# A Survey of Big Data Archives in Time-Domain Astronomy
## 7 Challenge of the Future Management in Astronomical Data Archives  
The current database management trends require the use of many models and data repositories. Previous models such as federated database systems (FDBS) and data warehouses function well with relational data but are incapable of storing a large variety of data types (arrays, graphs, and images). Different data stores that manage various types of data have their own local languages.  
Figure 3: BigDAWG Components [63].  
Figure 2: BigDAWG Architecture [63].  
Cooperative but autonomous databases comprise the FDBS [66]. With FDBS, local databases with decentralized control can gain more control over the information that they can exchange. Federated query agents (FQA) are used by FDBS to process queries. To store and execute queries, these agents can serve as intermediaries (mediators) between them. FDBS data are stored in a relational database, which is a single data model supported by the system [67].  
A data warehouse is a relational database built for analysis rather than for transaction processing. It normally comprises historical data collected from transactions, but can also contain data from other sources. It allows a company to combine data from multiple sources while separating the analytical and transaction workloads. Other programs that manage the process of obtaining data and distributing it to business users can be found in a data warehouse environment. A central data store or warehouse controls the data warehouses [68].  
Due to the rise of big data, models such as FDBS and Data Warehouse seem to be inefficient as they can integrate only databases with a single data model that is no longer relevant. In addition, the volume and velocity of the data growth cannot be accommodated. These models also fall short in terms of their cost and performance. It is now safe to conclude that the numerous methods provided by earlier data integration models for managing heterogeneous data have failed. The database community has become increasingly interested in managing huge amounts of unstructured data from numerous heterogeneous data repositories. Due to the development in data size, rate of data incre-mentation, and appearance of new data types in various scientific data archives, this issue has received increased attention. The \"one size fits all\" approach [69] is no longer appropriate for modern database engineering. The underlying DBMS must also have complete autonomy to optimize queries. A model that can span heterogeneous data sources using a unified query language is required. Data virtualization via mediation is essential for meeting these needs.  
Polystore can span many data management systems without requiring an underlying data location or storage engines, and it can be queried using a single language [63]. Polystore facilitate many-to-many interactions between information islands and data management systems across numerous distributed data models and query languages [65]. Polystores also provide seamless access to cloud data stores. The CloudMdsQL Polystore provides a functional SQL-like query language to access many data sources (relational, NoSQL, and HDFS) [70].  
Polystore systems were recently proposed as a novel data integration approach that provides integrated access to heterogeneous data stores via a unified single query language. Moreover, polystore systems eliminate heterogeneity issues by implementing a communication protocol within the underlying database management systems via islands/shims, mediation, or APIs. Different types of data, such as text, graph, image, log data, etc., require different user interfaces. However, polystore helps integrate all these data into a single query interface so that multiple models can be uniformly spanned.