  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.
