**1. Introduction**

Massive amounts of data are produced in the Internet of Things (IoT) age from a number of heterogeneous sources, such as mobile devices, sensors, and social media. Among the most revolutionary technologies are big data analytics, artificial intelligence (AI) and robotics, machine learning (ML), cybersecurity, blockchain

**Figure 1.** *Big data characteristics.*

technology, and cloud computing. The two basic features of machine learning are the automatic analysis of large data sets and the creation of models for the broad relationships between data (ML). Analyzing large amounts of data to find information—such as hidden patterns, correlations, market trends, and customer preferences—that can assist organizations in making strategic business decisions is known as big data analytics [1, 2]. Volume, value, variety, velocity, and veracity are the five characteristics that define Big Data, as shown in **Figure 1**.

Legumes, shea butter, groundnuts, and soybeans are significant crops in Sub-Saharan Africa (SSA) because they offer a range of advantages in terms of the economy, society, and the environment. Sub-Saharan Africa contributes a relatively little amount to global agricultural output despite having over 13% of the world's population and about 20% of its land area being used for agriculture, claims the [3]. The research paper is purposed to develop a Big Data Analytics Architecture Framework for the Production and International Trade of Oilseeds and Textiles in Sub-Saharan Africa (SSA).

## **1.1 Background**

More over 950 million people live in Sub-Saharan Africa (SSA), accounting for roughly 13% of the global population. Oilseed production in SSA is expected to increase by 2.3 percent per year to 11 Mt. by 2025, accounting for barely 2% of global production.

Although expected increase in Southern Africa is more modest at 16 percent, the base is significantly greater, and Southern Africa accounts for the largest proportion of additional protein meal use in absolute volumes. Southern (1.4 percent per year) and Eastern Africa (1.2 percent per year) are expected to grow at the quickest rates to 2025. Protein meal use is increasing across most of SSA as livestock industries strengthen in the future years, with Western Africa (43 percent) and Eastern Africa (43 percent) seeing the largest rise (32 percent). Oilseed production in SSA is expected to increase by 2.3 percent per year to 11 Mt. by 2025, accounting for barely 2% of global production. Nonetheless, total imports into SSA are expected to grow at a


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

### *Ubiquitous and Pervasive Computing - New Trends and Opportunities*


#### **Table 1.**

*Major oilseeds world supply and distribution (2017–2022) [million metric tons].*

#### **Figure 2.**

*World production of oilseeds (2017–2022).*

3.7 percent annual rate, with Nigeria (4 percent per year), Sudan (5 percent per year), Ethiopia (6 percent per year), and Kenya (3 percent per year) accounting for the majority. Per capita consumption has grown at a rate of 2.1 percent per year, making it one of the fastest growing commodities in the region during the last decade. Over the next decade, Sub-Saharan Africa's net food imports are expected to rise, however productivity-boosting investments could counteract this trend. Despite the fact that agricultural productivity has increased significantly over the last decade, SSA remains the world's most food insecure region, with inconsistent progress toward hunger eradication. The world oilseeds supply and distribution in million metric tons for the period 2017 to 2022 is shown on **Table 1**.

The world production of oilseeds for the period 2017–2022 is shown on **Figure 2**.

The world oilseeds crush distribution for the period 2017–2022 is shown on **Figure 3**.

The focus of the researchers was on how to use and implement Big Data to improve production for both oilseeds and textile production and international trade for Sub-Saharan Africa (SSA).

The top 15 textile exporters in Sub-Saharan Africa (SSA) are shown on **Table 2** below and illustrated on **Figure 4**.

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

**Figure 3.** *World oilseeds crust distribution (2017–2022).*


#### **Table 2.**

*Top 15 SSA exporters of textiles and clothing to US (US\$'000).*

Many textile and apparel inputs now produced in SSA nations can be made more competitive by new or increased investment or other methods, especially as output of these inputs is restricted and diminishing in many cases. New or expanded investment, as well as other initiatives, could help the industry maintain or expand present

#### **Figure 4.**

*The top 15 SSA exporters of textiles and clothing to US (US\$'000).*

production and export levels of these inputs, as well as extend the possibility for new product development.

This paper aims to develop a Big Data Architecture framework for oilseeds and textile industry production and international trade for SSA.

#### **1.2 Statement of the problem**

Organizations struggle to manage and track the growth of both new and old opensource big-data solutions, which are continually expanding. The considerable volume of data produced by a wide range of sources, including as information services, Internet of Things (IoT) devices, social media, and mobile devices, is not only too large but also moves too quickly and is too complex to be handled and stored by conventional techniques. The sector is driven by the data's exponential growth, which also draws researchers to create new models and scalable methods for handling big data. A well-known open-source framework for big-data analytics, Apache Hadoop is made to integrate with a number of other open-source technologies to allow for the storing and processing of large amounts of data using commodity hardware clusters. A distributed file system, cluster administration, storage, distributed processing, programming, data analysis, data governance, and data pre-processing tools are all included in the Hadoop Stack. The production and global commerce of oilseeds and the textile industries should take this into account.
