Competition between businesses is common in today's world, especially when they offer similar products. In the competitive field of Data Analytics, having a majority customer share in the market and offering efficient products and services helps determine the company's profit. When it comes to Database Management, the choice between MongoDB and PostgreSQL is pretty difficult.
This article provides a detailed evaluation of the both databases as well as highlights the key differences between them to assist you in making an informed decision between MongoDB and PostgreSQL. It also gives you a quick overview of both databases and their features. Finally, it discusses some of the difficulties you may encounter when using these databases. Continue reading to learn how to select the best database for your needs.
MongoDB employs BSON (Binary JSON) and MQL, a SQL-alternative language. BSON supports data types that are not supported by regular JSON, such as long, floating-point, and date. MQL, like SQL, provides similar features with some extra features. Because documents are handled as JSON-type documents, this is necessary.
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MongoDB provides a wide range of services, making it a better solution than other databases. Some of these features include:
PostgreSQL, often identified as Postgres, is really a free, open-source relational database management system that emphasizes extensibility and SQL compliance. It was created at the University of California, Berkeley, and debuted on July 8, 1996. It was the Ignes database's replacement. PostgreSQL stores data as Structured objects rather than documents. It adheres to the standard SQL format and syntax.
It is written in C and has a legacy system, which indicates that the elements are fully integrated and work in a logical order. It provides community support as well as additional assistance to some of its paid customers. Because of its creative backup mechanisms, it is widespread used during healthcare, financial services, and manufacturing sectors.
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PostgreSQL has some distinguishing characteristics that make it a viable alternative to other traditional RDBMSs.
MongoDB does have the possibility to be ACID compliant, whereas PostgreSQL already is. The ACID properties are the major assets of databases that allow transactions to be properly tracked.
MongoDB is a database system that processes data using BSON, whereas PostgreSQL is a relational database that processes data using traditional SQL.
MongoDB is a schema-free NoSQL database with distributed capabilities. MongoDB employs collections to enforce various rules and triggers in order to maintain the relationship between various attributes in the database.
PostgreSQL has a SQL-based architecture but also supports some NoSQL features. It has a monolithic architecture, as opposed to MongoDB. Tables are used to set various rules and triggers on the data. It also frameworks the data so that the database or an ETL (Extract, Transform, and Load) tool can process it efficiently.
Both databases support syntax that is quite different from one another. MongoDB, a NoSQL database, stores data in documents and allows users to access it with MQL. PostgreSQL, on the other hand, stores and accesses data using an RDBMS structure and SQL.
Below is the MongoDB syntax for creating a database, checking to see if it is the current database, and displaying the database.
Creating the database:
switched to db mydb
Checking to see if the newly created database is the current database:
Displaying the contents of the database:
The following is the MongoDB syntax for inserting a record into the database.
The PostgreSQL syntax for creating the table "statements" is shown below.
CREATE TABLE statements (
user_id serial PRIMARY KEY,
username VARCHAR ( 50 ) UNIQUE NOT NULL,
password VARCHAR ( 50 ) NOT NULL,
email VARCHAR ( 255 ) UNIQUE NOT NULL,
created_on TIMESTAMP NOT NULL,
The syntax for inserting a record into the table in PostgreSQL is shown below.
INSERT INTO statements(user_id serial, username, password, email, created_on, last_login)
Foreign key support:
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Query Processing data:
To process queries, MongoDB employs aggregation pipelines. These pipelines are made up of several stages that help transform data. PostgreSQL, on the other hand, processes and runs queries using the GROUP BY function.
Maintaining the data:
MongoDB uses redundant replica sets to keep data safe. These sets enable you to record and replay processes as needed. MongoDB employs synchronous replication, that also includes various repositories or systems updating concurrently.
PostgreSQL uses two-safe replication to keep its data safe. PostgreSQL can update both records at the same time, reducing the number of errors while also maintaining a complete and accurate backup.
Relationships among multiple tables in your database add value to your database's analysis and storage capabilities. Indexes are used by MongoDB to connect multiple tables. Indexes are a type of data structure that can store a small amount of information in an easily readable format. They are only one component of a join, but they make your data easier to understand and, as a result, assist you in resolving any queries.
Joins are used in PostgreSQL to incorporate data from multiple tables into a single table. In PostgreSQL, as long as you have two tables, you can combine them using joins. PostgreSQL, like traditional SQL, has four types of joins: inner, left, right, and full. A Full Join can be used to combine all of the data from both tables into a single table.
MongoDB allows the user to select your pricing model from three distinct price plans. They are as follows: shared-$0 per month, dedicated-$57 per month, multi-regional-$97 per month.Because PostgreSQL is open-source and free, anyone can use all of its features for free.
Challenges of MongoDB:
When you've a good understanding of MongoDB, it is crucial to understand some of the difficulties that you may face while working with MongoDB. The difficulties with MongoDB are as follows:
Challenges of Postgresql:
Now that you have a good understanding of PostgreSQL, it is time to look at some of its drawbacks. PostgreSQL's challenges are as follows:
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This article provided a thorough examination of the two most popular database technologies on the market today: MongoDB and PostgreSQL. It discusses both databases, as well as their features and limitations. It also provided the criteria for judging each of the Databases. Overall, the decision between MongoDB and PostgreSQL is entirely dependent on the company's goals and available resources.
If you really want your database to be infinitely performant and have a high computation and processing power, MongoDB is a good option. It can also be used if users do not have programming skills because it is simple to learn and does not use traditional SQL syntax.If you have fewer resources but are well-versed in sql Database syntax and procedures, PostgreSQL may be a better choice. Aside from these minor distinctions, both databases are equally powerful in terms of performance and will work well with any organization, customer, or business requirement.
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If you need an SQL database, then you can go with PostgreSQL. Otherwise, if you need a distributed database for modern analytical applications, then MongoDB will be the best option.
PostgreSQL is useful when you need high-level security, and you predict the scale in terms of queries. Further, it enables you to store large-scale data securely. On the other hand, MongoDB deals with unstructured data storage.
Both are popular databases and vary in terms of various benchmarks. Regarding data warehousing and analysis, PostgreSQL performs much faster than MongoDB. In terms of the relational data model and inserting raw data into the system, MongoDB is the best option.
When it comes to working with large data sets along with complex queries, PostgreSQL works much better. So, it is the best choice for Big Data analysis.
PostgreSQL is the best database to work with Big Data sets, and it will release a new version 16 in 2023. Also, it is an advanced RDBMS with open-source availability that is less prone to data errors.