Engineering, Technology & Applied Science Research Vol. 5, No. 5, 2015, 850-853 850

Engineering, Technology & Applied Science Research
Vol. 5, No. 5, 2015, 850-853
852 
www.etasr.com 
Erturk and Jyoti: Perspectives on a Big Data Application: What Database Engineers and IT Students… 
 
This is a cloud based tool, and does not require an installation. 
MMS offers a sophisticated database backup service, and can 
monitor up to thousands of online database deployments [7].
The second important area for learning MongoDB involves 
the storage concepts and terminology. Most trainees will 
already be familiar with relational database concepts and terms; 
therefore a good approach would be to explain to them the 
corresponding MongoDB terms along with the similarities and 
differences. As seen in Figures 3 and 4, a MongoDB database 
is made up of collections, which may be viewed as two 
dimensional tables. On the other hand, given a similar business 
domain, relational database structures necessitate the creation 
of multiple tables to represent the data whereas the same design 
can be done with fewer collections in MongoDB. Not having to 
create joining tables for many-to-many relationships and 
handling one-to-many relationships within the same collection 
with are examples of reasons for this. Each MongoDB 
document is similar to an SQL row; however MongoDB’s 
BSON (binary java script object notation) document format 
provides recursive functionality and allows more efficient 
database scanning.
Fig. 3. 
Viewing sample data from MongoDB.
Fig. 4. 
MongoDB structure vs traditional SQL.
Learning the syntax is essential for creating a MongoDB 
database. Advanced students and database professionals are 
already proficient in programming with SQL using relational 
database applications, e.g. Microsoft SQL Server or MySQL. 
Figures 5 and 6 compare statements in SQL versus MongoDB.
In the case of an INSERT command that creates new data 
records, the mapping of the values is easier to understand. It 
can be explained as a transposition from SQL’s horizontal 
coding (i.e. items and values are listed from left to right) to a 
vertical coding in MongoDB where the document items and 
values are listed from top to bottom. In SQL, the input row 
names are grouped together while their input values are also 
grouped together separately; these groups are implicitly 
corresponded based on their order within that table’s structure. 
In MongoDB, the input values are written next to the field 
name, similar to XML (Extensible Markup Language), and 
these fields can be listed vertically, each one on a new line. In 
the case of a READ command (Figure 6) that fetches data 
records, the difference is greater, between traditional SQL 
SELECT statements and MongoDB (which uses the FIND 
method). First, the same kind of transposition or projection 
difference applies, going from horizontal groupings to vertical 
listings. On the other hand, in MongoDB, the user needs to be 
careful to separate the fields involved in the query criteria from 
the fields included in the query output. Furthermore, SQL 
queries use intuitive Boolean operators to define query criteria 
while MongoDB queries use abbreviated tags, such as $gt 
(greater than). The value of ‘1’ also needs to be specified next 
to the field name(s) to show them in the output result.
The publicly available instructional information on coding 
with MongoDB is limited, with many of the resources written 
in a technical language that is not conducive to easier and faster 
learning. For this reason, the explanations offered above will be 
useful in a future training course by filling an educational gap.
Fig. 5. 
NoSQL vs traditional SQL syntax. 

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