SQL and NoSQL Databases in the Context of Industry 4.0

Abstract

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.

Cassandra is a Better Option for Handling Big Data in a No-SQL Database

“…Without a single point of failure, it offers highly available service [8]. Cassandra [9] is a column-oriented database, among other things. It is scalable, fault-tolerant, and very consistent.…”

“…Cassandra will provide the client with the most recent value if it is determined that some of the nodes provided outdated values in their responses. Cassandra updates the outdated values in the background after returning the most recent value [9][10] [11]. In order to prevent a single point of failure, Cassandra uses data replication among cluster nodes, as the schematic perspective.To enable nodes to speak with one another and find any defective nodes in the cluster, Cassandra uses the Gossip Protocol in the background.Query Language for Cassandra Cassandra will be accessed by users via its nodes being abused.…”

MogoDB: A NoSQL Database with Amazing Advantages and Features

“…Here, each row is made up of many columns (fields). One of the well-known column-family databases is Cassandra, and HBase and Cassendra are also significant column value databases [9][10] [15]. Data is logically structured into tables, rows, and columns in HBase, a distributed column-oriented data store built on top of HDFS.…”

“…Without a single point of failure, it offers highly available service [8]. Cassandra [9] has the following characteristics: • It is a column-oriented database. • It is scalable, fault-tolerant, and highly consistent.…”

“…Nodes are arranged depending on these relationships, and edges indicate the directionality. With this configuration, an intriguing analytics approach that focuses on keywords and topic mining and information retrieval from document-based NoSQL enables knowledge discovery [9]. The most well-known open-source graph database, Neo4j, was created utilizing Java technology.…”