Evaluation of Hadoop/Mapreduce Framework Migration Tools

In distributed systems, database migration is not an easy task. Companies will encounter challenges moving data including legacy data to the big data platform. This paper reviews some tools for migrating from traditional databases to the big data platform and thus suggests a model, based on the review

Data Mapping for XBRL: A Systematic Literature Review

It is evident the growth of the use of eXtensible Business Reporting Language (XBRL) technology in the context of financial reports on the Internet, either for its advantages and benefits or by government impositions, however, the data to be transported by this language are mostly stored in structures defined as database, some relational other NoSQL. The need to integrate XBRL technology with other data storage technologies has been growing continuously, and research is needed to seek a solution for mapping data between these environments. The possible difficulties in integrating XBRL with other technologies, relational database or NoSQL, CSV files, JSON, need to be mapped and overcome. Generating XBRL documents from the database can be costly, since there is no native alternative that the database manager system exports from the database manager system, the data in XBRL. For this, specific third-party systems are needed to generate XBRL documents. Generally, these systems are proprietary and have a high cost. Integrate these different technologies adds complexity, since these documents do not connect to the database manager system. These difficulties cause performance and storage problems and in cases of large data, such as data delivery to government agencies, complexity increases. Thus, it is essential to study techniques and methods that allow us to infer a solution to perform this integration and/or mapping, preferably in a generic way, that includes the XBRL data structure and the main data models currently used, i.e.  Relational DBMS, NoSQL, JSON or CSV files. It is expected, in this work, through a systematic literature review, to identify the state of the art concerning the mapping of XBRL data

Ontology-Based Resolution of Cloud Data Lock-in Problem

Cloud computing is nowadays becoming a popular paradigm for the provision of computing infrastructure that enables organizations to achieve financial savings. On the other hand, there are some known obstacles, among which vendor lock-in stands out. Furthermore, due to missing standards and heterogeneities of cloud storage systems, the migration of data to alternative cloud providers is expensive and time-consuming. We propose an approach based on Semantic Web services and AI planning to tackle cloud vendor data lock-in problem. To complete the mentioned task, data structures and data type mapping rules between different types of cloud storage systems are defined. The migration of data among different providers of platform as a service is presented in order to prove the practical applicability of the proposed approach. Additionally, this concept was also applied to software as a service model of cloud computing to perform one-shot data migration from Zoho CRM to Salesforce CRM

DATA MIGRATION FROM STANDARD SQL TO NoSQL

Currently two major database management systems are in use for dealing with data, the Relational Database Management System (RDBMS) also knows as standard SQL databases and the NoSQL databases. The RDBMS databases deal with structured data and the NoSQL databases with unstructured or semi-structured data. The RDBMS databases have been popular for many years but the NoSQL type is gaining popularity with the introduction of the internet and social media. Data flow from SQL to NoSQL or vice versa is very much possible in the near future due to the growing popularity of the NoSQL databases. The goal of this thesis is to analyze the data structures of the RDBMS and the NoSQL databases and to suggest a Graphical User Interface (GUI) tool that migrates the data from SQL to NoSQL databases. The relational databases have been in use and have dominated the industry for many years. In contrast, the NoSQL databases were introduced with the increased usage of the internet, social media, and cloud computing. The traditional relational databases guarantee data integrity whereas high availability and scalability are the main advantages of the NoSQL databases. This thesis presents a comparison of these two technologies. It compares the data structure and data storing techniques of the two technologies. The SQL databases store data differently as compared to the NoSQL databases due to their specific demands. The data stored in the relational databases is highly structured and normalized in most environments whereas the data in the NoSQL databases are mostly unstructured. This difference of the data structure helps in meeting the specific demands of these two systems. The NoSQL DBs are scalable with high availability due to the simpler data model but does not guarantee data consistency at all times. On the other hand the RDBMS systems are not easily scalable and available at the same time due to the complex data model but guarantees data consistency. This thesis uses CouchDB and MySQL to represent the NoSQL and standard SQL databases respectively. The aim of the iii research in this document is to suggest a methodology for data migration from the RDBMS databases to the document-based NoSQL databases. Data migration between the RDBMS and the NoSQL systems is anticipated because both systems are currently in use by many industry leaders. This thesis presents a Graphical User Interface as a starting point that enables the data migration from the RDBMS to the NoSQL databases. MySQL and CouchDB are used as the test databases for the relational and NoSQL systems respectively. This thesis presents an architecture and methodology to achieve this objective

New Perspectives for NoSQL Database Design: A Systematic Review

The use of NoSQL databases has increasingly become a trend in software development, mainly due to the expansion of Web 2.0 systems. However, there is not yet a standard to be used for the design of this type of database even with the growing number of studies related to this subject. This paper presents a systematic review looking for new trends regarding strategies used in this context. The result of this process demonstrates that there are still few methodologies for the NoSQL database design and there are no design methodologies capable of working with polyglot persistence

Extending a methodology for migration of the database layer to the cloud considering relational database schema migration to NoSQL

The advances in Cloud computing and in modern Web applications have raised the need for highly available and scalable distributed databases to accommodate the big data being created and consumed. Along with the explosion in data growth comes the necessity to rapidly evolve databases and schemas to meet user demands for new functionality. A special attention is being paid to the vast amounts of semi-structured and un-structured data, and the data management tools should reflect the support for these needs. This has lead to the development of new Cloud serving systems such as "Not Only" SQL (NoSQL) databases. NoSQL databases were driven by the scalability needs of the big companies, such as Google, Facebook, Amazon, and Yahoo. While the demands of these key players are different from those of small and medium enterprises in terms of scalability, the core problem is the same - storage arrays are not scalable and force you into expensive, forklift upgrades. These facts combined with changes in how IT resources are delivered and consumed through the Cloud computing paradigm, projects adopting NoSQL solutions are not a hype anymore. NoSQL databases are being offered as a service by the big Cloud providers, such as Google, Amazon, Microsoft, but by smaller vendors as well. In this master thesis we investigate the possibilities and limitations of mapping relational database schemas to NoSQL schemas when migrating the database layer to the Cloud. Based on literature research we provide recommendations and guidelines with regard to schema transformation and discuss the implications at other application architecture layers, such as business logic and data access layer. We extend an existing data migration tool and methodology for incorporating the migration guidelines and hints. Moreover, we validate our work based on a chosen sub-set of relational and NoSQL databases by using example data from the established TPC-H benchmark