Towards Self-Adapting Data Migration in the Context of Schema Evolution in NoSQL Databases

2020 ◽  
Author(s):  
Andrea Hillenbrand ◽  
Uta Storl ◽  
Maksym Levchenko ◽  
Shamil Nabiyev ◽  
Meike Klettke
Keyword(s):  
Data Migration ◽  
Schema Evolution ◽  
Nosql Databases

Remaining in Control of the Impact of Schema Evolution in NoSQL Databases

2021 ◽  
pp. 149-159
Author(s):  
Andrea Hillenbrand ◽  
Stefanie Scherzinger ◽  
Uta Störl
Keyword(s):  
Schema Evolution ◽  
Nosql Databases ◽  
The Impact

scholarly journals Data migration: relational RDBMS to non-relational NoSQL

2021 ◽  
Author(s):  
Feroz Alam
Keyword(s):  
Relational Databases ◽  
Data Migration ◽  
Business Decision ◽  
Nosql Databases ◽  
Dynamic Provisioning ◽  
Object Relational ◽  
Significant Performance ◽  
Query Structure ◽  
Day By Day ◽  
Migration Technique

As a part of achieving specific targets, business decision making involves processing and analyzing large volumes of data that leads to growing enterprise databases day by day. Considering the size and complexity of the databases used in today’s enterprises, it is a major challenge for enterprises to re-engineering their applications that can handle large amounts of data. Compared to traditional relational databases, non-relational NoSQL databases are better suited for dynamic provisioning, horizontal scaling, significant performance, distributed architecture and developer agility benefits. Based on the concept of Object Relational Mapping (ORM) and traditional ETL data migration technique this thesis proposes a methodology for migrating data from RDBMS to NoSQL. The performance of the proposed solution is evaluated through a comparative analysis of RDBMS and NoSQL implementations based on query performance evaluation, query structure and developmental agility.

scholarly journals An implementation of a fault-tolerant database system using the actor model

2021 ◽  
Vol 342 ◽  
pp. 05001
Author(s):  
Ioan Cristian Schuszter ◽  
Marius Cioca
Keyword(s):  
Performance Metrics ◽  
Fault Tolerant ◽  
Database System ◽  
Data Migration ◽  
Actor Model ◽  
Nosql Databases ◽  
Design And Implementation ◽  
Fault Tolerant Systems ◽  
Distributed Infrastructure

Fault-tolerant systems are an important discussion subject in our world of interconnected devices. One of the major failure points of every distributed infrastructure is the database. A data migration or an overload of one of the servers could lead to a cascade of failures and service downtime for the users. NoSQL databases sacrifice some of the consistency provided by traditional SQL databases while privileging availability and partition tolerance. This paper presents the design and implementation of a distributed in-memory database that is based on the actor model. The benefits of the actor model and development using functional languages are detailed, and suitable performance metrics are presented. A case study is also performed, showcasing the system’s capacity to quickly recover from the loss of one of its machines and maintain functionality.

scholarly journals Automated Algorithm for Data Migration from Relational to NoSQL Databases

2018 ◽  
Vol 21 (1) ◽  
pp. 60
Author(s):  
Alza A. Mahmood
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Database Management System ◽  
Data Migration ◽  
Management Systems ◽  
Structure Form ◽  
Nosql Databases ◽  
New Approach ◽  
New Class ◽  
Automated Algorithm

   One of the barriers that the developer community face once turning to the newly, highly distributable, schema agnostic and non-relational database, called NoSQL, which is how to migrate their legacy relational database (which is already filled with a large amount of data) into this new class of database management systems. This paper presents a new approach for converting the already filled relational database of any database management system to any type of NoSQL databases in the most optimized data structure form without bothering of specifying the schema of tables and relations between them. In addition, a simplified software as a prototype based on this algorithm is built to show the results of the output for testing the validity of the algorithm.

NoSQL schema evolution and big data migration at scale

2016 ◽  
Author(s):  
Meike Klettke ◽  
Uta Storl ◽  
Manuel Shenavai ◽  
Stefanie Scherzinger
Keyword(s):  
Big Data ◽  
Data Migration ◽  
Schema Evolution

A Method of Ease Schema Evolution

2011 ◽  
pp. 69-77
Author(s):  
Lex Wedemeijer
Keyword(s):  
Data Structure ◽  
Current Data ◽  
Data Migration ◽  
Schema Evolution ◽  
Functional Requirements ◽  
Conceptual Schema ◽  
Implementation Phase ◽  
Additional Requirement ◽  
Design Alternatives ◽  
The Impact

Maintenance on the Conceptual Schema of a database is necessary when the current data structure can’t meet changed functional requirements. An additional requirement, not expressed in the demand for change, is to minimize the impact of change. The problem of minimizing impact of change on the data is often postponed to the implementation phase when data have to be migrated into the new structure. We propose a method to address the problem of Conceptual Schema evolution in an earlier phase, and introduce the notion of Majorant Schema to support the application of the method. The advantage of the approach is that a more graceful schema evolution is ensured because a broader range of design alternatives is investigated. Other benefits are the early attention for the impact of change, and a better preparation of the data migration effort. Experiences show that the method is primarily suited to minor changes.

scholarly journals Integration of Relational and NoSQL Databases

2019 ◽  
Vol 06 (04) ◽  
pp. 389-405 ◽  
Author(s):  
Jaroslav Pokorný
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Multilevel Modeling ◽  
Data Model ◽  
Data Migration ◽  
Nosql Databases ◽  
New Methods ◽  
Big Data Applications ◽  
Nosql Database ◽  
Sql Database

The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially 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. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial 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. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.

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