What is NoSQL and How is it Utilized?

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What is NoSQL?

A NoSQL or a NoSQL Database is a term used when referring to a “non SQL” or “not only SQL” database. NoSQL databases store data in a different format than a traditional relational database management systems. This is why NoSQL is often associated with the term “non-relational” database. Simply put, NoSQL databases are modern databases with high flexibility, blazing performance, and built for scalability. These databases are used when you require low latency and high extensibility while working with large data structures. The versatility of NoSQL is due to the nature of as being unrestricted in comparison to relational databases models such as MySQL or DB2. 

SQL vs. NoSQL Comparison

There are multiple differences between SQL and NoSQL database types. In the table below, we will compare some of the most critical variations. 

SQL  NoSQL
Relational data structure databases Non-relational data structure databases
Better for multi-row transactions  Better for documents and JSON files
Databases based on tables Databases are documents, key-value, graph or wide-column stores
SQL databases are vertically scalable  NoSQL databases are horizontally scalable 
Uses predefined schema Uses dynamic schema
Structured Query Language (SQL) No query language

NoSQL Databases Types

There are four main types of NoSQL databases. 

  • Key-Value data stores
  • Document stores
  • Wide-Column stores
  • Graph stores 

We will review each of these types and explain what they are used for. 

Key-Value Data Stores

These types of databases are using key-value as a method to store data within the database. Each key-value is unique and serves as an identifier when a query is performed. The primary key is separated into a partition key and sort key. For example, the partition key would have product ID stored, and the sort key would have product types stored.

Stored values in these databases can be anything from the simplest binary objects such as JSON documents to videos or other massive complex items. Our application will have complete control over these values, and that is why Key-Value Data Stores are considered the most flexible NoSQL model. It is essential to mention that key-values often do not support transactions because data that is stored in them is partitioned and used across whole clusters.

Document Stores

As the name implies, a NoSQL database is a non-relational schema-free database that stores and queries data in a document similar to a JSON file. Unlike conventional MySQL tables, document store databases collect all the data from a given entity into a document. All data associated with that entity is stored in that single document as well.

So on one side, you have relational databases storing data of the commodity into tables, and that data is spread across several tables. On the other hand, you have a document store non-relational database which stores all the data into a single file. 

Wide-Column Stores

The second name for these databases is Extensible Record Stores. Like document stores, these wide-column databases are also schema-free databases. This database uses tables, rows, and columns, like a relational database.

But there is one difference! None of the columns or record keys are fixed, which means that name and format can vary. That is why wide-column databases are often seen as two-dimensional key-value stores.

Graph Stores

Graph Stores databases are constructed around a simple data structure:

Node — Relationship — Node

These databases have three types of data fields. These fields are called vertices and are composed of nodes, edges, and properties. These fields are used to store and represent the data within the Graph stores. 

Graph stores work by collecting data within a node, and edges store the relationship between each node. Each edge has four components: a start node, an end node, a type, and a direction. This structure is needed because one node can have an unlimited number of relationships. 

The final type of data field, properties, stores features, and additional information related to edges and relationships. One example of a property is called “Weight of an Edge.”This property can save a cost, distance, range, length, or any other measure of a relationship between two nodes.

NoSQL System Examples

Now that we know which types of NoSQL databases we have, we will review some of the most used database management systems for each of these types.

Database type Most used management system
Key-Value Data Stores Redis, is by far the most used NoSQL management system in general. Redis can be used for other operations such as caching, but database management is its primary function. 

 

 

Document Stores MongoDB is advertised as the “Most popular database for modern applications”. MongoDB is actually quite a bit more than just a database management system. It’s a whole data platform with many tools for developers and data scientists to use. 

 

 

Wide-Column Stores Apache Cassandra is used by Activision, Hulu, Uber, Walmart and dozens of other high-profile companies. According to the official page, 40% of Fortune’s Top 100 companies are using Cassandra in their day-to-day operations. The reason for this is simple. Cassandra is the best choice when you need a management system for a wide-column store that offers the best scalability and availability without compromising performance. 

 

 

Graph Stores Neo4j is a native graph database, built from the ground up to leverage nodes and relationships. Neo4j makes connections between data as it is stored, enabling queries types never imagined, at speeds rarely thought possible.

 

 

We now know the different types of NoSQL databases. We also know the basics behind them. Finally, let’s review what would be the best way to put each of these databases into practice. 

Best Use Cases for NoSQL Databases

  • Key-value store databases: These databases are best used for various shopping carts. This is simply because Key-Value databases can handle millions or billions of orders. Massive incoming data is processed without performance loss. These databases also have built-in redundancy so you won’t have to worry about data loss.
  • Document store databases: These databases are best used for various catalogs. This can again be connected to your e-commerce business, where you need to store thousands of different attributes about our products. Since data is stored in a single document, the management of products is fast and easy.
  • Wide-Column Stores: These databases are best used for geographic information, reporting systems, sensor logs, and such. We state this because Wide-Column stores use multi-dimensional mapping (row-value, column-value, and timestamp) in a tabular format meant for massive scalability. If you ever use a website or app to check the driving distance from A to B on a map, chances are pretty high that Wide-Column stores are in use on that website or application.
  • Graph store databases: These databases are best used for above-average fraud detection systems. Let’s say that we have a known fraud case on file for a specific email or credit card in question. If that person tries to log in and purchases something again with the information from that fraud case, someone would be notified by the system. That happens because of how the Graph store databases process relationships between nodes in real-time. 

Conclusion

We often encounter the use of NoSQL databases in our daily lives without realizing it. The data shows that the overall advantages of NoSQL and its effectiveness are incalculable. Hopefully, this article provides you with actionable information and how NoSQL can be implemented into your business methodology. In conclusion, we are glad that we could demonstrate how NoSQL can be beneficial to you.

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Data in a MySQL/MariaDB database is stored in tables. A simple way of thinking about indexes is to imagine an extensive spreadsheet. This type of system is not always conducive to quick searching; that’s where an index becomes essential. If there is no index, then the database engine has to start at row one and browse through all the rows looking for the corresponding values. If this is a small table, then it is no big deal, but in larger tables and applications where there can be tables with millions and even billions of rows, it becomes problematic. As you can imagine, searching through those rows one by one will be time-consuming, even on the latest hardware. The solution is to create an INDEX (or more than one) for your data.

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In this article, we will learn the basics of joining tables in SQL. We will learn about Left, Right, Inner, and Outer joins and give examples of how to use them.

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How to Install SQuirrel SQL Client

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What is SQuirreL SQL Client?

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In this article, we will discuss what the SQuirreL SQL Client is, some of its features, the supported database types, and how to install the client.

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