Examples of Big Data Types:

Structured

Structured data refers to data we can access, process, and store in a fixed format. Over time, computer science talent has successfully created methods for handling and extracting value from this data type. Today, we expect problems as this data's size increases significantly to zettabytes.

Unstructured

Unstructured data is any data whose form or structure is unknown. Unstructured data is enormous and presents several processing challenges that must be overcome to extract value from it. Unstructured data is frequently found in heterogeneous data sources that combine simple text files with images, videos, and other data types. Organizations today have a wealth of data at their disposal, but since this data is in its unstructured form, they cannot value-add from it.

Semi-Structured

Both types of data can be found in semi-structured data. Semi-structured data can appear to be structured, but it is not defined by a relational DBMS's definition of a table, for example. An XML file containing data is an example of semi-structured data.

Characteristics of Big Data

The following characteristics can describe big data:

Volume

Big Data refers to an enormous size by its very name. The size of the data is a very important factor in determining the value of the data. Additionally, the amount of data will determine whether or not a particular data set qualifies as big data. Therefore, one characteristic that needs to be considered when dealing with Big Data solutions is "Volume."

Variety

Different data sources and types, both unstructured and structured, are referred to as variety. In the past, most applications only looked at databases and spreadsheets as data sources. Today's analysis applications also consider data in the form of emails, videos, photos, PDFs, monitoring devices, audio, etc. These kinds of unstructured data present data mining, storage, and analysis challenges.

Velocity

The speed at which data is generated is called "velocity." The real potential in the data is determined by how quickly it is generated and processed to meet demands.

The speed at which data enters from sources such as business processes, application logs, networks, social media websites, sensors, mobile devices, etc. is referred to as big data velocity. There is an enormous and constant flow of data.

Variability

This refers to the inconsistency that the data may occasionally display, making it difficult to handle and manage the data effectively.

Big Data Processing Benefits

The benefits of being able to process big data in DBMS are many, including:

Smart Decision-Making

Organizations can now fine-tune their business strategies thanks to the availability of social data from search engines and websites like Facebook and Twitter.

Enhanced Client Services

New systems created using Big Data technologies are replacing conventional customer feedback systems. Big Data and natural language processing technologies are being used in these new systems to read and assess customer feedback.

Improved Operational Effectiveness

Before deciding which data should be moved to the data warehouse, big data technologies can be used to create a staging area or landing zone for new data. A company can offload rarely used data by integrating big data technologies and data warehouses in this way.