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Data Stores for New Age Business Applications
A wider choice of database solutions calls for a more careful selection of technologies that best answer your business needs. 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 requirements and help you choose the most appropriate database solutions.
Why NoSQL?
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
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
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.
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
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
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.
Database Requirements of Modern Applications
Modern web-based and web-scale applications demand one or several of these attributes from their databases:
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
Vector Databases
Vector databases capture the semantic meaning of unstructured data such as text, video, and audio and convert them into high-dimensional vectors that machines can easily understand and process.
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.
Vector Databases
Vector databases capture the semantic meaning of unstructured data such as text, video, and audio and convert them into high-dimensional vectors that machines can easily understand and process.
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.