SQL vs NoSQL

Whether you are an engineer, an IT professional or an analyst, we are certain that you must already be aware of SQL – Structured Query Language and how they are used for interaction with RDBMS – Relational Database Management Systems. These are decades old paradigm and remain still a wide standard. But somethings have changed and includes the increased availability of processing power and of storage, the ever- evolving analytic requirements and the alarming rate at which the volume of the unstructured data is rising.

These changes have led companies and individuals towards a different route and led them to the point where they wish to know, learn and work with fundamentally different technologies.

Traditional RDBMSs have been altercated with NoSQL, and they have certainly shown more promise when it comes to a multitude of modern- day use cases.

To make better informed decisions about the usage of either, it is best for the practitioner to know about the difference between SQL and NoSQL, along with the situations they are best suited for.

What is SQL?

Structured Query Language, more popularly known as SQL is the standard language in use for dealing with Relational Databases.

What is relational database? It defines all relationships in the form of tables.

With SQL programming, one can effectively and efficiently search, delete, update and insert database records. But does the prowess of SQL end here? Of course not!

SQL is efficient enough to do a lot of things including, but without being limited to, maintenance and optimizing of databases.

The use of SQL can be found in many relational databases such as Oracle, Sybase, MySQL Database, and Ms SQL Server, amongst others.

What is NoSQL?

NoSQL, which stands for ‘Not SQL’ or ‘Not Only SQL’ is a non-relational DMS, which is super easy to scale, avoids joins and does not feel the need to have a fixed schema. The main use of NoSQL database is found for distributed data stores that possess vast data storage needs. NoSQL has also found its use in real- time web applications and Big Data, such as Google, Facebook, wherein terabyte of user data is collected on an everyday basis.

The concept of NoSQL was introduced in 1998 by Carl Strozz.

While the traditional RDBM employs SQL syntax for storing and retrieving data for additional insights, the NoSQL database system involves a unique range of database technologies capable of storing unstructured, semi- structured, structure and polymorphic data.

While there are quite a few differences between SQL and NoSQL, we are going to take the blog ahead with five major factors that draw a line between the two.

Language

For more than 40 years now, practitioners have been using SQL. Thus, it is better documented, more widely- employed and has a better recognition rate. SQL is known to be better suited for complex queries because of its high qualities such as versatility and secure. However, there are certain restrictions of using SQL, including the user having to work within a set tabular schema, leading to more time and efforts being directed to the organizing and understanding of the data before using it.

On the contrary, NoSQL offers a better scope of flexibility with its dynamic schemata, wherein its database allows the representation of alternative structures, many a times alongside one another. The user gets to work with additional freedom when including new fields or attributes, exploring the possibility of multiple syntax across databases and with way less emphasis on the planning part. However, as a groups, NoSQL language falls short on the standard interface provided by SQL, thus leading to complications in executing more complex queries.

SQL has quite a few dialects but the share a common syntax and identical grammar, which makes leads to heightened efficiency when querying relational databases. With NoSQL, the consistency is quite low as there are quite a few unrelated technologies.

Structure

SQL database schemata is known for representing tabular, relational data with rules regarding integrity and consistency. They constitute tables with rows (records) and columns (attributes), while the keys show constrained logical relationships.

While the NoSQL database does not fit into the format set forth by SQL, it mostly fits into either of the four broad categories mentioned here –

Key- Value stores are dictionaries that access various objects with a unique key to every single one.
Graph databases are responsible for including the concept of relationships (which also refers to the direct link between the objects) to the documents. This allows swift traversal of greatly connected datasets.
Column- Oriented databases are ones that transpose row- oriented RDBMSs that allow efficient storage of individual records and high- dimensional data attributes.
Document stores semi- structured data. These are objects that contain their entire relevant information, but which can also be fully different from one another.

Scalability

Upon increasing the processing power of the already existing hardware, it is possible to vertically scale most SQL databases.

On the contrary, NoSQL databases employ a master- slave architecture, which is known to scale horizontally and contains additional nodes or servers.

While these generalizations are useful, here are some things you need to make a note of –

NoSQL technologies know no bound in diversity. There is option for vertically scaling even though a lot of them rely upon the quite famous master-slave architecture.
It is possible to horizontally scale SQL databases. However, partitioning or sharding logic is often said to be the user’s liability and is therefore, not well supported.
Savings that come as a result of incorporating more resourceful data structures often lead to the overwhelming of differences in scalability. It is, therefore, imperative to fully comprehend the use case and then get into the planning part.

Support & Communities

SQL databases are known to boast humongous communities, definite standards and stable codebases. There are a zillion examples that are already posted online and still experts ae available at all times to support new- comers in all programming relational data. On the other hand, NoSQL technologies are gaining traction at a quick pace but yet remain to achieve a similar level or expansion of community.

A lot of SQL languages are associated with big single- vendors or proprietary. Inversely, NoSQL communities gain benefit through their concerted communities and open systems to onboard users.

SQL is open for availability to almost every major platform, ranging from architectures to programming languages and operating systems. In the case of NoSQL, the compatibility takes a varied and wider route. Also, the dependencies in this scenario need to be further investigated in a careful manner.

Properties

When we talk about resolving transactions from a high level, SQL and NoSQL take the route f separate rules.

RDBMSs must be ready to exhibit the four major ACID properties –

Atomicity – every transaction should either fail or succeed entirely. The option of partially- complete is not available, even when talking about the case of a system failure.
Consistency – the database must follow invariants at each step of the way. Invariants are rules that prevent and validate corruption.
Isolation – it helps in preventing concurrent transactions from directly affecting one another. So, even if numbers are run parallelly, they should result in the same consequential state for every transaction, as though they were run sequentially.
Durability – it renders the transactions final. Even system failures are ineffective in rolling back the consequences of a successful transaction.

In the case of NoSQL technologies, the CAP theorem is adhered to, which states that only two of the below three properties can be guaranteed at one point of time, in any distributed database.

Availability – states that each request gets a non- error result, irrespective of how recent the result is.
Consistency – states that each request gets the most current result or error.
Partition Tolerance – states that any losses or delays will not result in interruption of the system’s operations.

Some other SQL Databases

MS- SQL
Oracle Satabase
Access
PostgreSQL
SQLite
Sybase
Ingres

Some other NoSQL Databases

MongoDB
Apache CouchDB
Redis (Remote Dictionary Server)
ArangoDB
Neo4j
InfinityDB
InfiniteGraph
DynamoDB

Conclusion

There are quite a few things the led to companies coming to the final call as to when to use which one. The decisive factors can be many and each play a role in coming to the final decision.

However, each is extremely important in the world of data analytics and thus a must for those who are looking to make a career in that sector. If you are one of those aspirants too, then you need to get yourself enrolled with Grras Solutions Data Analytics training program now. Why? Because there is no time like now to get started and get ahead.

With the help of experts at Grras Solutions, you will be able to find out which one is more profitable for businesses, SQL or NoSQL, and which one holds the capability to change to life. Get prepared to become a leading name in the field of data analytics with Grras Solutions today.