In Today’s Blog, We’ll begin with a brief overview of databases, then move on to an introduction to SQL and MongoDB. After that, we’ll look at their applications before doing a direct comparison of the two to see what each has to offer.
An organized collection of data is called a database. They support the electronic manipulation and storage of data. Databases simplify data management & administration.
Database Management System (DBMS) software is used to store and retrieve user data while taking necessary security precautions. It comprises a number of software applications that alter the database. When an application requests data, the DBMS acknowledges the request and tells the operating system to give the requested data. A DBMS facilitates the storing and retrieval of data for users and other third-party applications in big systems.
A database model describes the logical layout and structure of a database as well as the methods for storing, retrieving, and updating data in a database management system. Although the relational model is the one that is most frequently used for databases, there are other models as well. These are:
Entity-relationship Model and Relational Model
The well-known, open-source, and free relational database management system (RDBMS) MySQL was created by Oracle. Similar to other relational systems, MySQL employs tables and rows to store data, upholds referential integrity, and makes use of structured query language (SQL) to access the data. Users who need to obtain data from a MySQL database must build a SQL query that joins many tables to produce the view of the data they need. SQL stands for structured query language and it is one of the standard languages which is used for accessing and manipulating relational databases but then we have to talk about the overall scope or the overall goal of the language. SQL has a very simple goal and that is to make sure that it gives the user a very easy yet powerful way to access the data while storing it and working along with it at the same time. SQL is basically used with a lot of data sets to actually store and work with the data so all this data has to be generated right. Here, step one is working with any databases to make sure that
it actually has the data so after the data has been generated it gets queried
using SQL. Querying is the process where the programmer types in SQL
queries to create a database, create tables, alter the table contents and in fact even delete all of the contents. Also, there are subqueries, joins, multiple queries, nested queries, and whatnot. It has to be known that everything from the simplest query all the way to very complex queries is actually very powerful in the world of SQL. Data can eventually be stored in a very structured way and it can be accessed
very easily and very nicely just because of SQL. It ensures that you know
the data can be worked on at an effective pace.
Prior to storing data in a database, database schemas and data models must be established. Although this method of data storage is inflexible, it does provide some level of safety at the expense of flexibility. Schema migration, which may become complicated and expensive as the database size increases, is necessary whenever a new kind or format of data has to be stored in the database.
Now coming on to the quick introduction to MongoDB. Well, MongoDB is one of the world’s best no SQL databases. Even though MongoDB is open source and free to use, its design principles are different from those of conventional relational systems. MongoDB, which is sometimes referred to as a non-relational (or NoSQL) system, takes a very different approach to storing data than relational systems do, displaying data as a collection of documents that resemble JSON (but are really saved as binary JSON, or BSON).
MongoDB documents are made up of a variety of key/value pairs, including arrays and nested documents. The main distinction is that the structure of the key/value pairings in a particular collection can alter from document to document. Documents’ inherent self-description makes for a more adaptable method. No SQL is basically a non-relational database management system. Here the data is not interrelated to each other and it does not have a fixed schema. Concepts like joins don’t work here but at the end of the day, no SQL database can be spread across multiple
systems and can be scaled at a very rapid pace. This is one of the reasons why in fact no SQL has gained so much attraction and appreciation in today’s world. It is used by a variety of sources for any distributed data storage or needs and if there is a need for any huge data storage requirement at the same time. MongoDB is a general-purpose document-based and distributed database that is actually built for all of the modern developers to use and eventually it has been put together in such a nice way that you know you can move your entire databases to the cloud and take your entire business and scale it up as well.
No SQL databases contain a wide variety of technologies that basically can store any type of data that you give it. It can include structured data for example tables.
It can include semi-structured data, unstructured data for example- images, videos music, or even polymorphic data. There are companies that use MongoDB which make use of no SQL technologies on a day-to-day basis. Be it Twitter, Facebook, Google, or other big corporate companies.
The people at MongoDB claim that it’s been used by millions
of developers to power the world’s most innovative products and services. People at Facebook, Invision, eBay, Adobe, Google, Squarespace, Sega, the gaming company harmony, UK’s government, etc. Hence, all of the big companies are currently going on and working with MongoDB.
1.)SQL databases actually involve handling a lot of data by making use of relational models and this relational model makes sure that the data is related to one another and can be accessed and worked along in a very
simple manner. MongoDB actually makes use of a nonrelational database where the data is actually not interlinked to each other and this is how basically it works.
2.)SQL is actually the primary language when we have to work with any relational databases. We can use Microsoft’s SQL, MySQL, Oracle’s SQL, etc.
On the other hand, MongoDB actually supports querying in JSON where all the
data is Stored in JSON and BSON. This is mostly used to work with the data
indirectly and directly.
as well so coming to the third point we have data
storage in data storage well as
3.)SQL actually involves storing data in databases by making use of tables making use of interlinked storage methodologies but as MongoDB is no SQL database, it stores the data as key-value pairs and collection-based databases so
this is one of the most important differences when we have to talk about
SQL and in fact MongoDB at the same time.
4.) SQL actually makes use of keys to map each table to another or say for example a column to another column in another table so even if you have to map values from one table to another table we use something called keys. We have primary keys, candidate keys, foreign keys, and much more.
MongoDB does not support the use of foreign keys.
Trigger support so SQL ensures that there is valid support when we have to
make use of triggers because triggers again at the end of the day are a special type of stored procedure that will automatically run whenever an event
occurs. MongoDB actually does not allow the users the flexibility of making use of stored procedures and triggers.
SQL databases contain a predefined schema which ensures the user can work with fundamental structured data but then MongoDB does not contain a structured
predefined schema. It goes on with a dynamic schema and this dynamic
schema has been the gold standard of working in all big no SQL databases present today.
7.)SQL is not the best fit when we talk about hierarchical data storage because again storing data one after the other might become cumbersome and inefficient at some point in time. MongoDB shines here because it is in fact one of the best fits when we talk about hierarchical data storage methodologies.
8.)SQL databases can be scaled vertically on top of another just like a stack just by increasing the RAM of the system but then when we talk about MongoDB, Mongo databases can actually be scaled horizontally by adding more servers in parallel to each other. The outcome of this difference actually differs from what
your company wants because vertical scalability is something that is
required at one point in time in an organization while horizontal stability is required by adding more servers to ensure in-order tendencies control data, data backups and hence the data can be accessed quicker than usual. SQL is known for vertical scalability and MongoDB is known for horizontal scalability
9.)SQL emphasizes certain properties that we call the ACID properties.
A stands for Atomicity, C for Consistency, I for Isolation, and D for durability. The
entire basis of SQL is based on these properties to make sure that the
database is relevant and easy to work with and at the same time very low in
redundancy as well. On the another hand, MongoDB uses the CAP theorem where
C stands for consistency, A stands for availability, and P for partition
Tolerance. Hence, these are the driving force behind MongoDB and SQL
ACID for SQL and CAP theorem for MongoDB.
10.)SQL has been there for a while now and it has excellent support because every time there is a SQL provider out there, there are many more vendors out there who provide support for this because at the end of the day SQL as I told you there are
multiple flavors which can be accessed through which can be worked with and at
At the same time all of these flavors are about 70 to 80 percent the very same so
if you come to know one of the flavors of SQL it’ll be very easy to adapt and
learn the other thing as well so it has excellent support when it comes to this
MongoDB also has good support as it gets amazing community support as its community grows out of the product and
helps people too.
These two database systems have substantial internal differences. Choosing which one to utilize is actually more of a methodological choice than a strictly technical one.
For seasoned IT experts, MySQL’s relational database environment offers a comfortable work environment.
MongoDB is a well-known, non-relational database system that provides increased flexibility and horizontal scalability at the expense of several security features of relational databases, such as referential integrity.
Which option ought should you pick?
We’ll examine some of the many factors to take into account while choosing between MongoDB and MySQL in the sections that follow.
MongoDB is being embraced by businesses of all kinds, particularly as a cloud database because it makes it possible to develop applications more quickly, handle a wide variety of data types, and manage applications more effectively at scale.
The natural mapping of MongoDB documents to contemporary object-oriented programming languages simplifies development. By using MongoDB, you can do away with the intricate object-relational mapping (ORM) layer that converts objects created in code into relational tables. Because of MongoDB’s adaptable data model, your database structure may change in response to changing business needs. Because developers must modify objects in code to fit a relational structure, MySQL’s inflexible relational structure causes applications to take more time to load and slows down development.
Database architecture, application query patterns, and database load are only a few examples of the many variables that can affect database performance. It is frequently quicker to obtain a single document from MongoDB than it is to JOIN data across numerous tables in MySQL because MongoDB’s document model groups together relevant data.
Due to both greater scale-out performance and dramatically increased development efficiency, several customers have compared and chosen MongoDB over MySQL.
Author: Akash Upadhyay