Big Data is based on the 4V principle, which is Volume, Velocity and Variety. It depends on the purpose of the speaker and the reference. Technically and Scientifically- Data with large volumes that are generated from a variety sources at high velocity that authenticates the source is called Big Data. It is dependent on the source. Every day, data can be generated on the internet in many forms, including web, text, images, videos and social media posts.
Let’s take a closer look at the Big Data principles:
Volume-
Volume refers to the large number of data generated each second. These characteristics can be attributed to either user-generated or machine data. The computer is constantly evolving data at a speed of 10x: gigabytes, petabytes, and zettabytes.
Velocity-
Velocity refers to the speed at which data is generated. We have multiplied 500 times the data we generate since 2005. By 2020, 64.2 zettabytes will be generated. This number is expected to increase by 180 zettabytes over the next five-years. [1]
Variety-
Variety is a collection of unstructured, semi-structured and structured data. They can be created by humans or machines. Structured data, such as text, tweets and photos, is the most common data. Audio recordings, written text, and audio recordings, are unstructured data.
Veracity-
Big Data’s veracity is based on the objectivity of data. It determines whether the generated data is trustworthy and true. This is the most important V in Big Data. Data can sometimes be pulled from multiple data sets in different instances. This results in homogeneity. This makes it nearly impossible to trace the source. Is this why the data is so homogeneous? This can lead to faulty decisions. Data that can be traced back at the source to confirm authenticity are used in critical decision-making.
Big Data Opportunities and Challenges
There are approximately 1.7 billion [2] websites that are currently online. 800 million [3] are currently providing information about Big Data.
It is important to believe that Big Data will be the next big thing. Big Data offers a wealth of potential in Education, Health, Transportation, Research, and other areas. Traditional approaches to dealing with large quantities of data are inconsistent. We must look at the challenges associated with Big Data to understand them.
Big Data Challenges –
Data Collection and Acquisition-
Big data cannot be generated in a vacuum. It must be recorded from a data source. It is important to set up a filter that does not restrict important information. The vast majority of data generated is not of any use. It is therefore necessary to filter [4] data at the time of collection. It is a costly, but extremely difficult task to create filters that will save only the most useful data. Recording metadata, which explains how data was generated, is the second big data challenge. The acquisition of metadata acquisition software is a great way to reduce the human effort required to record data. Data provenance is also important, as the data generated must be recorded at the source and defined at source data pipeline. This is necessary to determine the truthfulness and accuracy of data.
Big Data Variety-
Big Data continues to be concerned about heterogeneity as data is generated from multiple sources. Complexity of data obtained from user-generated data comes from multiple sources and large numbers of users. Tweets, Instagram photos, discussion videos and groups are just a few examples of heterogeneity.
Multiple transactions can generate transactional data. These transactions occur every second and are subject to different payment methods. These transactions are linked to multiple products and services that can be rented, purchased, or granted simultaneously. It is difficult to identify the source of the information and combine it with data pipelines.
Scientific Data is created with complex physical observations and observation recorded with extreme complexity. It takes extreme care to form observation with distinct source and keep consistency.
Big Data Integration & Cleaning-
It is difficult to simply record data and put it in a repository, as heterogeneous data is. It is not enough to simply record the data and put it in a repository. Data analysis can be difficult and complicated.
This is necessary because of time and labour limitations for large-scale data analysis. This indifference is countered by introducing semantics in a way that the computer can understand so that it can automatically be solved. To achieve error-free resolution, it takes a lot of work at data integration.
Human Collaboration-
Humans can relate to many patterns. Machines are not capable of understanding human behavior to an extent. This is despite the fact that computational algorithms are based on real-world experience. Analytics of big data can’t be considered completely computational.
It needs human intervention [5] to form patterns and make decisions. Today, there are multiple experts in different fields to help understand the results of computationally analysed data.
To gain insights into data, it is very expensive to bring together experts from many different fields. This makes it very expensive to gather experts from different fields to gain insight into data. The data and findings must be kept secret and open to scrutiny by the experts. Crowdsourcing is a popular way to obtain results.
Conclusion –
Big Data is still a new technology, but it has the potential for rapid growth. This technology is extremely cost-effective and easy to use because it allows for micromanagement. Big Data is a game changer. Many companies are adopting a more analytical approach when making decisions. Small changes in the Company’s decision-making process can help save a lot of money. Every option that can improve the system’s efficiency is worth exploring. You have the opportunity to explore the Big Data concepts and create a rewarding career with edifypath.
