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When to Use NoSQL instead of SQL

What is SQL?

SQL is Structured Query Language, which is a computer language for storing, manipulating and retrieving data stored in a relational database.

SQL is the standard language for Relational Database System. All the Relational Database Management Systems (RDMS) like MySQL, MS Access, Oracle, Sybase, Informix, Postgres and SQL Server use SQL as their standard database language.

  • It is highly suitable for relational databases.

  • Has a predefined schema which is helpful in many cases.

  • Normalization can be greatly used here, thus it also helps in removing redundancy and organizing data in a better way.

  • Transactions in SQL databases are ACID compliant, thereby guarantees security and stability.

  • Follows well-defined standards like ISI and ANSI which are accepted worldwide.

  • Code-free.

  • Unbeatable speed in retrieving database records with great ease.

  • Uses single standardized language i.e SQL across different RDBMS.

  • The process of interfacing is complex.

  • As SQL is an object, it occupies space.

  • Handling Big data is very costly as you will have to increase the hardware for scaling.

  • When a table is dropped, the view becomes inactive.

What is NoSQL?

A NoSQL database includes simplicity of design, simpler horizontal scaling to clusters of machines and finer control over availability. The data structures used by NoSQL databases are different from those used by default in relational databases which makes some operations faster in NoSQL. The suitability of a given NoSQL database depends on the problem it should solve. Data structures used by NoSQL databases are sometimes also viewed as more flexible than relational database tables.

  • Capable of handling big data.

  • As it is schema-less and table free, it offers a high level of flexibility with data models.

  • It is a low-cost database and the open source NoSQL databases provide very affordable solutions to small enterprises.

  • Easier and low-cost scalability. You don’t need to increase the hardware for scaling. You just need to add more servers to the pool as NoSQL is schema-free and built on distributed systems.

  • Detailed database modeling is not required here. Hence it saves time and effort.

  • The benefits of NoSQL come at the cost of relaxing ACID properties. NoSQL offers only eventual consistency.

  • Relatively less community support.

  • Lacks standardization, unlike SQL, which in turn creates some issues during migration.

  • Inter-operability is also a concern in the case of NoSQL databases.

When to use SQL instead of NoSQL?

1. When you are working with complex queries and reports

With SQL you can build one script that retrieves and presents your data. NoSQL doesn’t support relations between data types. Running queries in NoSQL is doable, but much slower.

2. When you have a high transaction application.

SQL databases are a better fit for heavy duty or complex transactions because it’s more stable and ensure data integrity.

3. When you need to ensure ACID compliance.

(Atomicity, Consistency, Isolation, Durability) or defining exactly how transactions interact with a database. 

4. When you don’t anticipate a lot of changes or growth.

If you’re not working with a large volume of data or many data types, NoSQL would be overkill.

When to use NoSQL instead of SQL?

1. You are constantly adding new features, functions, data types.

It’s difficult to predict how the application will grow over time.

2. Changing a data model is SQL is clunky and requires code changes.

A lot of time is invested designing the data model because changes will impact all or most of the layers in the application.

3. In NoSQL, we are working with a highly flexible schema design or no predefined schema.

The data modeling process is iterative and adaptive. Changing the structure or schema will not impact development cycles or create any downtime for the application.

4. You are not concerned about data consistency and 100% data integrity is not your top goal.

This is related to the above SQL requirement for ACID compliance. For example, with social media platforms, it isn’t important if everyone sees your new post at the exact same time, which means data consistency is not a priority.

5. You have a lot of data, many different data types, and your data needs will only grow over time.

NoSQL makes it easy to store all different types of data together and without having to invest time into defining what type of data you’re storing in advance.

6. Your data needs scale up, out, and down.

As discussed above, NoSQL provides much greater flexibility and the ability to control costs as your data needs change.

Difference Between SQL and NoSQL


SQL database are only vertically scalable. You can increase the performance by increasing RAM, CPU or SSD.

NoSQL databases are horizontally and vertically scalable. You can handle more traffic and increase the performance by sharding, or adding more servers in your NoSQL database

SQL databases are table-based

NoSQL database are key-value pairs, document-based, graph databases or wide column stores.

SQL databases follow ACID properties

NoSQL Databases follow Brewers CAP theorem.

In SQL, Data is distributed across multiple tables

In NoSQL, Data is merged and nested in few collections.

Low performance for simple read ad write queries

Better performance for simple read and write queries

It can be stored in tables

It can be stored

Costly to scale

Cheaper to scale when compared to relational databases

They are a good fit for complex queries as SQL has a standard interface for handling queries.

The syntax of SQL queries is fixed.

Not a good fit for complex queries as there is no standard interface in NoSQL for handling queries.

The queries in NoSQL are not as powerful as SQL queries.

It is called as UnQL, and the syntax for using the Unstructured query language will vary from syntax to syntax.

SQL databases do not suit well for hierarchical data storage.

NoSQL databases suit best for hierarchical data storage as it follows the key-value pair method for storing the data.

Adding new data in SQL database requires some changes to be made like backfilling data, altering schemas.

New data can be easily inserted in NoSQL databases as it does not require any prior steps.

Best fit for high transaction-based applications.

You can use NoSQL for heavy transactional purpose. However, it is not the best fit for this.

Example of SQL databases: MySQL, Oracle, MS-SQL, SQLite.

Examples of NoSQL databases: MongoDB, Apache CouchDB, Redis, HBase.

The Tech Platform



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