A possible trajectory for generating overall architecture of federated query for data repository

Abstract

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 possible trajectory for generating over all architecture of federated query for data repository which extracts data from different data sources and stores the result datasets in a common in-memory data format. It includes the activities of data source exploration, data acquisition, data preparation and result exploration processes. The outlines of such a trajectory-based model and how it can be used to categories data science projects (goal-directed, exploratory or data management) is suggest in this paper. 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.