Companies in the manufacturing sector continue to embark on projects involving big data. The use of big data and analytics is a promising technological leap in optimizing the manufacturing process and increasing the competitive advantage. Manufacturers generate data across the supply chain, and this data was not traditionally analyzed to determine useful insights into how they relate from different sources. The ability to integrate this information brought on by analytics and big data can be crucial in realizing benefits such as forecasting, quality control, targeted product design, distribution, and being able to identify bottlenecks in the production system. When manufacturing companies are undertaking the transition to big data using analytics, they have to choose the type of database to implement. This determination typically swings between using NoSQL and using SQL. In the world of database technology, two primary forms of databases exist. These include SQL based and NoSQL. These otherwise represent the traditional relational database and the non-relational database systems. The SQL platform has been used for a long time hence has a long track record. However, NoSQL has been gaining traction in the last few years, with the platform having many proponents. These differences between two architectures stem from how they are built, the kind of data they store, and how they save this data.
Big data is made up of complex, extensive, diverse, distributed information that may be structured or unstructured. Big data is acquired from various sources, including internet transactions, sensors, videos, clickstreams, email, and instruments. In manufacturing, the big data can be obtained from assembly line sensor data, tracking the movement of shipping trucks, cameras on the production floor, and consumer devices. The automation of the manufacturing process would allow for more data to be recorded from the various machines and the sales records. The tools can be embedded with different sensors, hence providing the business with sufficient data to implement big data analytics techniques.
Datasets types and quality
SQL databases are relational databases. These types of databases are table based where there is a series of columns and rows. The tables are related together using a schema, which is the view of the database administrator. The NoSQL architecture implements a database structure that is distributed in nature. The database can be based on documents, key-value pairs, wide columns, or graphs. NoSQL does not have any standard schema. In manufacturing, the NoSQL architecture would be better since most of the data is unstructured; hence, it cannot effectively work on rigid architectures such as SQL.
The volume of enterprise data has been dramatically increasing over the past few years. These vast datasets are what we refer to as Big Data. Various uses of these data have also emerged, with numerous engineered applications based on big data. Handling this information requires advanced and specialized technology that cannot be met using traditional Relational Database Systems such as SQL. This has ensured the rise in uptake of using NoSQL database systems as a means to counter the problems above. We will discuss the characteristics of the data stored in these Database Systems to understand their differences better.
NoSQL databases feature flexible data structures. It means that one does not need to explicitly define a structure to be used as a data schema. Traditional SQL based DBMSs require that one clearly defines the schemas beforehand, which will be used subsequently. While this has functioned very well in SQL's high performing tasks, trying to redefine these structures or schemas carries with it a very high cost defeating the entire purpose of creating or changing the schemas in the first place. NoSQL DBMS requires no schemas meaning that the users can store data with differing structures, all in the same database table.
Secondly, an offshoot based on the previously mentioned variance is that NoSQL databases do not support advanced, high-level query languages such as SQL. SQL's homogenous nature of data makes it possible to write complex queries to gain information from seemingly separate database tables. On the other hand, NoSQL, with its heterogeneous data nature, does not provide such high-level questions. When considering the manufacturing environment, the data is obtained across the supply chain from production to distribution. Data from sensors is used to offer predictive maintenance, and this is unstructured hence the need for NoSQL databases.
Organization readiness and affordability
It is of utmost importance for an organization to carefully choose the correct database system to avoid incurring high costs when trying to change to the next best system. In this section, we will discuss the effect on an organization of choosing one or the other.
The affordability of both database systems is a critical factor in trying to select what system to use. Both SQL and NoSQL systems offer both free systems and premium systems. One can create incredible database systems using only SQLite, an entry-level, open-source RDBMS, or even while using MySQL. Many systems and frameworks usually offer off the box support for these RDBMS, making them very useful if the organization is on a tight budget. NoSQL systems, too, being open-source in nature, also offer the same advantages. The line of separation to be drawn, therefore, becomes dictated by the nature of the data that either company will use.
SQL databases are also portable. They quickly run and integrate into PCs, servers, laptops, mainframes, and even - for the enthusiasts - on mobile devices. NoSQL systems' portability is in the guise of cloud computing. NoSQL systems integrate quite well with cloud systems making them the go-to methods when one works considerably using cloud computing frameworks.
SQL systems, having been in the market for quite some time, offer the best organization readiness. Because of their prolonged use, many developers already know how to use them. They have also become integrated into the university and college syllabuses in many countries meaning graduates already have an inkling of what it means. NoSQL, being relatively new, means that not many people in the industry know it. While this number is quickly dwindling, SQL still offers the best regarding organizational readiness.
These points, I have tried to relate SQL and NoSQL systems, drawing out the differences while still highlighting some of their similarities.
Transactions processing requirements
For the SQL database, all transactions must fulfill four fundamentals of operations. These are referred to as ACID. The relational database must also schedule transactions when executing multiple transactions at the same time. The concurrent execution of transactions must exhibit a property referred to as serializing. Atomicity requires that operations of purchase must be completed. If all the processes are not completed, then the whole transaction is aborted and not saved. The transaction, in this case, is viewed as one complete unit of work that is indivisible. Consistency refers to the property that all transactions must leave the database in a persistent and valid state both before and after operations. The transaction being undertaken must take the database from one consistent state to another valid and steady-state. The transaction in this regard is aborted if it violates this rule. Isolation requires the execution of one transaction is not affected by another transaction until it is completed. This brings on the need to serialize multiple transactions requiring execution, while durability requires that all transactions remain committed once the transaction is complete. The transaction must not be lost even if it has errors.
The NoSQL databases do not adhere to this concept of transactions but instead follow the CAP theorem. The theorem includes Consistency, Availability, and Partition tolerance. Consistency requires that the data be similar across all replication servers. Availability requires that the data must be accessible to the user permanently. Partition tolerance requires that the database must efficiently work regardless of the machine and network failures. The CAP theorem says that only two of these elements can be guaranteed at any given time when using a distributed database system such as NoSQL. This then brings on the concern of transaction integrity when using NoSQL. While NoSQL does not fulfill all the transactional requirements, it makes better integration with the manufacturing process. The use of NoSQL can be integrated with the Manufacturing Execution Systems (MES) to optimize the manufacturing process in real-time. It was achieved through the use of big data analytics to generate faster responses to the changing conditions. Production activities produce a lot of data, and if this data is managed efficiently, then the production process can be improved by changing its operation based on insights generated by using NoSQL for analytics.
Analytics and decision support requirements
In the field of analytics, and the support of business intelligence, NoSQL is ideally more than SQL. The SQL is limited because of scalability and complexity concerns. Scaling relational databases require powerful servers, and this is expensive and hard to handle. The reason for these scalability issues is because the relational database has to be distributed on multiple servers. The complexity of the SQL server is a limitation since the data has to follow the table structure. If the intended data does not include fir, then it requires a complete redesign.
NoSQL deals with the unstructured data type. The NoSQL database does not require fixed table schemas. NoSQL is beneficial in that it is easily scalable; hence it can be used for analytics. Maintaining NoSQL servers is also less expensive, with the server cost being low. The use of big data in supporting decision making in the manufacturing environment proves a lot of opportunities for organizations. Analytics can be used to predict the demand for products; hence less time is wasted. The manufacturing process also potentially improves accuracy and the quality of output since they can pinpoint various metrics of production using big data analytics. Using faster response database architectures such as NoSQL allows the management to perform better simulations, affecting the real world decision to make a particular product.
Data privacy and security issues
NoSQL has emerged with several security challenges. The primary focus of the NoSQL form of database is handling new data sets. This has reduced the role of security in the formation of NoSQL databases. The NoSQL platform is primarily concerned with dealing with the requirements of analyzing the bid data but does not emphasize security consideration during its design phase. NoSQL platform does not provide features for embedding the security layer onto the database structure itself. However, the developers are encouraged to implement the security features at the middleware layer.
The first security concern of the NoSQL databases is transactional integrity. Due to its soft nature, the NoSQL platform does not impose strict transactional integrity. The addition of stringent transactional integrity constraints is not possible since it would impact some of the big data capabilities of NoSQL. The addition of the security constraints would affect the performance and scalability of the database. The second security concern regarding the NoSQL database regards their authentication mechanism. The NoSQL databases are susceptible to brute force attacks, cross-site, and injection attacks. This type of attack successfully leads to the leakage of information.
The third security concern is susceptibility to injection attacks. Injection attacks occur when an attacker adds data to the NoSQL query commands or storage statements hence leading to data corruption and unavailability of data. NoSQL is a susceptibility since it uses lightweight protocols and loosely coupled architecture. It allows the attacker to simply expose a backdoor to the file system hence undertake malicious activities undetected. The fourth security concern of NoSQL is that they lack consistency. The NoSQL architecture does not fulfill all the ACID properties. The platform uses distributed commodity servers; hence the consistency aspect is not always assured. The reason for this is that the participating commodity servers, in some instances, are not entirely in sync with the other servers with the up to date data. In the event of a failure in one node, an imbalance occurs if consistency is not maintained. The reason for multiple servers is to ensure redundancy, which is a feature of big data analysis.
Another security concern with NoSQL is insider attacks. The NoSQL platform has relatively weaker logging and logs analysis features. This makes it difficult for the user to reserve control over the information stored in this form of a non-relational database. The security concerns raised about the NoSQL architecture are of particular interest to the manufacturing industry since the attack on the production system could have a detrimental effect both economically and the safety of the employees. If an attacker was able to hit the production system and deliver a denial of service attack or injection attack, then they can alter the specifications hence leading to variations in quality. An attacker could also modify the demand forecasting done by analytics, consequently derailing the production and distribution process. In the highly competitive market, this could play to the advantage of competitors.
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