SQL vs NoSQL

Five Key Differences Between SQL and NoSQL

SQL is a programming language that is used to communicate with relational databases. (Relational databases represent data as rows and tables with logical links between them.) NoSQL is a type of DBM that is non-relational and does not use SQL.

SQL is a programming language that is used to communicate with relational databases. (Relational databases represent data as rows and tables with logical links between them.) NoSQL is a type of DBM that is non-relational and does not use SQL.

There are five key distinctions between SQL and NoSQL

1.Language

2.Language

3.Language

4.Language

5.Language

Language

SQL has been around for more than 40 years, so it is well-known, documented, and widely used. It is both safe and versatile, making it ideal for complex queries. However, SQL confines the user to a predefined tabular schema, and more care must be taken to organize and comprehend the data before it is used.

NoSQL databases dynamic schemata allow for the representation of alternative structures, often alongside one another, encouraging greater flexibility. There is less emphasis on planning, more flexibility when adding new attributes or fields, and the ability to use different syntax across databases. However, because NoSQL languages lack the standard interface that SQL does, more complex queries can be difficult to execute

Despite the fact that there are numerous SQL dialects, they all share a common syntax and nearly identical grammar. Fluency in one language translates to proficiency in the majority of others when querying relational databases. On the other hand, because NoSQL languages cover a wide range of unrelated technologies, there is little consistency among them. Many NoSQL databases have their own data manipulation language that is limited by specific structures and capabilities.

Scalability

The processing power of existing hardware can be increased to scale most SQL databases vertically. NoSQL databases employ a master-slave architecture that scales better horizontally as more servers or nodes are added. These are useful generalizations, but keep in mind:

SQL databases can also be scaled horizontally, though sharing or partitioning logic is frequently left to the user and is not well supported.

While many NoSQL technologies rely on the master-slave architecture, there are also options for scaling vertically. Savings from more efficient data structures can easily outweigh differences in scalability; the most important thing is to understand the use case and plan accordingly

Structure

SQL database schemata always represent relational, tabular data, with consistency and integrity constraints. They have tables with columns (attributes) and rows (records), and the keys have limited logical relationships. NoSQL databases are not required to follow this format, but instead fall into one of four broad categories

Column-oriented databases are a transposition of row-oriented RDBMSs that allow for the efficient storage of high-dimensional data and individual records with varying attributes.

Key-Value dictionaries are dictionaries that access various objects using a key that is unique to each.

Semi-structured data is stored in document stores: objects that contain all of their own relevant information and can be completely different from one another.

Relationships (direct links between objects) are added to documents in graph databases, allowing for rapid traversal of massively connected data sets

Properties

On a high level, SQL and NoSQL follow distinct rules for transaction resolution. RDBMSs are required to have four "ACID" properties

Atomicity implies that all transactions must either succeed or fail completely. Even if the system fails, they cannot be partially completed

Consistency means that the database follows invariants: rules that validate and prevent corruption at each step

Isolation prevents concurrent transactions from interfering with one another. Even if they are run in parallel, transactions must result in the same final state as if they were run sequentially.

Durability seals the deal. Even system failure cannot undo the consequences of a successful transaction.

NoSQL technologies follow the "CAP" theorem, which states that only two of the following properties can be guaranteed in any distributed database

Every request receives either the most recent result or an error. (Note that this differs from ACID.)

Availability: No matter how recent the result, every request has a non-error result.

Partition tolerance: Any delays or losses between nodes will not cause the system to stop working

SQL databases: advantages and disadvantages

Flexible queries: Allows for support of a wide range of workloads. Allows engines to optimize queries to fit on-disk representations by abstracting data over underlying implementations

Reduced data storage footprint: A smaller footprint optimizes database performance and resource usage through normalization and other optimization opportunities.

Data integrity semantics that are strong and well-understood: ACID, or atomicity, consistency, isolation, and durability, are database properties that ensure valid transactions.

Rigid data models: Need to be carefully designed from the start to ensure adequate performance and resistance to evolution. Because SQL has a predefined schema, changing it frequently results in downtime.

Horizontal scalability is limited because it is either completely unsupported, supported ad hoc, or only supported on relatively immature technologies.

Single point of failure: Replication and failover techniques mitigate single point of failure in non-distributed engines.

NoSQL databases: advantages and disadvantages

Many NoSQL databases are designed to support seamless, online horizontal scalability without significant single points of failure

Flexible data models: Most non-relational systems do not require developers to commit to data models in advance. Because existing schemas are dynamic, they can frequently be changed "on the fly."

Dynamic schema for unstructured data: Documents can be created without first defining their structure, allowing each to have its own unique structure. The syntax varies by database, and fields can be added as the document is built.

High efficiency: Many NoSQL databases achieve high performance despite having a limited database functionality range (e.g., by relaxing durability guarantees).

High-level data abstractions: NoSQL systems provide high-level APIs for powerful data structures in addition to the "value in a cell" data model. Redis, for example, includes a native-sorted set abstraction

ACID constraints with ambiguous interpretations: Despite popular belief that it supports NoSQL systems, ACID interpretations can be too broad to make definitive decisions about database semantics.

There are problems with distributed systems: Though not unique to NoSQL systems, such issues are common among NoSQL developers and may necessitate SME troubleshooting

Inflexibility in access patterns: The on-disk representation of data leaks into the application queries without the abstraction found in relational databases, leaving no room for NoSQL engines to optimize queries

When should you use SQL vs NoSQL in your business?

NoSQL is generally preferred for

Data that is graphed or hierarchical. Data sets that are both large and mutate significantly, businesses that are expanding rapidly but lack data schemata. This could be applied to social networks, online content management, streaming analytics, or mobile applications.

SQL is more appropriate when the data is as follows

Small. Conceptually, the model is tabular. When consistency is essential in systems. Consider small business accounting systems, sales databases, or transactional systems such as e-commerce payment processing. When in doubt, SQL is preferable because RDBMSs are better supported and fault-tolerant.