Data has evolved to include an ever-expanding range of information. It can be used to collect user information, geolocation data, sensor-generated and social media feed data, as well as many other forms. This huge set of unstructured data, commonly called big data, has become the backbone for many mission-critical applications.
There are two options when it comes to storing large amounts of data. One is in relational databases and the other is in a mapping manner. SQL is the best choice for the first, while NoSQL is the right answer for the second. Also, NoSQL vs. SQL.
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Although SQL is widely accepted and used in the market as a database technology, many organizations are now considering NoSQL databases to replace relational database management systems for large data applications. We will be discussing the reasons for this trend and giving a comprehensive overview of NoSQL vs. SQL in this blog.
Factors that Support SQL For Big Data Applications
Let’s start by looking at the benefits of relational databases rather than SQL. It has two key points that are crucial for any database operation:
1. ACID Compliance (Atomicity Consistency Isolation and Durability): Integrity is a key criterion in any database transaction. It restricts possible anomalies. Any SQL database must have ACID compliance. This is vital for financial and e-commerce applications.
2. Structured Data Structured data is easier to manage. A relational database system also maintains consistent data, which is useful unless the business has to deal with large growing data sets of different types.
NoSQL databases lose the two above points.

Factors that Support NoSQL in Big Data Applications
NoSQL’s real purpose is to prevent data bottlenecks when enterprise applications are handling petabytes. This is why NoSQL databases such as HBase, Cassandra and MongoDB are so popular.
NoSQL databases are characterized by the following key features that make them useful:
1. Large storage capacity for unstructured data: NoSQL databases can store unlimited numbers of data of any type. It allows users to easily change the data type at any time. It is a document-based database. It is therefore not necessary to define the data type beforehand.
2. Cloud-based storage: Most enterprises today use cloud-based storage to reduce costs. Cassandra, a NoSQL database, makes it easy to set up multiple data centres without much effort.
3. Fast development: Relational databases are not the best choice if you work in an agile environment that requires frequent feedbacks and rapid iterations. NoSQL database is a good fit for this framework.
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A Comprehensive Comparison of NoSQL and SQL
Let’s compare NoSQL with SQL. NoSQL vs. SQL.
RDBMS is a row-oriented data base
NoSQL is column-oriented databases
RDBMS works with structured data and related data
NoSQL can work with both structured and unrelated data
RDBMSs use schema, which means that the data structure should be predefined.
No schema is required to store data
Only enhancing hardware can scale SQL databases
NoSQL databases can store unlimited amounts of data
SQL databases can be expensive
NoSQL databases can be cheaper
SQL database maintains data integrity
NoSQL database can sometimes compromise data integrity in order to handle large amounts of data.
RDBMS databases can be licensed
NoSQL databases can be downloaded open-source.
NoSQL vs. SQL from a Developer’s Perspective
Developers use new data types to deal with big-data applications. They store them in the databases and don’t have to alter the original data structures. These data are often unstructured or semi-structured. Developers look for flexibility to best fit data into databases.
Schema-based relational database schemas have a weakness. They are not able to handle unstructured or semi-structured data well and can’t easily include new types of data. NoSQL fills those gaps because its data model matches the requirements better. Let’s look at NoSQL vs. SQL as a developer’s view.
NoSQL is a better fit for big data applications
Two perspectives can be used to view big data.
Operational data – This is mainly about online live data that are stored in operational database. For example – flight booking data. This contains large amounts of data.
Analytical data – It is a lot of data to get insights from. For example, social media data can be used for market analysis.
Therefore, big data storage is based on an operational database that NoSQL database can manage better.