Database NoSQL

Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non-relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.

Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non-relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.

Big data analytics uses efficient analytic techniques to discover hidden patterns, correlations, and other insights from big data. It brings significant cost advantages, enhances the performance of decision making, and creates new products to meet customers’ needs. This method has various applications in plants, bioinformatics, healthcare, etc. It can be improved with various techniques such as machine learning, intelligent tools, and network analysis. This chapter describes applications of big data analytics in biological systems. These applications can be conducted in systems biology by using cloud-based databases (e.g., NoSQL). The chapter explains the improvement of big data technology in plants community with machine learning. Furthermore, it presents various tools to apply big data analytics in bioinformatics systems. Medical signal and genomics are two major fields in healthcare environments that would be improved by this type of analytical method. Finally, the chapter discusses on several use cases of healthcare information system.

Due to the large amount of data generated by user interactions on the Web, some companies are currently innovating in the domain of data management by designing their own systems. Many of them are referred to as NoSQL databases, standing for 'Not only SQL'. With their wide adoption will emerge new needs and data integration will certainly be one of them. In this paper, we adapt a framework encountered for the integration of relational data to a broader context where both NoSQL and relational databases can be integrated. One important extension consists in the efficient answering of queries expressed over these data sources. The highly denormalized aspect of NoSQL databases results in varying performance costs for several possible query translations. Thus a data integration targeting NoSQL databases needs to generate an optimized translation for a given query. Our contributions are to propose (i) an access path based mapping solution that takes benefit of the design choices of each data source, (ii) integrate preferences to handle conflicts between sources and (iii) a query language that bridges the gap between the SQL query expressed by the user and the query language of the data sources. We also present a prototype implementation, where the target schema is represented as a set of relations and which enables the integration of two of the most popular NoSQL database models, namely document and a column family stores. 4

Nowadays, producing streams of data is not helpful if you cannot store them somewhere. Applications, software, and objects generate huge masses of data, which need to be collected, stored, and made available for analysis. Moreover, these data are very valuable and need to be preserved. That is why Big Data has attracted global interest from all leaders of information technology and new ways of storing information have emerged and flourished. Accordingly, while proceeding our analysis on this subject, we note that in terms of Big Data architecture, the storage layer is very useful and is essential for the proper functioning of any Big Data system. In fact, there are two types of storage at this layer: Hadoop distributed file system (HDFS) and NoSQL databases. We relied on previous works in which we identified key storage concepts through comparative studies of main big data distributions. The storage layer is located directly above Data Sources and Data ingestion layers for which we already proposed a meta-model. Thus, in this paper, we applied techniques related to Model Driven Engineering ‘MDE’ to provide a universal Meta-modeling for the storage layer at the level of a Big Data system.

Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non-relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.