**2.1 Literature review**

Big Data (Data Intensive) Technologies aim to process (1) highvolume, highvelocity, high-variety data (sets/assets) to extract intended data value and ensure high-veracity of original data and obtained information; this calls for cost-effective, innovative forms of data and information processing (analytics) for improved insight, decision-making, and process control; all of these call for (should be supported by) new data models (supporting all data states and stages during the whole data lifecyle) and infrastructure services and tools. Generally, the term "big data" refers to the rapidly expanding volume and velocity of data sets that are being accessible and connected. According to studies, big data may generally be defined using the four (4) V's of big data. The five properties of volume, value, diversity, velocity, and veracity are frequently used to describe big data, which is a collection of data from various sources. Big data analytics, which some academics define as the capacity to compile and analyze those fine-grained data sets, is already altering how insurers see sizable client bases, manage risks, and meet the diverse needs of their clients. Kabanda [4] defines big data analytics as the straightforward application of analytics approaches to significant data sets. The five properties of volume, value, diversity, velocity, and veracity are frequently used to describe big data, which is a collection of data from various sources. Many significant businesses employ software for machine learning, artificial intelligence, data mining, cybersecurity, and other big data. Big data analytics, which some academics define as the capacity to compile and analyze those finegrained data sets, is already altering how insurers see sizable client bases, manage risks, and meet the diverse needs of their clients. OECD-FAO [4] defines big data analytics as the straightforward application of analytics approaches to significant data sets.

The types of analytics applicable in the oilseeds and textile industries are shown on **Figure 5** and are briefly explained below:


*A Big Data Analytics Architecture Framework for the Production and International Trade… DOI: http://dx.doi.org/10.5772/intechopen.107225*

c. *Prescriptive analytics* - Prescriptive analytics helps organizations assess their current situation and make informed choices on alternative course of events based on valid and consistent predictions. It combines analytical outcomes from both descriptive and predictive models to look at assessing and determining new ways to operate to achieve desirable outcomes while balancing constraints indicated that prescriptive analytics enables decision makers to look into the future of their mission critical processes and see the opportunities as well as presents the best course of action to take advantage of that foresight in a timely manner.

Machine learning is a method for instructing computers to learn (ML). Big data analytics is known to be automated using machine learning, which also creates models of the fundamental relationships in the data. The way we teach, learn, and study in the educational setting could be completely changed by machine learning (ML). Localization, transcription, text-to-speech, and personalisation are just a few of the ways that machine learning is expanding the reach and impact of online learning content [5]. Data mining can be handled through machine learning. According to Truong [6], there are three types of ML:


Machine Learning essentially includes programming analytical model construction and is a technique of big data analytics [7].

Data mining is the process of discovering anomalies, trends, and correlations in large data sets in order to predict outcomes [8]. Data mining is most usually

characterized as the process of searching massive sets of data for patterns and trends using computers and automation, then translating those findings into business insights and predictions. Data mining is an important element of data analytics and one of the fundamental disciplines in data science, in which advanced analytics techniques are used to extract meaningful information from large data sets. While both are valuable for spotting patterns in enormous data sets, they work in quite different ways. The practice of detecting patterns in data is known as data mining. And, while data mining is sometimes used as part of the machine learning process, it does not necessitate continual human engagement (e.g., a self-driving car relies on data mining to determine where to stop, accelerate, and turn).

Computer systems that imitate human intellectual processes, such as learning, reasoning, and self-correction, are referred to as artificial intelligence (AI). The ability of AI to arrive at a solution based on facts rather than a predetermined series of procedures is what most closely mimics the human brain's thinking function. Artificial intelligence (AI) is defined by its ability to replicate human behavior and cognitive processes, to capture and preserve human expertise, to respond swiftly, and to manage large amounts of data quickly.

As a result, cyber security has become an important concept in everyday life, and cyber security knowledge is critical in preventing cyber attacks on people and systems. With the rise of a global and borderless information culture, the internet has brought and continues to present new opportunities to all countries globally, as technologies play a key role in social and economic development [9]. With the rise of a global and borderless information culture, the internet has brought and continues to present new opportunities to all countries globally, as technologies play a key role in social and economic development [9]. Cyber security refers to strategies used to secure sensitive data, computer systems, networks, and software applications from cyberattacks, according to [10]. The cyber security concept's main purpose is to protect data confidentiality and integrity while also providing data availability when it's needed. However, as the nature of cyber threats changes, so does public concern about cyber security issues like social engineering and phishing.

The foundation of Big Data architecture is infrastructure. In every Big Data project, having the correct tools for storing, processing, and analyzing your data is critical [11].
