SQL vs NoSQL databases

Wondering whether to use SQL vs NoSQL databases to develop your digital product? Both are important in the software development landscape. Read on, as we compare SQL databases against NoSQL databases. You can then make an informed decision.

The key characteristics of SQL databases

Although we commonly refer to a type of database system as “SQL databases”, SQL (Structured Query Language) is a programming language. Precisely, we want to refer to database management systems that use the relational model.
Edgar. F. Codd, a mathematician working with IBM had come up with the idea of the relational model of databases in 1970. The objective was to enable users to retrieve data without a high degree of computer and programming expertise.
IBM DB2 became a popular relational database management system (RDBMS). SQL became an industry-standard language for retrieving data from RDBMSs. The software development industry identified SQL and RDBMSs very closely, therefore, we commonly call relational databases “SQL databases”.
RDBMSs have the following key characteristics:

1. Structured data

RDBMS stores structured data only. Data structures in a relational database consist of rows and columns. RDBMS terms refer to rows as “entities” and columns as “attributes”.
An RDBMS table consists of multiple rows with a fixed set of columns. SQL databases require that you define the data types of columns upfront.
You can have related data in another table in the same database. However, you need to define the relationship upfront. The relationships between two tables must follow certain relational database rules. E.g., a foreign key in one table must be a primary key in another table. Also, a primary key in a table can’t contain null values.

2. Structured Query Language (SQL)

A few variations of SQL exist. However, they all follow a set of basic principles and rules. You need to use SQL to communicate with relational databases. SQL is a simple and English-like language. You can learn it easily.
You need to exercise care when organizing data in an RDBMS. Creating a predefined schema requires thoughts and debates. When there are multiple tables, you need to define their relationship carefully.
Subsequently, querying relational databases using SQL is easy. SQL also supports complex queries.

3. The focus on eliminating data duplication

Relational databases emerged at a time when computer data storage was expensive. Naturally, the creators of RDBMSs wanted to prevent data duplication. They invented concepts like normalization to reduce data redundancy.

4. Relational databases are generally vertically scalable

The design of most SQL databases is such that you can scale them vertically. In this approach, you increase the computing resources and processing power of the existing hardware.
This doesn’t mean that you can’t scale RDBMSs horizontally. You can create partitions in a relational database. However, managing partitions in an RDBMS can be complex.

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Relational databases need to exhibit “ACID” properties

SQL databases need to exhibit 4 properties, which are commonly known as “ACID”. These are as follows:

• Atomicity: Transactions must succeed completely, alternatively, they must fail completely. You can’t have a partially complete transaction in an RDBMS.
• Consistency: SQL databases follow a set of rules to ensure data integrity. Such databases must follow them at all times.
• Isolation: An RDBMS must prevent concurrent transactions from affecting each other.
• Durability: A successful transaction is always final. You can’t reverse the effects of a completed transaction. You know that your critical data is safe.

The pros and cons of SQL databases

Relational databases offer the following advantages:

• Popularity: Relational databases emerged in the 1970s. They represented considerable improvements over hierarchical data storage solutions. Many organizations of all kinds used SQL databases for managing data. All traditional software applications across all industries use RDBMSs. Therefore, you know what to expect.
• Assurance: Relational databases are “ACID”-compliant, therefore, your critical data is safe.
• Versatility: Relational databases support a wide range of use cases, e.g., transaction processing systems, reporting systems, data warehouses, analytics/big data.
• Established RDBMSs: Whether you want a proprietary or open-source RDBMS, you can choose from many established RDBMSs.
• Ease of programming relational data: SQL and the ability to create data models make life easier for developers.
• Availability of developers: Many developers know RDBMSs. You can find developers easily. You also find excellent community support.
• The disadvantages of SQL databases are as follows:
• The rigid structure of SQL databases makes it hard to rapidly change applications.
• SQL databases can process structured data only.
• There can be performance degradation due to high data or transaction volume.

The key characteristics of NoSQL databases

NoSQL databases are non-relational databases. The term “NoSQL” can mean “non-SQL” or “not only SQL”.
These databases emerged in the late 2000s. The creators of NoSQL databases emphasized scalability and fast queries. They also wanted to reduce the rigidity, which can make it easier to modify apps frequently.
The key characteristics of NoSQL databases are as follows:

NoSQL databases support unstructured and semi-structured data

NoSQL databases don’t follow a rigid table-row-column structure to store data. They can store various data types. These databases can be of the following types:

• Column-oriented databases: Column-oriented databases fundamentally vary from row-oriented relational databases. They allow you to store individual records with varying attributes.
• Key-value stores: These databases have diverse objects, and each object has a unique key.
• Document databases: Document-oriented databases can hold semi-structured data. Each object in such a database can be entirely different from another object.
• Graph databases: These databases can help you to track relationships and direct links between objects.

Varied languages for developers to query data

You don’t have one standard query language for all NoSQL databases. A NoSQL database technology provider can offer its own query language. E.g., MongoDB offers MongoDB Query Language (MQL).
Query languages for NoSQL databases might or might not follow the SQL syntax. E.g., Apache Cassandra offers CQL (Cassandra Query Language). CQL has similarities with SQL languages. Other NoSQL database query languages might not follow this pattern though. Query languages for NoSQL databases don’t offer a standard interface like SQL either.

NoSQL databases are generally horizontally scalable

Most NoSQL databases support horizontal scaling. You can add multiple servers to cater to increased demands. However, some NoSQL database management systems support vertical scaling too.

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NoSQL databases need to conform to the “CAP” theorem
The “CAP” theorem states that a distributed database can demonstrate only two of the three specific properties simultaneously. These properties are as follows:

• Consistency: A request will receive the most-recent result or an error.
• Availability: A request will have a non-error result, even if the result isn’t a recent one.
• Partition tolerance: Losses or delays between different nodes will not adversely impact the operations of the database system.

The pros and cons of NoSQL databases

NoSQL databases offer the following advantages:

• They are decentralized;
• NoSQL databases are flexible and easy to use.
• They handle semi-structured and unstructured data in addition to structured data.
• NoSQL databases offer high scalability, performance, and availability.
• These databases can support high transaction and user volume, and they are good for big data applications.
• NoSQL databases have the following disadvantages:
• They don’t offer atomicity, which adversely impacts the accuracy of data.
• The design and structure of NoSQL databases can vary widely. You see a lack of standardization. This can make it hard to change the database when the need arises.
• You can’t use a standardized query language like SQL for different NoSQL databases.
• Although NoSQL databases are gaining popularity, they are still relatively new. You get fewer frameworks and tools. While the developers’ communities for NoSQL databases are growing, they are still relatively small.

A comparison of SQL and NoSQL databases

When comparing SQL and NoSQL databases, the following key differences are notable:

• Relational databases are well-established and highly popular. NoSQL databases are becoming popular, however, they are still relatively new.
• Relational databases can support structured data. NoSQL databases can support structured, semi-structured, and unstructured data.
• SQL databases use a rigid table-row-column structure. They must follow important rules to preserve data integrity. NoSQL databases don’t follow such a rigid structure, therefore, they are flexible.
• RDBMSs must exhibit “ACID” (atomicity, consistency, isolation, and durability) properties. This ensures data integrity. NoSQL databases adhere to any two of the three CAP (consistency, availability, and partition tolerance) at one time. Availability is more important than atomicity in NoSQL databases.
• Compared to SQL databases, NoSQL databases offer higher performance and scalability when handling high data and user volume.
• You can use SQL to query any relational database. Different NoSQL databases have their own query languages.
• You can find more developers, frameworks, tools, and community support for SQL databases than for NoSQL databases.

When to use relational database management systems vs NoSQL database systems

You should use an RDBMS in the following circumstances:

• You are building a traditional transaction processing, reporting, or analytics application;
• When you need to process structured data that have relations between data elements;
• You want to ensure data validity and consistency;
• When you need flexible and easy access to data;
• You want atomicity, isolation, and durability of your critical data;
• You want to scale the app when needed.
• Consider using a NoSQL database in the following circumstances:
• You need to process semi-structured and unstructured data;
• Your application needs flexible data models;
• When your app might need to use different data structures that don’t fit the relational database model;
• If you have very high availability and scalability requirements;
• When you need more flexibility than what SQL databases offer.

You can use an RDBMS and a NoSQL database in the same project, and you might need to do that in many cases.

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