This website uses cookies.

Cookies are small text files held on your computer. They allow us to create the best browsing experience for you on our site. By using this website or closing this message, you are agreeing to our Cookies policy.

inqicon

START A CONVERSATION

Share your requirements and we'll get back to you with how we can help.

Fill the form below or use your

Send via

account

account

Thank you for submitting your request.
We will get back to you shortly.

Data Stores for New Age Business Applications

Relational database management system (RDBMS), the dominant paradigm in data management for decades, has been supplemented by a wide range of non-relational database technologies collectively referred to as NoSQL. A wider choice calls for a more careful selection of technologies that best answer your business needs. And, increasingly, this choice is not about having one data store instead of another, but about having an eclectic mix of different data stores for different data types.

We analyze your requirement and help you choose the most appropriate database solutions that can meet the complex requirements of your applications.

Database Requirements of Modern Applications

Unlimited capacity for fast growing data

Ability to handle diverse data types

Capacity to store and query complex-structured data

Support for dynamic schema

Support for agile development

Faster response times

High fault-tolerance

Capacity for high-speed analytics

Why NoSQL?

Flexibility in Design

Flexibility in Design

Modern applications have to deal with a range of evolving data, so it is necessary that their backend data stores are equipped to deal with sudden schema changes. Relational databases are highly structured and have a rigid schema that is defined at the time of design. Getting the structure right the first time is important for these databases as revisions are laborious and difficult to achieve without disrupting the application. NoSQL databases, on the other hand, allow the data model to evolve with the application and the business environment, which makes them ideal for iterative and agile development.

Performance at Scale

Performance at Scale

Relational databases achieve consistency through data normalization or the organization of data into multiple tables in a way that removes data duplication. However, as the data grows in volume, and with it the number of tables and joins, performance takes a hit. For a large number of applications that integrate social networking and cloud computing, availability and partition tolerance have become critical properties, more so than consistency. NoSQL databases are a great fit for such applications as they are built to scale horizontally and cost-effectively without interrupting access. They also offer variable choices in data consistency.

Diversity in Data Types

Diversity in Data Types

Relational databases are good at handling highly structured data such as sales, inventories, ledger, payroll, among others. But the range of data that businesses have to deal with today has expanded greatly. Apart from business transactions, data processing systems have to make sense of a gamut of digital interactions that generate information of diverse structures. There are also different formats to be dealt with, including binary documents like image, video, and audio. Some NoSQL databases offer efficient storage and processing capabilities for one or many of these data types and enable retrieval with little downtime.

Say Yes to NoSQL:

  • If you are dealing with non-tabular data with multiple nesting and hierarchies
  • If you are dealing with large volumes of volatile data
  • If you cannot decide the database schema upfront
  • If your application does not need the ACID properties of an RDBMS

NoSQL Classification

Graph Databases

They offer a highly expressive data model in the form of graphs. Index-free adjacency in graph databases makes them a great alternative for querying complex and implicit relationships in data.

Document Databases

They contain documents that can store very simple to complex nested data in them. They offer a flexible model that lends itself to changes in the schema as and when your business grows.

Columnar Databases

They store data in columns and scan only those columns relevant to the query. They enable rapid aggregation of data with less I/O activity, which makes them ideal for BI and analytics.

Key-Value Stores

They present a simple yet powerful data model that stores data as key-value pairs. The fast lookup they provide makes them ideal for storing user sessions or shopping carts.