Hackernoon logoSQL Databases Vs. NOSQL Databases by@vijay-aneryae

SQL Databases Vs. NOSQL Databases

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@vijay-aneryaeVijay Aneraye

The decision to choose a database for project is not that simple. But when it comes to choosing a database, the biggest decisions is picking a relational (SQL) or non-relational (NoSQL) data structure.
When we say, ‘SQL vs NoSQL, the primary need becomes, to understand the basic meaning of both these terms.These are key differences between the NoSql and SQL that xusers must keep in mind when making a decision.

SQL databases

  • SQL databases are primarily called as Relational Databases (RDBMS)
  • SQL databases use Structured Query Language (SQL) for defining and manipulating data.
  • A relational database defines relationships in the form of tables
  • SQL databases have predefined schema
  • It uses a single uniform language (DDL) for different roles (developer, user, DBA).
  • It uses a single standardized language for different RDBMS.

The Scalability:

  • In almost all situations SQL databases are vertically scalable. This means You can manage increasing load on a single server by increasing things like RAM, CPU or SSD.

Property followed:

  • SQL databases follow ACID properties (Atomicity, Consistency, Isolation and Durability) thus guaranteeing stability, security, and predictability both of the entire database and every transaction in particular.

When use SQL

  • If your data is primarily structured, a SQL database is likely the right choice.
  • Relational database is unsuited for large datasets and big data analysis.
  • A SQL database is a great fit for transaction-oriented systems such as customer relationship management tools, accounting software, and e-commerce platforms. Each row in a SQL database is a distinct entity (e.g. a customer), and each column is an attribute that describes that entity (e.g. address, job title, item purchased, etc.).

Limitations

  • it can be restrictive. SQL requires that you use predefined schemas to determine the structure of your data before you work with it. In addition, all of your data must follow the same Structure. This can require significant up-front preparation)
  • Change in the structure would be both difficult and disruptive to your whole system.
  • Scaling is problem with SQL, it is very difficult to scale as much as a database grows larger. Sharding is quite problematic as well.

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NoSQL databases:

  • NoSQL database are primarily called as non-relational or distributed database.
  • NoSQL database have dynamic schemas for unstructured data, and data is stored in many ways.
  • NoSQL databases are document based, key-value pairs, graph databases or wide-column stores . This flexibility means that
  • You can create documents without having to first define their structure
  • Each document can have its own unique structure
  • The syntax can vary from database to database, and
  • You can add fields as you go.

The Scalability:

  • NoSQL databases are horizontally scalable This means that you handle more traffic by sharding, or adding more servers in your NoSQL database.

Property followed:

  • NoSQL database follows the Brewers CAP theorem (Consistency, Availability and Partition tolerance).

When use SQL:

  • Data which need a flexible schema
  • Constraints and validations logic not required to be implemented in database
  • Logging data from distributed sources
  • NoSQL databases are easily scalable, flexible and simple to use as they have no rigid schema. They are ideal for applications with no specific schema definitions such as content management systems, big data applications, real-time analytics, etc.
  • A NoSQL database is a much better fit to store data like article content, social media posts, sensor data, and other types of unstructured data that won’t fit neatly into a table

Limitations:

  • Only limited community support is available for NoSQL databases.
  • Security: NoSQL databases security is not as robust as relational databases security. :
  • NoSQL does not strictly follow ACID properties. NoSQL uses the principle of eventual consistency. This means that if there are no new updates for a particular data item for a certain period of time, eventually all accesses to it will return the last updated value. That’s why such systems are usually described as providing BASE guarantees (Basically Available, Soft state, Eventual consistency) — as opposed to ACID. While this approach greatly increases access time and scalability, it may lead to data loss – the severity of the problem depends on database server support and the quality of application code. In some cases, this issue might be very serious.
  • Data Consistency: Most NoSQL databases do not perform ACID transactions, a tried and true technique for ensuring that data remains consistent across the entire database as it is moved around
  • Lack of Standardization: NoSQL is not a specific type of database or programming interface. The design and query languages of NoSQL databases vary widely between different NoSQL products

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