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SQL vs NoSQL Databases

database is an organized collection of data, generally stored and accessed from a computer system. There are two major types of databases available: SQL and NoSQL. Both of them are powerful & popular and only differ in the way how they store and process data. Before comparing the differences between SQL and NoSQL databases, let’s first briefly understand what they actually are.

SQL – Structured Query Language(SQL) are languages for defining and inserting, deleting, and updating data in relational databases such as MySQL, Oracle, Postgres, etc. It is very helpful in handling structured data.

NoSQL – NoSQL (“non-SQL” or “not only SQL”) databases or non-relational databases that provide a way of storing and retrieving data in means other than tabular format provides by relational databases. It is very helpful in handling unstructured data.

Difference between SQL and NoSQL

  1. Type – SQL databases are primarily Relational Databases.
    NoSQL databases are distributed or non-relational databases.
  2. Storage – In SQL databases the data is stored in tabular format, and the data is organized in a row-and-column format and each row is unique.
    NoSQL databases don’t follow a table-based data storage model instead they have a very flexible schema. The data can be stored in key-value, document, graph, and wide column format.
  3. Schema – SQL databases have a fixed predetermined schema to determine the structure of your data before you work with it. Every time data is inserted it should follow the predefined schema. The structure of schema can be altered but it is difficult and also the entire database suffers downtime.
    NoSQL schema on other hand has a very dynamic schema for storing unstructured data. It means additional columns can be added anytime and every row doesn’t have to follow the same structure.
  4. Querying Language – SQL databases use Standard Query Language for defining and manipulating the data.
    Opposed to SQL database databases, the NoSQL databases don’t have any fixed schema for storing data. Hence they use Unstructured Query Language(UnQL). Unlike SQL, different databases have different syntax for UnQL.
  5. Property – SQL databases follow ACID(Atomicity, Consistency, Isolation, and Durability) properties.
    NoSQL databases follow the CAP (Consistency, Availability, and Partition tolerance) theorem.
  6. Scalability – SQL databases are mostly vertically scalable which means we can increase the performance of a single server by increasing various components like RAM, SSD, and sometimes processors, but the major challenge with this approach is that increasing the CPU components gets very expensive.
    The NoSQL databases are horizontally scalable which means we can easily shard the database or add more servers hence our NoSQL infrastructure can easily handle a lot of traffic. Moreover, horizontal scaling is cheaper as compared to vertical scaling. This ability to become larger and powerful at scale makes the NoSQL databases the preferred choice for large or ever-changing data sets.
  7. Example – Most popular SQL databases are – MySQL, Oracle Database, Microsoft SQL Server, PostgreSQL.
    The most popular NoSQL databases are – MongoDB, Cassandra, Google Cloud BigTable, Apache HBase.

Which one to Choose?

Both SQL and NoSQL databases are popular and have their own merits and demerits, hence to determine which database is best for your organization can be challenging.

Use SQL when
  1.  The data is unchanging and structured.
  2. A SQL database is a great fit for transaction-oriented systems such as customer relationship management tools, accounting software, and e-commerce platforms.
  3. You need to ensure ACID compliance.
  4. Preferred when you want to use joins and execute complex queries.
Use NoSQL when
  1. Data is changing and unstructured and is present in large volumes.
  2. ACID property is not needed.
  3. Data is highly distributed and you need to scale the traffic with zero downtime.
  4. Rapid Development is required because it doesn’t need to be prepped ahead of time.
  5. Support for a new application paradigm is required.

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