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The Evolution of NoSQL Databases


Big Data - RapidValue

The label ‘NoSQL’ misleads and many of you would be confused.  NoSQL – What does it mean?  Is it a NO for SQL or not to use SQL?  Let’s explore the details.

NoSQL is a class of database management systems (DBMS) that do not follow all of the rules of a relational DBMS and cannot use traditional SQL to query data. The name ‘NoSQL’ is somewhat confusing and mostly interpreted as ‘Not Only SQL’. These categories of databases are not a replacement to surviving RDBMS but rather a value addition to RDBMS and SQL.

What’s driving this change?

Relational databases have a long-standing position in most of the applications across different organisations which meet the required business rules and are supported by large pool of resources to implement and maintain these kinds of systems. Databases designed for today’s needs are not only projected to be flexible enough to handle different data formats, but they are also expected to have extreme performance and to scale towards humongous data volumes.

Companies are progressively considering alternatives to legacy relational infrastructure.

  • In some situations the impulse is technical — such as a necessity to scale or perform beyond the capabilities of their existing systems.
  • While in other cases companies are driven by the longing to identify viable alternatives to expensive trademarked software.
  • Another important motivating factor is speed or agility of development, as establishments aim to adapt to the market more swiftly and embrace agile development approaches.

These propel apply both to analytical and transactional applications.

But today, non-relational, NoSQL databases are gaining prominence as an alternative model for relational database management.

Why is this evolution in database management happening these days? Let’s find out.

RDBMS store, manage and retrieve data in relational databases through applications and queries either on the same computer or over those on the network. It holds structured data like name, address, email, telephone numbers etc. commonly known as datasets and the resulting group is defined as schema. The structured methodology of RDBMS database like SQL slows down performance as data volume or size increases and it is also not scalable to meet the needs of Big Data.

NoSQL encompasses a wide variety of different database technologies that were developed in response to a growth in the volume of data stored, the frequency, in which this data is retrieved, and performance and processing needs. It has now become an alternative to the traditional relational databases with scalability, availability with no slowdown in performance.

Benefits of NoSQL Database

Using NoSQL database for processing big data has several advantages. Some of the key benefits are:

Elastic Scaling: Earlier with relational database, database administrators always depend on scaling-up or purchasing larger, expensive, multiple servers, as the database load increased, rather than scaling-out or distributing the data across multiple hosts. The new classes of NoSQL databases are aimed to expand transparently and horizontally, to consider the advantage of new nodes and it is generally designed, keeping in mind, the low-cost commodity hardware.

Bigger Data Handling Capability: RDBMS capacity has been increasing to match the growth in volumes of data, but the boundaries of data volumes that can be handled by a single RDBMS are insufficient for some enterprises.  NoSQL distributed databases allow data to be spread through thousands of servers with little reduction in performance. And it exceeds that of other databases, which can be controlled by the biggest RDBMS. Features like automatic repair, relaxed data distribution, and simpler data models make administration and tuning requirements lesser in NoSQL.

Cost Effective: NoSQL databases usually use clusters of inexpensive commodity servers, to manage the humongous data and transaction volumes, while RDBMS inclines to rely on expensive servers and storage systems. Therefore, the processing and storing data cost per terabyte in case of NoSQL can be many times lesser than the cost of RDBMS.

No Schema or Fixed Data Model: Data can be inserted in a NoSQL database without defining an inflexible database schema. The data being inserted or the data model can be restructured any time, without application disruption. This offers immense application and business flexibility. But on the other side, change management is a challenge in SQL.  In NoSQL, even trivial changes to the data model have to be wisely managed and may require downtime or reduced service levels.

Integrated Caching Facility: To increase data output and performance improvement, NoSQL techniques cache data in system memory. This is different to SQL database, where this kind on caching has to be performed using separate infrastructure.

The Conclusion

Volume of data is expanding at an alarming rate and at the same time presents incredible business opportunities. The almost limitless array of data collection technologies ranging from simple online actions, point of sale systems, GPS tools, smartphones and tablets, sophisticated sensors and many more, act as force multipliers for data growth. These massive data (commonly known as Big data) is one of the key forces, driving the growth and popularity of NoSQL for enterprises.

Have you worked with Big Data and NoSQL database before? If, yes do share your experience with us by adding a comment or send an email to


Riji Varghese and Anish George Mathews

Technical Lead – Cloud & Big Data Experts, RapidValue Solutions

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