The Data Lake is a repository that exhibits great scalability and has the capability to store both structured and unstructured data. It presents a potentially effective resolution to the modern challenge of storing large volumes of data, sometimes referred to as Big Data. Nevertheless, it is important to acknowledge that this system does have some limitations, such as inadequate security measures and deficiencies in access control. This paper presents a comprehensive analysis of several business Data Lake solutions currently available on the market. Apache Hadoop is acknowledged as a prevailing standard in the realm of data lakes. The parallel processing frameworks of this system provide efficient and rapid processing of substantial volumes of data. The primary benefits of the data lake environment are the use of affordable hardware, the adoption of open-source technologies including cost-free software, and the capacity to scale elastically. This study will explain the potential use of a data lake in conjunction with a data warehouse. The objective of this study is to propose a potential data lake architecture for the banking industry model inside a specific multinational banking organization. These systems include Amazon Web Services (AWS) Data Lake and Azure Data Lake. AWS Data Lakes provides a streamlined solution accompanied by robust safeguards to mitigate the risk of data loss, while Azure Data Lakes emphasizes its superior scalability and high-level security measures tailored for business use. Data Lake solutions are seeing a surge in popularity among several sectors, including Banking & finance, manufacturing, and healthcare. Furthermore, it assumes a prominent function within the context of Industry 4.0.

Data Lake Deep Lake Amazon S3 Deep Learning Azure Data governance Data Security Banking