UFCF8H-15-M Big Data Assessment: SQL and NoSQL for Agriculture

This report provides a critical review of the use of SQL and NoSQL databases in the agriculture sector, addressing the challenges of managing and analyzing big data. It explores the advantages of SQL, including its structured approach, interaction capabilities, and scalability, and the benefits of NoSQL, such as its flexibility, scalability, and ability to handle unstructured data. The report highlights the differences between SQL and NoSQL in terms of type, language, scalability, and structure. It offers guidance on choosing between RDBMS and NoSQL based on specific needs, considering factors like data consistency, query complexity, and the volume of data. The conclusion emphasizes the impact of big data and the importance of selecting the appropriate database for optimizing operations in the agriculture sector.

Running Head: SQL and NoSQL in Agriculture sector
SQL AND NOSQL IN AGRICULTURE
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction:....................................................................................................................................2
Importance of SQL in the agriculture sector:..................................................................................2
Importance of NoSQL in the agriculture sector:.............................................................................4
Difference between SQL and NoSQL:............................................................................................6
RDBMS or NoSQL database for agriculture sector:.......................................................................7
Conclusion:......................................................................................................................................8
References:....................................................................................................................................10

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Introduction:
Agriculture is one of the main aspects of economic growth. There are many benefits of
ensuring data quality that is used by different experts to support their activity, such as monitoring
and planning methods. To mitigate the challenges in agriculture sectors, many types of database
is used. To understand complex agriculture ecosystems, people are using many advanced
technologies like big data and different types of databases. Modern technologies can
continuously monitor the physical environments. Most advanced technologies produce a large
amount of unpredictable data. By using big data analysis, different companies and farmers can
abstract the value from large datasets. With the help of this method, they can increase their
productivity. This technique is not only used in agriculture to store every detail, it is also used in
most of the big industries. Most of the countries are trying to implement this feature ion agriculture.
The aim of this report is to perform a critical review of current technologies and different
research in agriculture sector, which employs the recent practice of big data, SQL and NoSQL, and
analysis, to solve various problems (Hu et al., 2018).. The definition of data quality can be different,
and it can be subjective. Data quality can be seen in the form of multi-dimensional concept.
Importance of SQL in the agriculture sector:
This is one of the serious concerns when a person choosing a database in the agriculture
sector (Kumar and Menakadevi, 2017). Most of the people facing many confusing when they are
choosing a database type, and often that decision swings between NoSQL and SQL. SQL is
famous in this sector for decades. But recently, NoSQL is gaining popularity.
SQL is dominating the agriculture sector for several decades and still using it in this
sector (Woodard, 2016). SQL has better interaction between the data and a user. Before SQL
data is not interactive, and without any interaction, data is useless. SQL is consistent, and it

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allows a user to apply their knowledge while developing a database for agriculture. A farmer can
get support from third-party tools and add-ons. This is a versatile and scalable language and
helps to solve different types of problems. It can be used for scan-intensive deep analytics in big
data technologies (Zhao and Guo, 2018).. SQL is accurate in data storage and representation.
Few SQL systems can support the JASON data format and other different structure object
format.
Some essential benefits of using SQL system in agriculture are discussed below:
SQL support interaction:
SQL is one of the declarative query language. Farmers can build their database as they
want. SQL databases can use an algorithm that can assemble internally and provides the
requested results. But NoSQL support procedural query technique. It requires a user to provide
the exact needs, but also, they need to provide how to produce the result.
The declarative query language is comparatively easy to use for every person. This kind
of database will help to analytics, manages, and operates many business operations.
Standardized SQL:
Sometimes many vendors specify and introduce the SQL languages to their application
interface. SQL is consistent, and it has its additional specifications, such as JDBC and ODBC,
and it is widely available. These features can enable an ecosystem and operator tool that can
monitor, inspect, explore, design and develop various agriculture applications that are based on
SQL system (WANG, WU and LI, 2017)..
Not only farmers, but other people can also reuse their UI knowledge and API in the
multiple backend systems, to reduce the application development time. Standardization provide a

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special ETL tool that can enable every organizations to data flow among the systems and
databases.
SQL scales:
There are many false allegation that SQL lack scalability in the agriculture sector. A big
organization such as Facebook developed an SQL interface that can retrieve petabytes of data
and SQL is fast and effective, and it can perform fast ACID transactions. SQL support cloud-
friendly replication, scalability, and various fault tolerance.
SQL supports the JSON data format:
In the early age of the database, the development database cannot support the XML
documents. But a few years back, they added the XML document. And now, most of the SQL
system support the JSON formation of data (Krintz et al., 2016). JSON is one of the popular
interchange formats of data, and it is used widely in agriculture. There are many arguments for
structure data types. Oracle12c, VoltDB, PostgreSQL, and other SQL databases can support the
JSON data format, and many farmers use this database to store their data.
SQL will continuously dominate in the agriculture sector. Modern SQL databases proved
their scalability and established a strong relationship among medium to large scale farmers.
Importance of NoSQL in the agriculture sector:
Nowadays, NoSQL is gaining popularity among relational databases, especially for big
data applications in the agriculture sector. NoSQL support shameless database. The Schemaless
database is fast reliable for the agriculture sector (Sekhar and Sekhar, 2017). Apart from the
agriculture sector, many big industries use this technology to develop and maintain their big data
application. Schema-less data models are better to identify different types of data. NoSQL
databases does not seem analytical when using the interface, NoSQL databases can host huge

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datasets with huge numbers of users that continuously use data to perform several transactions in
time (Waga and Rabah, 2014).
Critical for Scalability:
Most of the agriculture industry faces fundamental changes in hardware development.
Relational databases such as IBM and Oracle scale up. They are centralized databases, and it can
share every technology that can only scale up by adding expensive hardware. NoSQL database
was developed for the distribution. They use many distributed nodes that are known as cluster
and provides the highly elastic scaling capability, and it let farmers add more nodes to control
workloads.
The scale-out approach is usually cheaper than scale-up approach. The costs of
commercial relational database’s licenses are high, and it prohibitive because they set the price
with the single server (Li, 2018). But NoSQL databases are open-source, and most of the farmers
can use this type of database for agriculture purposes.
Critical for Flexibility:
A relational database and NoSQL database are different from each other. A relational
database model takes the input data and divided into several tables, and these tables has rows and
columns. Tables are interconnected with foreign keys and it is also stored in the columns format.
When a farmer runs a particular query, they want to fetch information from many tables,
and that can be hundreds of tables when a person writes data in tables that need to coordinate and
match with another table. When data volume is low, the relational database can capture the data
efficiently (Shukla, Radadiya and Akotiya, 2015). But agriculture applications can produce a
massive amount of data and that need to be retrieved quickly.

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A NoSQL database is a different model. This database is non-relational. The meaning of
non-relational databases is they do not dependent on the tables. Also, it is not dependent on links
between tables to store the data and shape the information. As an example, the document-
oriented NoSQL databases gather data from a person and aggregates and store it into the
documents by using the JSON data. As an object every JASON file can be used. JSON data can
store in the row that contains total 25 tables in relational database. It can aggregate within single
object. The result of this aggregating information can store duplicate information, but in this type
of database storage is not an issue. Because of this feature, the output data model is flexible and
provides efficiency while read and write the database.
NoSQL for Big Data Application:
Nowadays, data has become easier to store and access by using many third parties
applications, including every social media site. Every personal information, geographical data,
and user-generated data can be captured by using a big data application. In agricultural sector,
many big data applications are used to store important information (MATHIVANAN and
JAYAGOPAL, 2019). Many countries are shifting their database from the relational database to
NoSQL database due to the above reasons.
Difference between SQL and NoSQL:
When it comes to questions about database selection, a person can SQL or NoSQL type of
database to use in their application (Sunil, 2019). There is much difference presented in these
two types of databases. Some of the common differences are discussed below:
- Type:-
The SQL database is generally called as RDBMS or relational database management
system. And a NoSQL database is generally known as a non-relational database.

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- Language:
Every SQL database uses the structural query language. This is one of the powerful
language that is mostly used in different organizations. SQL queries can efficiently
handle the complex queries where no NoSQL can not perform complex queries. In the
SQL database, a person needs to select the schema before they start working on SQL and
every data must follow the structure.
But NoSQL database uses the dynamic schema that can handle every unstructured
data. In many ways, data can be stored in the NoSQL database. It can be documented
based or column-oriented, graph-based or in organised ways. A person can find the flexibility
of using the database (Vathy-Fogarassy and Hugyák, 2017). This flexibility means a
document can be generated without creating structure first. SQL and NoSQL databases
also use different syntaxes.
- Scalability: SQL databases are vertically scalable in every situation. Vertically scalable
means a farmer can increase the workloads on specific server by increasing the
component such as SSD, Ram and CPU. But every NoSQL databases horizontally
scalable (Mátyás et al.,2015). Here, horizontally scalable means a person can manage the
workloads by adding the severs or sharing in the database.
- Structure: Generally, SQL database is table-based and NoSQL database are key-value
based, document-based and graph-based. SQL databases are best for accounting services
and NoSQL databases are best for the agriculture sector because this sector produce the
vast amount of data (Kevorchian, Gavrilescu and Hurduzeu, 2015).

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- Support: Every SQL database provider provide great support to every customers. But
NoSQL database is the open sources software and there are only a few experts can
provide support. In the energy situation, they fail to support.
RDBMS or NoSQL database for agriculture sector:
The first step is choosing a database for agriculture is to determining what a farmer need
(Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017). If they are already using a database for the
business, they can easily select one. The complete understanding of ACID vs. BASE properties
can help to make a decision. A farmer needs to know about the ACID properties to choose a
system. The critical factor that a farmer must consider while determining a database between
RDBMS and NoSQL.
They can choose NoSQL because they need:
 When they are using semi or unstructured data to store.
 Limited query patterns.
 There are no complex queries, Views and store procedures are required.
 If farmers are using high-velocity transactions.
 If they are storing large amount datasets.
 If they are required to use distributed computing and storage devices.
 If they do not want to use the data warehouse or bi use cases.
Farmers can choose an RDBMS if they want to:
 If they store consistent data.
 If they required to perform complex dynamic quires, views, or store procedures.
 If they are required to use data warehouses or uses cases.
 If they are wanted to migrate to another database without any changes, the logic or paths.

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Conclusion:
Thus, it can be concluded that the impact of big data and databases like SQL, and NoSQL
in the agriculture sector is huge. This sector is one of the essential sectors for the region. It helps
to balance the market economy. Advanced technologies are helping farmers to grow their crop
and help to maintain their business. The uses of SQL and NoSQL database in Agriculture sector
can enhance the crop business. This report can represent the importance of SQL and NoSQL
databases in the agriculture sector and provide a complete comparison of why and what
databases a farmer can use to increase their business growth. Overall, the value and benefits of
SQL and NoSQL are clear, and the importance of these technologies in the agriculture sector is
properly mitigated.

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References:
Mátyás, B., Mátyás, G., Horváth, J. and Kátai, J., 2015. Data storage and management related to
soil carbon cycle by a NoSQL engine on a SQL platform-Joker
Tao. AGRÁRINFORMATIKA/JOURNAL OF AGRICULTURAL INFORMATICS, 6(3), pp.67-74.
Vathy-Fogarassy, Á. and Hugyák, T., 2017. Uniform data access platform for SQL and NoSQL
database systems. Information Systems, 69, pp.93-105.
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F.X., 2017. A review on the practice of big
data analysis in agriculture. Computers and Electronics in Agriculture, 143, pp.23-37.
Krintz, C., Wolski, R., Golubovic, N., Lampel, B., Kulkarni, V., Sethuramasamyraja, B.,
Roberts, B. and Liu, B., 2016, August. SmartFarm: Improving agriculture sustainability using
modern information technology. In KDD Workshop on Data Science for Food, Energy, and
Water.
Waga, D. and Rabah, K., 2014. Environmental conditions’ big data management and cloud
computing analytics for sustainable agriculture. World Journal of Computer Application and
Technology, 2(3), pp.73-81.
Li, J., 2018. Research on International Competitiveness of Distinctive Agricultural Products in
the Era of Big Data.
Shukla, P.A.R.A.G., Radadiya, B.A.N.K.I.M. and Akotiya, K., 2015. An emerging trend of big
data for high volume and varieties of data to search of agricultural data. Oriental journal of
computer science & technology, 8(2), pp.121-129.
WANG, Y.S., WU, H.R. and LI, Q.X., 2017. Design and Simulation of Agricultural Big Data
Cloud Storage System Based on the Relational Database. DEStech Transactions on Computer
Science and Engineering, (mmsta).

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Kumar, H. and Menakadevi, T., 2017. A Review on Big Data Analytics in the field of
Agriculture. International Journal of Latest Transactions in Engineering and Science, 1(4), pp.1-
10.
Kevorchian, C., Gavrilescu, C. and Hurduzeu, G., 2015. An Approach Based On Big Data And
Machine Learning For Optimizing The Management Of Agricultural Production
Risks. Agricultural Economics and Rural Development, 12(2), pp.117-128.
Sunil, G.C., 2019. Report on Implementation of Big Data technologies in low resources local PC
on Agricultural Sensor Data.
Woodard, J., 2016. Big data and ag-analytics. Agricultural Finance Review.
MATHIVANAN, S. and JAYAGOPAL, P., 2019. A Big Data Virtualization Role in Agriculture:
A Comprehensive Review. Walailak Journal of Science and Technology (WJST), 16(2), pp.55-
70.
Sekhar, C.C. and Sekhar, C., 2017, March. Productivity improvement in agriculture sector using
big data tools. In 2017 International Conference on Big Data Analytics and Computational
Intelligence (ICBDAC) (pp. 169-172). IEEE.
Zhao, J.C. and Guo, J.X., 2018, April. Big data analysis technology application in agricultural
intelligence decision system. In 2018 IEEE 3rd International Conference on Cloud Computing
and Big Data Analysis (ICCCBDA) (pp. 209-212). IEEE.
Hu, C., Zhu, X. and Zhou, Y., 2018, July. The use of NoSQL in product traceability system
construction. In 2018 5th International Conference on Information Science and Control
Engineering (ICISCE) (pp. 574-577). IEEE.