To understand whether we need NoSQL or not, we first need to understand the underlying assumptions and features of the more traditional DB format i.e., the Relational Databases.
General Architecture of RDBMS:
The general idea behind relational DBs is that data is stored in rows and columns and most traditional, and even modern applications, use this storage architecture since it has been quite prominent even before the development of the internet. The basic backbone or an intrinsic feature of RDBMS is the data manipulation & query language SQL which effectively enables DB admins and developers to perform operations on the datasets.
Moreover, considering the well-structured relationship between rows and columns, and essentially the data in these rows and columns, RDBMS offers ACID properties that guarantee the integrity of data and transactions:
Atomicity: This ensures that a transaction is fully completed. A good example to explain this would be money/fund transfer applications wherein funds if deducted from one account would be added to another account. A failure at any point does not lead to partial completion of such a transaction.
Consistency: This ensures that the database is the same before and after the transaction. For example, the total amount both before and after a transaction takes place should be the same.
Isolation: This property enables those transactions although occurring concurrently will be mutually exclusive to one another and will resemble transactions that occur sequentially.
Durability: In case of system crash/failure all the transactions completed are frozen and irreversible.
The Inevitable Evolution of Technology:
With the advent of the internet and more advanced solutions that are delivered over complex cloud architecture the need of the hour is a flexible DB format that is more customizable and that provides more control to the developers. Moreover, these solutions produce and operate with vast unstructured datasets that do not fit into the traditional RDBMs row column architecture and thus a more contemporary format that is adept to deal with such an unstructured dynamic database is called NoSQL.
In a NoSQL database such as BangDB the developer/admin is free to store their data in multiple formats from documents, key value store to graph-based data. Also, a NoSQL DB not only provides the required DB storage format flexibility but also ensures uptime and availability for applications that deal with multiple concurrent users that are spread across geographies and that generate a huge number of data points in each time window.
Primary properties of NoSQL:
The general idea of a database schema/model that can deal with an unstructured form of data is more than a decade old and its foundation/primary principles are:
Scalability: NoSQL DBs can be scaled horizontally by just adding more servers to support the application.
Fault Tolerance: As DB is stored across multiple nodes there is an inherent fault tolerance/avoidance in NoSQL DB considering a system failure/outage at one point/server farm does not bring down complete application.
Adaptable Schema: As each application/business is constantly increasing its services and functionalities to survive in a competitive landscape a traditional and rigid RDBMS schema is neither easy to change nor scale. A NoSQL DB is efficient in handling such complex scenarios as it has a flexible schema that allows creation of new fields/columns to make way for future upgrades.
NoSQL vs NewGen NoSQL
In today’s day and age, data capturing and analysis is not only limited to one device as consumers interact with multiple mobile and connected (IoT) devices. A NoSQL database designed using a ground up approach is adaptable in real-time to the changing requirements of the application which in turn is dependent on the changing/ever-increasing needs of the consumers that are using and interacting with these applications. Moreover, the new generation NoSQL DBs such as BangDB are designed to natively support the following functionalities as they are built from scratch within the database without dependence on third party applications/integrations:
Convergence of Different Dimensions
Integrated Stream Processing: As applications and interfaces generate a huge amount of data it is not only important but also quite necessary to derive real-time/near-instant value from such data processing and this becomes an important decision making and business tool for a range of applications from system security and fraud detection to online shopping. A conventional NoSQL DB although adept at handling this unstructured data and scaling dynamically may need to integrate with a more modern solution.
Native AI and ML Integration: This is a natural extension to the integrated stream processing as it not only enables prediction of key events based on the incoming stream of data but also gives the developer/data scientists an opportunity to train models on the incoming stream of data.
Clever Graph & Ontology: Data stored in NoSQL inherently lacks structure but latest solutions such as BangDB are able to provide a graph-based storage that implicitly arranges and forms relationships within the data. This not only provides ease of management but also ease of query and operations on an unstructured data format.
Multi Model:
An earlier generation of NoSQL was designed to handle unstructured data in a particular form for example MongoDB core-competency is enabling JSON file storage but the requirements of today’s applications cannot be force-fitted to a particular model as the same business might require different schemas and models and precisely because of such needs more modern databases such as BangDB natively provide a multi-model approach that offers Key Value, Graph, Document and Columnar schemas. Such a multi-model approach not only enhances the age-old flexibility offered by NoSQL’s but also provides an opportunity to make applications future ready.
Hybrid Deployment Model:
Current world use cases are neither limited by local computing power nor the network bandwidth/availability and hence each app/device might develop and deploy solutions locally or globally and earlier NoSQL schemas were not designed for such dynamic deployment. The more contemporary form of NoSQL DBs is built to handle multiple types of deployment models:
Embedded at the edges: NoSQL Database instances capable of deployment near the data generation source give immense capabilities to systems that avoid computation and processing in a centralized environment, such as IoT devices.
Client Server model: This becomes particularly important for applications that use shared database specially for mid-sized data.
Distributed: This is an inherent property of NoSQL, as discussed earlier, which enables horizontal scaling and multi-nodal approach to ensure availability and faster write operations.
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Newgen NoSQL the Inevitable Future:
Although the traditional RDBMS schemas are tightly integrated into general technology architecture of both current and legacy systems but with the increase in not only the amount of data generated but also the complexity of real time analysis it becomes imperative, especially for organizations that are scaling businesses, that a modern NoSQL solution that is specifically built to natively support Streaming, AI/ML and graph linking is deployed as these next generation of applications and businesses will be based on the premise of extracting as much value from data within an excessively small window of time.
