Big data analytics refers to the application of sophisticated analytical methods to extremely extensive and heterogeneous datasets encompassing structured, semi-structured, and unstructured information. These datasets originate from various sources and range in size from terabytes to zettabytes. With the purpose of facilitating data-driven decision making, big data analytics entails the identification of correlations, trends, and patterns in vast quantities of unprocessed data. These procedures employ well-known statistical analysis methods, such as regression and clustering, and employ more sophisticated instruments to implement them on larger datasets. Since software and hardware advancements enabled organisations to manage vast quantities of unstructured data in the early 2000s, big data has been a popular term. Subsequently, the proliferation of emerging technologies, such as smartphones and Amazon, has further augmented the considerable volumes of data accessible to organisations. For the storage and processing of big data, early innovation initiatives such as Hadoop, Spark, and NoSQL databases were developed in response to the data deluge. Data engineers are constantly inventing new methods to process and integrate the massive volumes of complicated data generated by many sources, such as the internet, smart devices, transactions, networks, and sensors. Presently, emergent technologies such as machine learning are being integrated with big data analytics methods in order to uncover and escalate the magnitude of more intricate insights.