Data Management System created to bridge the gap between Data Science and Databases


Relational databases find use among data scientists. This is because data stored is in such a manner that relations between stored data or stored information is preserved. Nonetheless, there is gap between data scientists and relational database research community. Resultantly, this leads to inefficient databases in data science. This led a research scholar to bridge the gap between data science and relational data bases.

As a practice, most data scientists employanalytical tools such as Python, R, and C/C++ for their research. However, due to difficulties to integrate these tools with current database systems, this results in slow and cumbersome data analysis.

“To address this, database scientists have chosen to reinvent database systems by developing a gamut of data management alternatives. The new database systems perform tasks similar to classical database management systems, nonetheless,display several problems that were resolved decades ago in the database field.

Development of Powerful Database Engines accomplishment of Research Community

Meanwhile, the database research community has made notable strides in the development of powerful database engines that allow efficient processing of analytical query. To accomplish this, the research scholar tried to combine innovations in database science with analytical tools that are mostly used by data scientists. The investigation involves means to gauge how to promoteefficient and painless integration of analytical tools with relational database management systems.

Also, in computer science, use of standard database system puts forth issues with the size of data that is handled.

The investigation is focused on three primary methods for database client integration: in-database processing, embedding of database inside the client application, and client- server connections. For each method, the scholar studied the implementation of powerful database engines in existing database systems. The database engines were further evaluated for their usability of large datasets and workloads common to data science.

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