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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.
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
Capacity for high-speed analytics
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.
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.
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.
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.
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.
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.