Is your database a bottleneck to your application performance? Is your relational database slowing you down? Do you need to make changes to your data structure frequently? Are you working with huge amount of unstructured data?
These are few of the questions which gets asked often when it comes to deciding between SQL and NoSQL database. In this article, you will learn few basic concepts of SQL and NoSQL Databases, understand the basic difference between them & briefly walk through the issues which the NoSQL approach resolves.
Let us consider a real world scenario —
Information sharing has increased by leaps and bounds. If you look at social media usage these days – from Facebook to Twitter to Instagram to LinkedIn – people continuously share messages, tweets, pictures, videos and more. Most of this information does not adhere to a fixed schema. So, keeping track of these unstructured data and making it readily available is a big ask. NoSQL Databases are gaining more popularity because of their ability to handle unstructured data and cater to this huge increase in data volume.
Relational Databases have rigid schemas and data is stored in a structured manner. You enforce data integrity rules by using foreign key constraints. Data Consistency is of prime importance. Whereas in a Non-Relational Database, there is no concept of relationships and you store de-normalized data.
Let us now look at some key features of NoSQL databases and what makes it different —
Support for varied Data Models
Relational Databases have been there for a long time now. But with the continuous evolvement of data and its format, everything cannot be stored in the traditional row/column structure.
The basic difference between SQL and NoSQL databases is the Data Model — Instead of storing data in rows/columns in a table, the data is stored as documents/key-value pairs in a NoSQL database. With a relational database, you need to ensure that the data stored confirms to the schema defined, but with non-relational database you can store documents that have completely different structure.
NoSQL databases utilize a variety of data models, including document, graph, key-value, and columnar.
NoSQL Databases are schema-agnostic and provides more flexibility if the schema needs to change. With Relational database, you require additional work to modify the schema and also handle the already stored data separately.
With RDBMS, you need to have a strong understanding of how data is mapped and need to define the relationship between your tables. There is also an initial effort required to finalize the relational table structure.
With NoSQL approach, there is no need to spend time to understand the relationship between data and define your schema. You can insert data into your tables without any fixed schema. If there is a Business requirement to change the structure of the inserted data, it is not a problem any more, since your schema design is extremely flexible.
Developers with no experience in SQL can get up and running in no time.
With RDBMS, if you want to modify the relational data across multiple tables, you need to perform it inside a transaction so that you do not leave your database in an inconsistent state. NoSQL databases simplify this process immensely.
Response Time of a second which was considered great for Web Applications until few years back, is considered as slow with the current day applications.
Since data is not normalized in NoSQL Database, there is no need to join multiple tables to fetch the required data. Even while storing data, there is no need to store information in multiple tables, you can insert a single document into the table. Each document can be stored with a different structure.
Few of the popular NoSQL databases are below –
NoSQL Databases solves a number of issues related with the traditional approach of handling data. It is a change of mindset and thinking beyond what relational databases has to offer. NoSQL technologies are getting embraced by the community for solving their specific data issues. Hence next time when you bump into a data management issue, do evaluate if a NoSQL solution fits well into your use case.
Categories: NoSQL, SQL Server
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