NoSQL examples showcase the way in which the databases offer various features and advantages. The examples also help businesses comprehend the multiple techniques used by NoSQL to store information. Further, there are different types of NoSQL databases that help scale large and high-end data sets.
Moreover, NoSQL Database examples offer new perspectives for businesses to expand their dependability on the non-relational DBMS. Further, tech giants Google, Facebook, Amazon, Twitter, etc. rely on NoSQL capabilities to gather and store terabytes of user data. Therefore, it is important for businesses to dwell deeper into NoSQL examples as it uses different technologies to store data. Hence, this article discusses the various NoSQL examples that benefit different businesses more.
Understanding NoSQL Examples
NoSQL databases are non-relational database systems that store distributed data sets. Moreover, it does not require any set schemas and avoids any joins to simplify scalability. NoSQL also stands for Not Only SQL, Not SQL, or NoREL.
Further, NoSQL databases incorporate different database methods to gain actionable insights. It also stores structured, semi-structure, non-structured, and polymorphic data. Moreover, it is non-relational and provides more scalability than relational databases. Hence, web applications easily use their functions to manage and store large volumes of data.
Key Features of NoSQL
- Firstly, NoSQL databases execute a non-relational model. Therefore, it does not use
- object-relational mapping or data normalization to work with data sets. It also offers simple features to store and manage the data.
- Moreover, it is a schema-free database management system and does not require any definition for schemas. It also provides heterogeneous data structures to maintain uniformity in the domain.
- Further, it provides simple APIs for users to easily store and query datasets. That is to say, APIs enable the system to manipulate and choose techniques for low-level data.
- Above all, it enables execution for multiple datasets using a distributed approach. It also provides auto-scaling and fail-over features to enable eventual capabilities.
- NoSQL databases use multiple data models to collect and store the data. Therefore, making them more flexible to manage the data and execute specific requirements.
- Moreover, the database uses no single unit or storage within the architecture. Further, it makes data constantly available as it distributes multiple copies across networks.
- It also provides a flexible schema, unlike relational databases to manage data. Hence, it can easily store and manage multiple datasets regardless of their nature and requirements.
- Further, it helps increase performance by mitigating downtime. It also maintains data using multiple nodes within the architecture to avoid downtime in case one node fails.
- Above all, it uses horizontal scaling to provide accessibility while managing the data.
Businesses should firstly consider their requirements while selecting databases. Hence, here are the factors that can help them select NoSQL Databases:
- Firstly, when businesses are apprehensive about expeditious and agile development.
- Moreover, when there is a requirement to store and manage large volumes of structured and unstructured data.
- Further, there may be requirements to scale-out database environments.
- Businesses may also consider NoSQL to execute the modern application models for microservices and real-time streaming.
Data Structures and Models
Firstly, the pivotal difference between NoSQL and SQL databases includes the management of various data structures. Moreover, SQL databases manage tabular data structures and NoSQL databases manage non0relational data. Further, SQL incorporates structured data to execute assessments using relational models. On the other hand, NoSQL databases integrate unstructured data across predefined models.
Further, SQL databases are vertically scalable. That is to say, users can add more RAM or CPU functions to a single server for storage efficiency. Whereas, NoSQL databases are horizontally scalable. In other words, users can easily add more servers to the database and acquire more storage.
Moreover, when it comes to querying data, SQL executes more complex queries. Meanwhile, NoSQL manages simple queries to process the data. As a result, it depends on the requirements of the business and the database while executing queries.
Further, SQL uses a more elementary technique when it comes to storing data, while NoSQL provides complex solutions for various data structures.
Key-value store model:
It refers to a simple database architecture that includes keys and values. Moreover, it is the least complex NoSQL database that stores schema-free data.
Column Store or Wide-Column stores refers to the technique of collecting and storing data in tables, rows, and dynamic columns. Moreover, it segments the columns to enable eminent scalability and performance.
Document databases refer to storing documents similar to JSON components. Moreover, all the documents include sets of fields and values that include numbers, arrays, objects, etc. It is also an ideal database to store, retrieve, and manage data.
Graph Database stores datasets in various nodes and edges. Moreover, it interconnects and represents the data using graphs. Further, this database enables the processing of complex datasets.
- MongoDB: MongoDB is one of the most popular and open-source NoSQL databases. Moreover, it is a document-based system that stores JSON-adjacent documents.
- Apache CouchDB: Apache CouchDB is Apache’s open-source and web-based system. Further, it uses JSON data exchange architecture to store documents.
- Oracle NoSQL Database: It is an exclusive database that helps JSON-adjacent table and key-value datatypes. It also executes databases across on-premise or cloud environments.
Real-time Data Processing NoSQL Examples:
With the increase in real-time data, companies are in search of a robust and agile solution for data analyses. Further, a basic approach that helps process real-time data requires stream processing and historical data analysis. As a result, NoSQL databases integrate with both approaches.
One of the top examples of NoSQL databases is PayPal which processes real-time big data using multiple techniques. It also apprehends large volumes of raw clickstream data using various models using NoSQL databases. Moreover, it stores the data in a cloud environment for business analysts and data scientists to easily access it. It also helps detect fraudulent activities, data mining, customer categorization, and executing personalization for customers.
NoSQL Examples of Internet of Things:
In 2021, there were 46 billion active IoT devices that include smartphones, appliances, etc. Moreover, these devices support healthcare, manufacturing, finance, and other industries to connect to the internet. Further, the volumes of data the devices constantly produce reach around 847 zettabytes.
Therefore, NoSQL databases help store large sets of diverse data for scalability and processing. Further, Freshub, a smart kitchen application, is a suitable example that helps process data from various IP devices. Moreover, it uses the MongoDB NoSQL database to manage around 1 million products on an online catalog in real-time. It also uses NoSQL to incorporate different data schemas from various sources and devices.
Content Management NoSQL Examples:
Content Management is a great way for businesses to reach their audiences. Moreover, there is a significant increase in competition. As a result, to increase their online sales, businesses must indulge in multimedia content. Therefore, NoSQL databases provide data models that help store multiple content structures. It also helps collect data to serve various business agendas and applications to reach their audiences.
One of the top examples of NoSQL content management is Forbes.com, which strives for views and ad revenues. Moreover, it uses a NoSQL database to manage the plethora of content available on the website. Further, Forbes works with MongoDB to build a custom content management system for better agility, manage contributor content, and social media analysis.
In conclusion, NoSQL examples describe the way various businesses of all sizes and industries leverage their benefits. Moreover, increasing trends in data render businesses investing in an agile and scalable database. Hence, the NoSQL database and its examples help businesses understand their requirements and agendas.
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