Introduction to NoSQL – Enteros

Introduction to NoSQL – Enteros

NoSQL Databases

Everything that you need to know about NoSQL databases, which provide greater flexibility than traditional databases.

What is a NoSQL database?

NoSQL, often known as “not only SQL” or “non-SQL,” is a database design paradigm that allows data to be stored and queried outside of the typical structures found in database systems. It can still store data from relational database management systems (RDBMS), but it does it in a different way than an RDBMS. The choice between a relational and non-relational database is mostly contextual, and it differs based on the use scenario.

NoSQL is a distributed database, which means that data is duplicated and stored on a number of servers, both remote and local. This ensures data availability and consistency. Even if some of the data is unavailable, the database as a whole can continue to function.

In practically every industry today, firms must manage massive data volumes at high speeds with the flexibility to scale up quickly to run current web applications. NoSQL databases enable speed and scalability in this era of rapid expansion in cloud, big data, mobile, and web applications, making them a popular choice for their performance and ease of use

NoSQL vs. SQL

The structured query language (SQL) is frequently used in conjunction with NoSQL. Understanding the history of SQL, a programming language used to retrieve specific information from a database, may help to better comprehend the distinction between NoSQL and SQL.

​​

Prior to relational databases, businesses relied on a hierarchical database system with data tables organised in a tree-like layout. Users could arrange vast amounts of data with these early database management systems (DBMS). However, they were difficult to use, typically proprietary to a single application, and limited in what they could find in the data. Due to these restrictions, relational database management systems, which organise data in tables, were developed. SQL provides a way for analysts to interact with relational data by allowing them to join tables based on common fields.

For new technology, such as e-commerce applications, the requirement for faster and more diverse utilisation of massive data volumes grew increasingly vital as time progressed. Something more adaptable than SQL databases was required by programmers (i.e. relational databases). That alternative was NoSQL.

While NoSQL offers a viable alternative to SQL, it was never intended to completely replace SQL databases. Let’s pretend you’re in charge of retail orders at a business. Individual tables would manage customer, order, and product data independently in a relational model, and they would be linked together by a unique, common key, such as a Customer ID or an Order ID. While this is useful for swiftly storing and retrieving data, it consumes a lot of memory. SQL databases can only scale vertically, not horizontally, when adding extra memory, which means your capacity to add more memory is constrained by the hardware you have. As a result, scalability limits your company’s data storage and retrieval capabilities.

NoSQL databases, on the other hand, are not relational, hence no connecting tables are required. Horizontal scalability is made easier by their built-in sharding and high availability features. If a single database server can’t process all of your queries or hold all of your data, the workload can be split amongst two or more servers, allowing businesses to grow their data laterally.

While each database type has its own set of benefits, firms frequently combine NoSQL and relational databases in a single application. SQL and NoSQL databases are supported by today’s cloud providers. Which database you use is determined on your objectives.

Types of NoSQL databases

NoSQL offers a variety of additional possibilities for data organisation. NoSQL can be used for data analytics, big data management, social networking, and mobile app development because it provides a variety of data structures.

A NoSQL database stores data in any of the following primary data models:

Key-value Store

This is often thought of as the most basic form of NoSQL database. This schema-free data model is arranged as a dictionary of key-value pairs, with a key and a value for each item. The key may be anything like a shopping cart ID from a SQL database, while the value is an array of data, such as each individual item in that user’s shopping cart. It’s frequently used for caching and storing user session data like shopping carts. When you need to pull numerous records at once, though, it’s not optimal. Open-source key-value databases such as Redis and Memcached are examples.

Document Store

Document databases, as the name implies, store data in the form of documents. They can aid in the management of semi-structured data, which is often saved in JSON, XML, or BSON formats. When the data is used in applications, this keeps the data together, decreasing the amount of translation required. Data schemas do not have to match across documents, giving developers additional flexibility (for example, name vs. first name). However, for complex transactions, this might be troublesome, resulting in data corruption. Content management systems and user profiles are two common uses of document databases. MongoDB, the MEAN stack’s database component, is an example of a document-oriented database.

Wide-column store

These databases store data in columns, allowing users to access exactly the columns they need without wasting memory on data that isn’t important to them. This database attempts to address the drawbacks of key-value and document stores, but it is not advised for usage by newer teams or projects due to its complexity.

Wide-column databases such as Apache HBase and Apache Cassandra are open-source options. Hadoop Distributed Files System provides a mechanism of storing sparse data sets, which is extensively utilised in many big data applications. Apache HBase is built on top of Hadoop Distributed Files System. On the other hand, Apache Cassandra was designed to monitor enormous volumes of data across several servers and clustering that spans multiple data centres. It’s been used for social networking websites and real-time data analytics, among other things.

Graph Store

The data from a knowledge graph is usually stored in this form of database. Nodes, edges, and properties are used to store data elements. A node can be any thing, location, or person. The relationship between the nodes is defined by an edge. A node could be a customer, such as enteros , or an agency, such as Ogilvy. An advantage would be to classify enteros and Ogilvy’s partnership as a customer relationship.

Graph databases are used to store and manage a graph’s network of connections between its constituents. Neo4j (link outside enteros) is a Java-based graph-based database service featuring an open-source community edition where users can acquire licences for online backup and high availability extensions, as well as a pre-packaged licenced version that includes backup and extensions.

In-memory Store

Data is stored in main memory rather than on disc in this form of database, such as enteros solidDB, making data access faster than in traditional disk-based databases.

Examples of NoSQL databases

Many businesses have entered the NoSQL market. Here are several popular NoSQL databases, in addition to those mentioned above:

·         Apache CouchDB is a JSON document-based library that employs JavaScript as its query language and is open source.

·         Elasticsearch is a full-text search engine built inside a document-based database

·         Couchbase is an important and document database that enables developers to create responsive and flexible cloud, mobile, and edge computing applications.

Advantages of NoSQL

Each form of NoSQL database has advantages that make it more suitable for various applications. They all, however, have the following advantages for developers and provide the basis for better customer service:

Cost-effectiveness: Maintaining a high-end commercial RDBMS is costly. To scale vertically, they require the purchase of licences, qualified database managers, and strong hardware. NoSQL databases allow you to extend horizontally quickly, allowing you to better allocate resources and reduce expenses.

Flexibility :  Horizontal scaling and a flexible data schema enable NoSQL databases to handle massive volumes of rapidly changing data, which makes them ideal for agile development, rapid iterations, and frequent code pushes.

Replication : NoSQL replication is a feature that copies and saves data across several servers. This replication ensures data security by ensuring access during downtime and preventing data loss if servers go down.

Speed: NoSQL allows all users, from developers to sales teams to customers, to store and process data faster and more agilely. NoSQL databases are also faster, making them a better fit for modern, complicated online applications, e-commerce sites, and mobile apps.

NoSQL  use cases

The structure and type of NoSQL database you select will be determined by the way your company intends to use it. Here are some examples of specialised NoSQL database applications.

Managing data relationships:

A graph-based NoSQL database is often used to manage the complicated aggregation of data and the links between these points. Recommendation engines, knowledge graphs, fraud detection apps, and social networks are examples of programmes that use multiple data kinds to connect individuals.

Low-latency performance: 

High throughput is required for real-time data management in gaming, home fitness apps, and ad technologies. Whether it’s market bidding updates or returning the most relevant advertisements, this infrastructure gives the most value to the consumer. In-memory NoSQL databases are required by web applications to provide fast response times and manage spikes in usage without the latency that disc storage can cause.

Scaling and High Data Volumes: 

Whether it’s for a one-day sale or the Christmas shopping season, e-commerce demands the capacity to handle massive surges in usage.

Because of their simple structure, key-value databases are commonly employed in e-commerce systems. They can readily scale up during times of high traffic. Gaming, adtech, and Internet of Things (IoT) applications all benefit from this agility.

Microservices and NoSQL Databases

Microservices have grown in popularity as a result of large firms’ need to provide services with low latency and expand more quickly, prompting companies to consider which database to employ for certain applications.

Companies have discovered that employing a single, relational database for all programme components has drawbacks, especially when better solutions exist for certain components.Microservices are appealing because they eliminate the need for a complete application’s data to be stored in a single, common data store. Instead, the application is made up of a number of loosely coupled, independently deployable services, each with its own data model and database, and all of which are connected through API gateways or an iPaaS.

Polyglot persistence, or the use of many databases within a single application, has helped to provide room in the market for NoSQL databases to prosper. Rather of trying to make everything function in the framework of a single, database system, developers may now use the proper database for the right microservice.

NoSQL and Enteros Cloud

Many apps are now supplied as services, and such services must be available 24 hours a day, 7 days a week, across a wide range of devices, and scaled to millions of users. Enteros services and partners cater to a wide range of data requirements, allowing businesses to adapt swiftly.

NoSQL was established to address the scalability and agility issues that modern applications confront, but a database’s applicability is determined by the problem it must answer. Because SQL and NoSQL are each suited to different use cases, deciding which technology to utilise is more dependent on your goals. Furthermore, SQL systems such as PostgreSQL have been bridging the gap between NoSQL and SQL in recent years by providing JSON support or scale-out features. Enteros delivers enterprise-ready, fully managed PostgreSQL designed with native integration into the enteros Cloud with enteros Cloud Databases for PostgreSQL.

Enteros Cloudant, Databases for MongoDB, Databases for Redis, and Databases for Elasticsearch are all feature-rich open source NoSQL databases.

Enteros Data Management Platform for MongoDB Enterprise Advanced, which can be integrated into your existing data management solution for x86, enteros Power, and enteros Z environments, is a modern database platform built for mission-critical, highly secure, and highly available deployments.

Cloudant from enteros is a scalable JSON document database for online, mobile, IoT, and serverless applications. Apache CouchDB, PouchDB, and libraries for the most popular online and mobile development stacks are all compatible with the service.

Enteros is a scalable JSON document database optimized for web, mobile, IoT,and serverless applications. The service is compatible with an open source ecosystem that includes Apache CouchDB, PouchDB, and libraries for the most popular web and mobile development stacks.