Big Data has gained attention due to its significant impact on society. With the growth of IoT, data has grown exponentially. Big Data has played an essential role in decision-making. After collecting a large volume of data, one of the critical tasks is performing analysis. As a result, there is a rise in the adoption of data visualization, where the raw data is converted into useful information through research and the generation of patterns. The representation of the natural numbers offers an easier way to interpret the collected data, and thus, it becomes easier to comprehend the significance of the data collected. While data visualization is significant, it faces numerous challenges; therefore, this paper aims to provide the different challenges of Big Data visualization.

IoT generates a large volume of data in different formats. Exponential data generation has resulted in challenges in capturing, managing, storing, analyzing, and visualizing data. This necessitates the implementation of exemplary database architecture to store data. The paper aims to evaluate the various database architectures.

There is a surge in the usage of IoT platforms, which has increased the number of interconnected devices worldwide. There are different challenges faced with adopting the IoT platform, and this paper will evaluate the storage challenges.

A polystore system is composed of multiple heterogeneous databases which are used to store data that best suites its features for faster processing and querying. A single database does not fit all data formats, which has resulted in the development of a polystore system that leverages databases belonging to different categories such as relational, NoSQL, and NewSQL. Retrieving data from relational databases for analytics is a costly operation. This paper presents an extension to a polystore system which queries data from different relational databases using a standard in-memory data format for analytical queries with low latency.

Big Data analytics is used in decision making. It involves heavy computation to obtain exact answers. To alleviate this problem, approximate query processing (AQP) was adopted, which provides approximate results with error bounds. The AQP models which have been proposed are supported only by a single database. In an organization, big data is stored in multiple databases that have different data models. This research aims to provide AQP as a middleware solution using query optimization for heterogeneous databases.

As the world continues to encounter challenges related to public health and economic well-being, technology and big data can be used to carry out analyses and provide predictions concerning the progression of the COVID-19 pandemic. This information allows virologists and epidemiologists to understand the Coronavirus better. Historically, medical experts relied on hospital records and medical publications to manage pandemics. However, this approach proved to be slow and inefficient when it comes to predicting a disease's course. Using contemporary technologies for collecting and analyzing information is a fast and efficient way.

As the number of databases continues to grow data scientists need to use data from different sources to run machine learning algorithms for analysis. Data science results depend upon the quality of data been extracted. The objective of this research paper is to implement a federated query processing framework which extracts data from different data sources and stores the result datasets in a common in-memory data format. This helps data scientists to perform their analysis and execute machine learning algorithms using different data engines without having to convert the data into their native data format and improve the performance.