A Comparative study of SQL Databases and NoSQL Databases for E-Commerce

With the advent of E-Commerce, businesses persistently examine various ways to improvise and accomplish their demands with web engineering that provide notable resolution. The progress in economic status demands colossal databases that store the data efficiently. The databases currently used are relational or non-relational. Both these types have their benefits and limitations that influence the overall processing of data. Non-relational databases are referred to as NoSQL-not only SQL, and Relational databases are known as SQL-Structured Query Language. It has been suggested in many studies that NoSQL databases surpass SQL databases. Our paper aims to evaluate these claims by analyzing the CRUD [Create, Read, Update, Delete] operations executed by both database types. Keywords: NoSQL, SQL, Non-relational Databases, MySQL, E-Commerce, MongoDb , Relational Databases

SQL and NoSQL Databases in the Context of Industry 4.0

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that big data and analytics is considered a technological pillar of this process. The goal of I4.0 is the implementation of the so-called Smart Factory, characterized by Intelligent Manufacturing Systems (IMS) that overcome traditional manufacturing systems in terms of efficiency, flexibility, level of integration, digitalization, and intelligence. The literature reports a series of system architecture proposals for IMS, which are primarily data driven. Many of these proposals treat data storage solutions as mere entities that support the architecture’s functionalities. However, choosing which logical data model to use can significantly affect the performance of the IMS. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, considering the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of big data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of relational and NoSQL databases for different scenarios within I4.0.

An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution

In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as non-relational DBMSs; this comparison is based on a detailed analysis of CRUD operations for different amounts of data showing how the databases could be modeled and used in an application. A case-study application was developed for this purpose in Java programming language and Spring framework using for data storage both relational MySQL and non-relational Elasticsearch and MySQL Document Store. To model the real situation encountered in several developed applications that use both relational and non-relational databases, a data replication solution that imports data from the primary relational MySQL database into Elasticsearch and MySQL Document Store as possible alternatives for more efficient data search was proposed and implemented.

An Approach for Schema Extraction of NoSQL Columnar Databases: the HBase Case Study

Although NoSQL databases do not require a schema a priori, being aware of the database schema is essential for activities like data integration, data validation, or data interoperability. This paper presents a process for the extraction of columnar NoSQL database schemas. We adopt JSON as a canonical format for data representation, and we validate the proposed process through a prototype tool that is able to extract schemas from the HBase columnar NoSQL database system. HBase was chosen as a case study because it is one of the most popular columnar NoSQL solutions. When compared to related work, we innovate by proposing a simple solution for the inference of column data types for columnar NoSQL databases that store only byte arrays as column values, and a resulting schema that follows the JSON Schema format.

Referential Integrity in Cloud NoSQL Databases

<p>Cloud computing delivers on-demand access to essential computing services providing benefits such as reduced maintenance, lower costs, global access, and others. One of its important and prominent services is Database as a Service (DaaS) which includes cloud Database Management Systems (DBMSs). Cloud DBMSs commonly adopt the key-value data model and are called Not only SQL (NoSQL) DBMSs. These provide cloud suitable features like scalability, flexibility and robustness, but in order to provide these, features such as referential integrity are often sacrificed. In such cases, referential integrity is left to be dealt with by the applications instead of being handled by the cloud DBMSs. Thus, applications are required to either deal with inconsistency in the data (e.g. dangling references) or to incorporate the necessary logic to ensure that referential integrity is maintained. This thesis presents an Application Programming Interface (API) that serves as a middle layer between the applications and the cloud DBMS in order to maintain referential integrity. The API provides the necessary Create, Read, Update and Delete (CRUD) operations to be performed on the DBMS while ensuring that the referential integrity constraints are satisfied. These constraints are represented as metadata and four different approaches are provided to store it. Furthermore, the performance of these approaches is measured with different referential integrity constraints and evaluated upon a set of experiments in Apache Cassandra, a prominent cloud NoSQL DBMS. The results showed significant differences between the approaches in terms of performance. However, the final word on which one is better depends on the application demands as each approach presents different trade-offs.</p>

Referential Integrity in Cloud NoSQL Databases

<p>Cloud computing delivers on-demand access to essential computing services providing benefits such as reduced maintenance, lower costs, global access, and others. One of its important and prominent services is Database as a Service (DaaS) which includes cloud Database Management Systems (DBMSs). Cloud DBMSs commonly adopt the key-value data model and are called Not only SQL (NoSQL) DBMSs. These provide cloud suitable features like scalability, flexibility and robustness, but in order to provide these, features such as referential integrity are often sacrificed. In such cases, referential integrity is left to be dealt with by the applications instead of being handled by the cloud DBMSs. Thus, applications are required to either deal with inconsistency in the data (e.g. dangling references) or to incorporate the necessary logic to ensure that referential integrity is maintained. This thesis presents an Application Programming Interface (API) that serves as a middle layer between the applications and the cloud DBMS in order to maintain referential integrity. The API provides the necessary Create, Read, Update and Delete (CRUD) operations to be performed on the DBMS while ensuring that the referential integrity constraints are satisfied. These constraints are represented as metadata and four different approaches are provided to store it. Furthermore, the performance of these approaches is measured with different referential integrity constraints and evaluated upon a set of experiments in Apache Cassandra, a prominent cloud NoSQL DBMS. The results showed significant differences between the approaches in terms of performance. However, the final word on which one is better depends on the application demands as each approach presents different trade-offs.</p>

SQL and NoSQL Databases in the Context of Industry 4.0

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that Big Data &amp; Analytics is considered a technological pillar of this process. The literature reports a series of system architecture proposals that seek to implement the so-called Smart Factory, which is primarily data-driven. Many of these proposals treat data storage solutions as mere entities that support the architecture's functionalities. However, choosing which logical data model to use can significantly affect the performance of the architecture. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, taking into account the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of Big Data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of SQL and NoSQL databases for different scenarios within I4.0.

Research consistency and perfomance of nosql replicated databases

This paper evaluates performance of distributed fault-tolerant computer systems and replicated NoSQL databases and studies the impact of data consistency on performance and throughput on the example of a three-replicated Cassandra cluster. The paper presents results of heavy-load testing (benchmarking) of Cassandra cluster’s read and write performance which replicas were deployed on Amazon EC2 cloud. The presented quantitative results show how different consistency settings affect the performance of a Cassandra cluster under different workloads considering two deployment scenarios: when all cluster replicas are located in the sane data center, and when they are geographically distributed across different data centers (i.e. Amazon availability zones). We propose a new method of minimizing Cassandra response time while ensuring strong data consistency which is based on optimization of consistency settings depending on the current workload and the proportion between read and write operations.