Towards Polyglot Data Stores -- Overview and Open Research Questions

TL;DR

This paper reviews the current state of polyglot data stores, analyzing their capabilities, limitations, and open research questions in managing heterogeneous data for diverse workloads.

Contribution

It provides a comprehensive overview of real-world use cases, evaluates existing polyglot data systems, and identifies key challenges and future research directions.

Findings

Polyglot data stores aim to unify multiple database systems for diverse workloads.

Current systems lack adaptability and tight integration in changing conditions.

Identified open research questions for improving polyglot data management.

Abstract

Nowadays, data-intensive applications face the problem of handling heterogeneous data with sometimes mutually exclusive use cases and soft non-functional goals such as consistency and availability. Since no single platform copes everything, various stores (RDBMS, NewSQL, NoSQL) for different workloads and use-cases have been developed. However, since each store is only a specialization, this motivates progress in polyglot data management emerged new systems called Mult- and Polystores. They are trying to access different stores transparently and combine their capabilities to achieve one or multiple given use-cases. This paper describes representative real-world use cases for data-intensive applications (OLTP and OLAP). It derives a set of requirements for polyglot data stores. Subsequently, we discuss the properties of selected Multi- and Polystores and evaluate them based on given needs illustrated by three common application use cases. We classify them into functional features, query processing technique, architecture and adaptivity and reveal a lack of capabilities, especially in changing conditions tightly integration. Finally, we outline the benefits and drawbacks of the surveyed systems and propose future research directions and current challenges in this area.