Introduction to NoSQL

What is NoSQL?

NoSQL stands for "Not Only SQL." NoSQL is a type of database management system (DBMS) that follows all of the rules of traditional RDBMS systems. It also avoids using traditional SQL syntaxes to retrieve data from the database. This sort of database system is often utilized for huge volumes of data.

When to use NoSQL?

• When a large amount of information must be stored and accessed.
• The relationship of the data you keep is not so important.
• The data varies with time and is unstructured.
• Database support for constraints & joins is not necessary.
• The data is always growing, and the database must be scaled regularly to handle it.

Why NoSQL?

Due to its capacity to manage large volumes of data more effectively than traditional relational databases and its ability to spread the database load across numerous servers, NoSQL databases have become increasingly popular among internet companies. This has prevented expensive hardware upgrades and system slowdowns.

Different NoSQL database types

There are four different types of NoSQL databases available:

1. Document Database
2. Key-Value Database
3. Column Store Database
4. Graph Store Database

Document Database

This sort of NoSQL database always uses a Document-Oriented method to store data. The primary goal of the Document Database is to store all data for a single entity as a document, and all documents can be stored as Collections. Examples of document databases include MongoDB, CosmosDB, CouchDB, and PostgreSQL.

Key-Value Database

Key-value databases are the most simple type of database; as such, they store data without the need for a schema. A key can point to any sort of data, including an object, a string, or any other type of data. These databases' key advantages are their ease of implementation and data entry. Examples include Redis and DynamoDB.

Column Store Database

These databases store data in columns within a keyspace. The key space is always defined by its unique name, value, and timestamp. Example: Cassandra, BigTable, HBase, Vertica, and HyperTable.

Graph Store Database

These databases are specifically built for data that can be simply represented as graph data. This means that data are interconnected by an undetermined amount of data relations, such as family and social relationships. Examples include AllegroDB, GraphDB, OrientDB, and Titan.

Features of NoSQL

• Dynamic schema: NoSQL databases can adapt to changing data structures without requiring migrations or schema modifications.
• Horizontal scalability: The ability to scale out by adding more nodes, allowing them to handle massive data volumes and high traffic.
• Document-based: Some databases, such as MongoDB, store data in schema-less semi-structured forms like JSON or BSON.
• Key-value-based: Other systems, such as Redis, store data as key-value pairs.
• Column-based: Some systems, such as Cassandra, organize data into columns rather than rows.
• Distributed and high availability: NoSQL databases are designed to be highly available, automatically handling node failures and data replication.
• Flexibility: Developers can dynamically store and retrieve data, with support for a variety of data types and structures.
• Performance: Designed for excellent performance, capable of managing large read/write volumes, and suitable for big data and real-time applications.

Advantages of NoSQL

• High Scalability: NoSQL databases use sharding for horizontal scaling, which distributes data across numerous machines while maintaining order. Examples include MongoDB and Cassandra.
• Flexibility: Designed for unstructured or semi-structured data, the data model can accommodate dynamic changes, making it appropriate for applications with changing data requirements.
• High Availability: NoSQL databases provide high availability by auto-replication, which maintains consistent data states in the event of failures.
• Scalability: Highly scalable, capable of managing massive amounts of data and traffic with ease, making it ideal for high-demand applications.
• Performance: NoSQL databases excel at managing big data volumes and traffic, outperforming traditional relational databases.
• Cost-effectiveness: They are typically less expensive than relational databases because of their simpler infrastructure and software requirements.
• Agility: Suitable for agile development procedures that require rapid iterations and dynamic data model updates.

Disadvantages of NoSQL

• Lack of Standardisation: NoSQL databases differ greatly, making it difficult to choose the best one for a certain application.
• Limited ACID Compliance: NoSQL databases do not have full ACID compliance, which affects data consistency, integrity, and durability.
• Narrow Focus: NoSQL databases are primarily built for storage and have limited capability when compared to relational databases, particularly in transaction management.
• Open Source & Variability: Due to a lack of consistent standards and variable features, NoSQL databases are uneven and unpredictable.
• Complex Query Support: NoSQL databases fail to handle complex queries, making them unsuitable for applications that require substantial data processing or reporting.
• Maturity or Reliability: NoSQL databases are newer and less mature than relational databases, which may make them less reliable and secure.
• Management Complexity: NoSQL databases are notorious for their installation and maintenance complexity, making them especially difficult to manage and operate daily.
• GUI Availability: The market lacks flexible GUI tools for NoSQL databases, making database maintenance jobs more difficult.
• Backup Challenges: Some NoSQL databases, such as MongoDB, lack consistent data backup methods, which is a vulnerability.
• Large Document Size: NoSQL databases that store data in JSON format, such as MongoDB and CouchDB, may experience performance and network capacity difficulties.