Architecture of Data Lake

Abstract

Data can be traced from various consumer sources. Managing data is one of the most serious challenges faced by organizations today. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics. A data lake could be a merging point of new and historic data, thereby drawing correlations across all data using advanced analytics. A data lake can support the self-service data practices. This can tap undiscovered business value from various new as well as existing data sources. My paper will present the overview of data lake, benefits and it’s architecture along with the opportunities laid down by data lake and advanced analytics, as well as, the challenges in integrating, mining and analyzing the data collected from these sources. It goes over the important characteristics of the data lake architecture and Data and Analytics as a Service (DAaaS) model.