Enterprises’ corporate Big Data Analytics Management via Amazon Web Services Data Lakes

In the new word economy scenario, data has become the most valuable asset and the main driver after people for businesses. Facing the digital transformation, enterprises need to leverage humongous amount of heterogeneous data, known as Big Data, trying to profit from its hidden meanings and correlations. For this purpose, data from different sources such as databases, frameworks, sensors and social media must be stored in the most efficient way. In the past and some organizations currently, data warehouses are used to store Big Data. One of the major limitations of data warehouses lies in their data ingestion philosophy, based on an on-write schema; this means that data is organized and defined, and metadata is applied before the data is written and stored. This approach is evidently inflexible in front of the massive variety and unstructured nature of Big Data. For this reason, researchers are working on a new data repository system, and the Data lakes, or Data meshes, recently emerged as an alternate solution for storing data of widely divergent types and scales, thus providing more flexibility. The aim of this research is therefore to present the structure of Data lakes, to then assessing in a real case study how they can provide more powerful and profitable data analytics to enterprises.