- Integration of Relational and NoSQL Databases. - The analysis of relational and NoSQL databases leads to the conclusion that these data pro- cessing systems are to some extent complementary. - In the current Big Data applications, es- pecially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. - In terms of performance, it may be bene¯cial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. - NoSQL database. - Big Data. - The environment leads to the often disappearing central DBA's roles and a more decentralized approach with more DBA groups maintaining databases in the enterprise.. - NoSQL databases, a database alternative for storage and processing so-called Big Data today, contribute to this state signi¯cantly.. - However, these tools have never been competitive in the market. - Major vendors of relational DBMSs (RDBMSs), such as Oracle, Microsoft SQL Server and MySQL, include XML support in their products, but native XML databases are not much involved in the database market. - However, this is now already possible with document-oriented NoSQL databases (see the popular JSON a format), though not in such powerful languages as the XQuery in XML environment.. - The situation in the database world today is a®ected by Big Data. - They fundamentally a®ect the storage and processing infrastructure of Big Data. - In other words, Big Data is an evolving term that describes a large volume of structured, semi-structured and unstructured data that have the potential to be mined for information and used in machine learning (ML) projects and other advanced analytics applications. - But DW technology has always been focused on structured data compared to the much richer variability of data types, as it is today usual for Big Data. - In the ICT history, di®erent DBMSs were designed to solve di®erent problems, considering still new and new data types. - appeared in the past. - To store and process Big Data today, we can choose from many options:. - NoSQL databases;. - The aim of this paper is to discuss the relation between SQL databases and NoSQL databases, modeling databases in the SQL and NoSQL polyglot world, in order to support Big Analytics. - We show the duality between SQL databases and NoSQL databases. - Section 4 presents various integrated database architectures composed of both NoSQL databases and a mix of RDBMS and NoSQL. - Analytical Processing of Big Data. - The problems that arise in this context are based on the fact that the requirements for Big Data are often more dynamic than the classic data processing in DWs. - We need to scale both the infrastructure and the standard data processing techniques for Big Data. - Another issue is how to analyze Big Data coming from relational DBs, possibly from databases of both types, i.e. - from relational and NoSQL ones.. - Big Data is often mentioned only in relation to business intelligence (BI). - Data variability is now part of Big Data storage design and analytical system design. - In any case, the main problems of the current DM techniques used for Big Data come from their lack of scalability and parallelization.. - NoSQL Databases. - Large-scale data collections are often used for the storage and processing in NoSQL databases. - NoSQL databases, starting in the late 1990s, provide easier scalability and performance compared to traditional RDBMS. - A more detailed discussion of NoSQL databases and, more generally, their relationships to Big Data issues can be found, for example, in Refs. - Categories of NoSQL databases. - What is the main classical approach to databases a (logical) data model is described in NoSQL databases rather intuitively, without any formal basis. - The most well-known NoSQL databases can be classi¯ed according to the used data model as:. - A column family in di®erent rows can contain di®erent columns. - There are also other approaches in the NoSQL world. - More generally, NoSQL databases include also graph databases 8 and others, e.g. - The answer is no, because NoSQL databases are normally denormalized (save copy of object data. - (3) are basically of the key – value type. - They di®er mainly in the possibilities of aggregating couples (key,value) and accessing these values. - Usability of NoSQL databases. - There is much debate about the role of NoSQL databases in providing information services. - On the other hand, the RDBMS supporters argue that the NoSQL databases have a big disadvantage of failing to provide correct data integrity. - In any case, NoSQL tech- nologies being designed with Big Data needs to be kept in mind.. - The absence of some ACID properties even allows signi¯cant acceleration and decentralization of NoSQL databases.. - NoSQL databases usually have little means of ad-hoc querying and analysis. - NoSQL databases cannot also be recommended for applications requiring enterprise-level functionality (ACID prop- erties, security and other relational technology features). - In any case, NoSQL should not be the only choice in the cloud.. - Experience with the NoSQL databases shows that they can be used even on. - We remind that in the mobile data processing environment, transactions are even more technically impossible in a larger range.. - Among the good properties of NoSQL databases we can ¯nd:. - Table 1 shows a comparison of NoSQL databases and RDBMSs in more detail.. - Comparison of relational and NoSQL DBs.. - In the database world NoSQL databases occupy a signi¯cant place. - In the DB-Engines Ranking, 347 various DB-Engines were tracked in May 2019.. - SQL and NoSQL: Towards Integrated Architecture. - In the work, 9 the authors argue that the NoSQL databases are rather comple- mentary to traditional transactional DBMSs. - For example, customer data is in one table, data about the banks where the account exists is in the second table. - Concerning an integration of distributed data from di®erent databases, two approaches based on a database schema management were at disposal:. - In the context of RDBMSs and NoSQL databases, it is not possible to use simply traditional approaches to data integration. - Now, the tendency is to create multimodel and multi- level modeling approaches involving both relational and NoSQL architectures in- cluding their integration. - We just need a di®erent kind of data modeling than in the past. - An interesting variant occurs in the case when relational database and NoSQL database have to co-exist. - NoSQL database schema. - 14 Then, even a double-sided data migration between an RDBMS and a NoSQL database can be performed.. - Vendors have mixed and matched elements from di®erent NoSQL databases to achieve more generally useful systems. - Obviously, implementations of such approaches are di®erent in general. - A co-existence of RDBMS and a NoSQL database is denoted sometimes as NoSQL-on-RDBMS approach. - The work 18 o®ers an e®ective integration approach of relational DBs and NoSQL data stores, including MySQL, MongoDB and Redis, which allows users to query data from both relational SQL systems and NoSQL systems in a single SQL query.. - Despite the fact that DB schemas are mostly not used in the NoSQL world, some variations on multilevel modeling approaches exist. - In relation to solution of an alternative for data processing with relational and NoSQL data in one infrastructure, the common design methods for such DBs are based on the modi¯cation of the traditional three-level ANSI/SPARC approach. - 19 The approach involves not only heterogeneous data sources but also the development of a database schema in the overall infrastructure, i.e. - Some approaches have been proposed for providing mapping of relational DB schemata and operations onto equivalent ones in NoSQL databases to deal with large relational datasets in the cloud, focusing on scalability and availability.. - 20 present the SQLtoKeyNoSQL, a layer able to translate rela- tional schemata as well as SQL commands to equivalent schemata and access methods to any key-oriented NoSQL databases [see (1. - di®erent separated DBMSs according to the ¯ltering options and migrates them. - Data selection is performed in the source systems using native query languages (SQL and Cypher), and then the results are mapped onto data structures associated with the source query term. - n Its authors call their approach relational NoSQL databases. - Fauna Query Language (FQL) is another example of a functional query language used in the context of NoSQL and relational DB.. - A more advanced integrating architecture including several NoSQL databases is proposed in Ref. - The purpose of this work was to show that it is not possible to use simply traditional approaches for the integration of RDBMSs and NoSQL database. - NoSQL databases are °exible but their data design requires also signi¯cant modeling decisions that impact their performance in inte- grated architectures. - Particularly, these approaches are in°uenced by the fact that Big Data and Big Analytics are considered. - In practice, the key issues for building Big Data processing infrastructure are in decisions concerning NoSQL databases. - This is still a big problem for Big Data analysts.. - developing meaningful and usable formalisms for modeling NoSQL databases and a su±ciently general user-friendly query language;. - modeling multimodel databases including relational and NoSQL ones in one in- frastructure;. - Interpreting a query — especially in the schema's absence — and received answers may be nontrivial.. - Considering these DBs in a cloud environment we can observe, in the last years, attempts to achieve even a cloud integration. - Big Data (IEEE, 2016), pp. - Pokorný, Big Data storage and management: Challenges and opportunities, in ISESS 2017: Environmental Software Systems: Computer Science for Environmental Protection, IFIP Advances in Information and Communication Technology, Vol. - Pokorný, Integration of relational and NoSQL databases, in ACIIDS 2018: Intelligent Information and Database Systems, Lecture Notes in Computer Science, Vol. - Schia±no, Persisting Big-Data:. - Warren, Big Data: Principles and Best Practices of Scalable Real-Time Data Systems, 1st edn. - Pokorný, NoSQL databases: A step to databases scalability in Web environment, Int.. - Abell o , Big Data design, in Proc. - Hugy a k, Uniform data access platform for SQL and NoSQL database systems, Inf. - Jiang and Y.-Ch
Xem thử không khả dụng, vui lòng xem tại trang nguồn hoặc xem
Tóm tắt