What Is a NoSQL Database?

• NoSQL Definition

  NoSQL Definition

  A NoSQL database is used to store and retrieve data in a non-tabular format compared to relational tables. NoSQL databases include document, key-value, wide-column, and graph.
• Differences between relational databases and NoSQL

  Differences between relational databases and NoSQL

  NoSQL Database

  A NoSQL database is a non-relational database that helps store and retrieve huge volumes of data. The major purpose of using a NoSQL database is to store distributed data that requires humongous storage needs. The use of NoSQL is increasing over time. It’s used in Big Data and real-time web applications and by Twitter, Facebook, and Google to collect and store terabytes of user data generated every day. NoSQL database systems store structured, unstructured, semi-structured, and polymorphic data. A NoSQL database is sometimes preferred over a relational or SQL database due to its simplicity of design, finer control over availability, and simpler horizontal scaling to clusters of machines.

  Why NoSQL?

  The concept of NoSQL databases became popular with internet giants such as Twitter, Google, Amazon, and more who generate huge volumes of data every day. However, if you use a relational database to store and collect such huge volumes of data, your system response time can get hampered. This problem can be resolved by scaling your systems or upgrading your hardware. However, the process can be expensive. Another way is to distribute database load on multiple host servers whenever required. This method is known as scaling-out. NoSQL databases use this method to handle huge data loads. A NoSQL database is best suited:

  • To store a huge amount of data
  • When the data is unstructured and changes over time
  • When you have to handle continuously growing data

  Features of NoSQL

  Distributed

  • Multiple NoSQL databases can be arranged in a distributed fashion
  • NoSQL follows shared-nothing architecture that enables less coordination and higher distribution
  • Provides eventual consistency
  • Offer capabilities such as auto-scaling and fail-over

  Non-relational

  • NoSQL is also known as a non-relational database and never follows a relational data model
  • It doesn't require data normalization and object-relational mapping
  • It can store different data types such as structured, unstructured, semi-structured, and more but not in a tabular format
  • It doesn’t include complex features, query languages, ACID (Atomicity, Consistency, Isolation, and Durability), query planners, referential integrity joins, and more
  • It works with self-contained aggregates or BLOBs

  Schema-free

  • NoSQL databases are either schema-free or have relaxed, flexible schemas
  • They can include different forms of data structures in the same domain
  • These databases don’t have any particular definition of the schema of the data

  Simple API

  • NoSQL has a simple API that allows low-level data manipulations and selection methods
  • It uses text-based protocols, mostly HTTP REST with JSON
  • It doesn’t require any specific query language, also known as NoSQL query language
  • It provides an easy-to-use interface for storage and querying data

  What is a SQL database?

  SQL databases are the traditional databases that use SQL (Structured Query Language) to store and query data. In these databases, data is stored in relational tables with fixed columns and rows.

  Relational database vs. NoSQL

  Choosing a database can be tricky as both relational and NoSQL databases are viable options. However, they have certain differences based on which you can choose the database best suited to your data storage needs.

  • Language: SQL databases use a powerful, versatile, and widely used language known as Structured Query Language (SQL) to define and manipulate data. Although it can resolve complex queries, it can be restrictive at times as it requires a predefined schematic representation to determine the data structure. Your data must follow the same structure. This arrangement of data requires significant upfront preparation and can disrupt your entire database if there’s an abrupt change in the structure.
    NoSQL databases have a dynamic schema and don’t require structure to store data. They can store unstructured data in several ways, such as document-oriented, graph-based, column-oriented, or organized as a KeyValue store. It offers flexibility as documents can be created without defining the structure. Every document can have its own layout or schema, and you can add fields as needed.
  • Structure: SQL uses relational database tables to store structured data, whereas NoSQL can store unstructured data in several ways, such as document-oriented, graph-based, column-oriented, or organized as a KeyValue store.
  • Scalability: SQL databases are vertically scalable, which means you can increase the load on a single server by adding resources, such as SSD, CPU, RAM, and more. NoSQL is horizontally scalable and can handle huge traffic volumes by sharding or adding more servers to your database, making NoSQL more powerful.
  • Features: NoSQL databases follow the Brewers CAP theorem (Consistency, Availability, and Partition tolerance), whereas SQL databases follow ACID (Atomicity, Consistency, Isolation, and Durability) properties.
• What are the types of NoSQL databases?

  What are the types of NoSQL databases?

  • Document databases: These databases store the data in the form of a document similar to JavaScript Object Notation (JSON) objects. Documents include fields and values that can be of different types, such as numbers, Boolean, arrays, strings, objects, and more. Document databases use powerful query language and a variety of file and value types. These databases can horizontally scale out to accommodate large and complex data volumes. MongoDB is one of the best NoSQL database examples.
  • Wide column stores: These databases store data in tables, rows, dynamic columns. Wide column stores are more flexible than relational databases. They organize data and its related facts into columns. These databases are also known as column families, columnar databases, or column-oriented DBMS. These are ideal for large datasets that need to be distributed across multiple database nodes.
  • Key-value database: These databases are simpler and use key-value methods to store data. In a key-value database, the key serves as a unique identifier. A value can only be retrieved with the help of its key. Redis and DynamoDB are popular key-value databases. These databases are highly partitionable and allow horizontal scaling.
  • Graph databases: These databases use graphical structures to determine the relationship between datasets. They use semantic queries with edges, nodes, and properties to represent and store data. Nodes store information about people, places, and things, while edges store information about the relationship between nodes. This type of database is useful when looking for relationships and patterns such as fraud detection, social networks, and more.
• Benefits and disadvantages of NoSQL databases

  Benefits and disadvantages of NoSQL databases

  Advantages of NoSQL databases:

  • Provide easy replication
  • Offer fast performance and horizontal scalability
  • Can handle semi-structured, structured, and unstructured data
  • Easy to implement
  • Support key developer languages and platforms
  • Don’t require a dedicated high performing server
  • Capable of handling big data
  • Offers a flexible schema design

  Disadvantages of NoSQL databases:

  • Limited query capabilities
  • Steep learning curve for beginners
  • Less open-source options

  Whether you use a relational database or a NoSQL database, you need to regularly monitor database performance to ensure they’re performing well. NoSQL database management systems or monitoring tools provide deep database performance monitoring at scale to provide complete visibility into SQL and NoSQL databases. This helps enhance the system performance and team efficiency and saves infrastructure costs. Additionally, tools offer comprehensive database performance analytics, a unified view of your database types and servers, meaningful summaries, weekly and daily reports, and customizable alerts.