sql vs nosql

SQL vs NoSQL: Benefits, Challenges, and Use Cases

Modern businesses and software engineers often go through a dilemma of choosing between SQL vs NoSQL. After all, choosing the right database type has considerable ramifications on data operations and scalability prospects.

But, regardless of the business domain and the requirements at hand, it is imperative to weigh the pros and cons of both relational (SQL) and non-relational (NoSQL) databases.

It’s also imperative to consider certain factors for both options, before favoring one specific option.

What are SQL databases?

SQL stands for Structured Query Language. SQL database systems fall into the relational database category. 

For example, the following are some common database providers:

  • MySQL
  • Oracle
  • Microsoft SQL Server
  • PostgreSQL
sql vs nosql

SQL is widely adopted by developers and businesses for creating, maintaining, and manipulating relational data models. SQL can also be used to reduce data illiteracy within organizations, which rely on data for decision making. 

SQL carries this out by abstracting data into sets of tuples on the basis of relations. This allows for a more concise representation of data and its access paths.

 SQL has a time-tested value as a relational database language. Presently, it finds widespread application in its iterations as NewSQL and Distributed SQL.

SQL comes with its own list of pros and cons as well, which merits its use or the lack thereof in your database management approach.

SQL Benefits

  • You can run customized queries that can be easily optimized for any kind of workload
  • SQL reduces your technical footprint which heightens performance; this is possible with its normalization and other forms of optimization features
  • SQL providers highly versatile data integrity semantics for atomicity, consistency, isolation, durability, and other types of database transactions
  • The NewSQL framework aids user transparency and distributed logic. Frameworks such as CockroachDB and Spanner are two excellent examples of Distributed SQL, which are useful for the development of scalable, relational databases

SQL Challenges

  • SQL needs meticulously envisioned schema designs to guarantee performance and scalability. Hitherto changes to the predefined schema can disrupt functionality
  • Horizontal scalability on SQL is challenging and error-prone
  • Failover and replication techniques are used to supplant shortcomings in non-distributive database engines

What are NoSQL databases?

NoSQL or Not-Only SQL rose to prominence as an alternative to SQL database language. Some command examples of NoSQL databases include:

sql vs nosql

The NoSQL techniques make up for the shortcomings in SQL, with their non-relational database systems and query syntaxes. Given NoSQL’s birth, even after SQL was in prominence, the former sought to fix the horizontal scalability, data, and availability model challenges that programmers faced with relational databases.

However, designing non-relational databases is no cakewalk. But, due to its targeted improvements over NoSQL, you can find a steady use for NoSQL in database management in the field of DevOps for cloud, web, mobile, and other emerging technologies.

NoSQL Benefits

  • NoSQL databases are highly appreciated for granting seamless horizontal scalability
  • Highly malleable data models that do not force developers to invest in the schema design beforehand
  • NoSQL technology enables dynamic schema design that can be designed as per your bespoke business needs, on the go. This is particularly helpful when you set out to design databases with unstructured data
  • High performance is guaranteed by its limited database functionalities, such as durability guarantee leniencies
  • NoSQL offers a native-sorted set abstraction and powerful data structures through high-level APIs

NoSQL Challenges

  • ACID interpretations for NoSQL as semantics are too vague in nature
  • NoSQL databases can become larger in size than their SQL counterparts. This is because there are query optimization features available in NoSQL, but no remedy for data duplication
  • Selecting the right NoSQL-based framework is essential because all NoSQL frameworks do not fall into the one-size-fits-all description

SQL vs NoSQL: A structured comparison

sql vs nosql

Businesses must always carefully suss the type of database they choose, because no two business instances will have the same parameters and requirements. Hence, making this critical decision requires a data-driven approach to ensure the chosen option provides you an optimal ROI at all given times.

SQLNoSQL
OriginIn the 1970sIn the late-2000s
Data StorageFixed number of rows and columns for tablesDocument: JSON
Key-value: Key-value pairs
Wide-column: Tables with dynamic rows and columns
Graph: Noodles and edges
SchemasRigidVersatile
ScalabilityVerticalHorizontal
ACID TransactionsSupported by defaultOnly selective frameworks support multi-record ACID transactions
JoinsRequiredNot required
Data to Object MappingNeeds object-relational mappingNot usually dependent on ORMs, as most frameworks map directly to data structures
PurposeGeneral purposeDocument: General purpose
Key-value: Handling large blocks of data with simple query lookups
Wide-column: Large amounts of data with predictable queries
Graph: Analyzing and traversing relationships between related data

Choosing Between SQL vs NoSQL

In the interest of making well-informed data-driven decisions, businesses and developers need to select a database type which is based on their current requirements.

 An in-depth analysis of what drives either type of database decisions can help you  infer the following:

SQLNoSQL
Data structureSQL is better for handling structured data SQL databases are an ideal option for transaction-oriented systems that work with distinct entities Excellent ACID compliance that guarantees transactional data integrityNoSQL is better for handling unstructured or semi-structured data NoSQL does not rely on a predefined schema for data structures, like SQL. Hence, NoSQL has proved to be a better option for services with dynamic structures, dynamic databases, and storing multiple documents with dynamic structures and syntaxes.NoSQL is too vague and requires broad interpretations for ACID-compliant database statements 
Data query requirementsStructured data demands structured queries; which is where SQL is quite helpful SQL data queries are declarative and can be easily memorized Data queries are semantically verbose. This is caused by the differences in data structures NoSQL data queries require extra processing. In some instances, it may be subverted by building querying functionality into the application layer instead of the database layer
Scalability requirementsSQL databases are single server-oriented. This means they will have to be scaled vertically, while incrementing the server’s hardware capabilities.This helps with the integrity of the structured data; SQL databases are accustomed to such structures    Given their unstructured and semi-structured data tendencies, NoSQL servers usually rely on horizontal scalability, i.e., multi-server growth. This further helps the case with storing non-relational dataNoSQL follows the BASE consistency model that offers some trade-off return for NoSQL’s lack of ACID compliance  
Best use cases– Data that has a strong relational definition
– SQL data requires a highly navigational database with a flexible query system
– There is a minimal scope to write and work while sharing data accesses and storage
– Ideal for business environments where vertical scalability is the only way forward
– For bulk, unstructured data
– NOSQL data works well with data types which don’t fit into any relational archetype
– Documental databases, along with databases that feature key-value storage, without involving stringent integrity compliance requirements
– Suitable for business environments where horizontal scalability is a better option, provided there is a easy availability of data requirements

Kloudio and SQL

As a business intelligence organization, there are a few things Kloudio excels in. One of the primary things is getting you the required expertise to run SQL codes within Google sheets, to pull the required datasets from various databases, without having to write a single line of code. 

That’s right; if this surprised you, then you are in for a treat. There are various instances wherein you would be forced to write multiple lines of code spanning over pages, just to collect the required data to assist you in your reporting. 

With Kloudio’s Ad Hoc Query Feature, there is not much to do. You install the add-in, and you can run, and pull data within no time at all. Connect your Google sheets with Kloudio’s Ad Hoc Query Feature add-in, and you will be ready to download any form of data into your localized Google sheets. 

Depending on the need of the hour, you can choose between SQL vs NoSQL providers for your business needs. Despite its inherent limitations, SQL continues to be one of the most preferred tools for data wrangling and data manipulation. 

Thankfully, most database frameworks these days incorporate the best qualities of the top data tools in the market. 

To understand the true functionalities of SQL and Kloudio’s Ad Hoc Query Feature, you have to create a free account for your business, and test out the features. 

Try our Ad Hoc Query Feature

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