SQL vs. NoSQL - Infovistar

There are two primary types of solutions for databases: SQL vs NoSQL – or relational databases and non-relational databases.

A relational database has a structured and predefined schema, like a phone book, which stores phone numbers and addresses. Non-relational databases are unstructured, and distributed, and have dynamic schema-like file folders that hold everything from a person’s address and phone number to their Facebook ‘likes’ and online shopping preferences.

SQL

Relational databases store data in rows and columns. Each row contains all the information about one entity, and the columns are all separate data points.

NoSQL

The following are the most common forms of NoSQL:

Key-Value Stores

Data is stored in an array of key-value pairs. The “key” is an attribute name, which is linked to a “value”.

Example: Redis, and Memcached.

Document Databases

Rather than rows and columns in a table, these databases store data in documents, which are grouped into collections. Each document can have an entirely different structure.

Example: MongoDB.

Wide-Column Databases

Instead of ‘tables,’ wide-column databases have column families, which are containers for rows. Unlike relational databases, we don’t need to know all the columns upfront, and each row doesn’t have to have the same number of columns. Therefore, columnar databases are best suited for analyzing large datasets.

Example: Cassandra and HBase.

Graph Databases

These databases are used to store data whose relations are best represented in a graph. Data is saved in graph structures with nodes (entities), properties (information about the entities), and lines (connections between the entities).

Example: Neo4J.

Search Engine Databases

They are used to search and analyze unstructured data.

Example: ElasticSearch, Splunk, and Solr

Multi-model Databases

A multi-model database is a database management system designed to support multiple data models against a single, integrated backend.

Example: DynamoDb and CosmosDb

Time Series Databases

They have optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, Internet of Things sensor data, and real-time analytics.

Example: InfluxDB

Differences between SQL vs NoSQL

Storage

SQL stores data in tables, where each row represents an entity, and each column represents a data point about that entity; for example, if we are storing a car entity in a table, different columns could be ‘Color,’ ‘Make,’ ‘Model,’ and so on.

NoSQL databases have different data, and storage models. The main ones are key-value, document, graph, and columnar.

Schema

In SQL, each record conforms to a fixed schema, meaning the columns must be decided and chosen before data entry, and each row must have data for each column. The schema can be altered later, but it involves modifying the whole database and going offline.

Whereas in NoSQL, schemas are dynamic. Columns can be added on the fly, and each ‘row’ (or equivalent) doesn’t have to contain data for each ‘column.’

Querying

SQL databases use SQL (structured query language) to define and manipulate the data, which is very powerful.

In a NoSQL database, queries are focused on a collection of documents. Sometimes it is also called UnQL (Unstructured Query Language). Different databases have different syntaxes for using UnQL.

Scalability

in SQL vs NoSQL, SQL databases are vertically scalable in most common situations, i.e., by increasing the horsepower (higher Memory, CPU, etc.) of the hardware, which can get very expensive. It is possible to scale a relational database across multiple servers, but this is a challenging and time-consuming process.

On the other hand, NoSQL databases are horizontally scalable, meaning we can add more servers easily to our NoSQL database infrastructure to handle large traffic. Any cheap commodity hardware or cloud instances can host NoSQL databases, thus making it a lot more cost-effective than vertical scaling. A lot of NoSQL technologies also distribute data across servers automatically.

ACID Compliance (Atomicity, Consistency, Isolation, Durability)

The vast majority of relational databases are ACID-compliant. For data reliability and a safe guarantee of performing transactions, SQL databases are the better bet.

Most of the NoSQL solutions sacrifice ACID compliance for performance and scalability.

Which one to use?

When it comes to database technology, there’s no one-size-fits-all solution. That’s why many businesses rely on both relational and non-relational databases for different needs. Even as NoSQL databases are gaining popularity for their speed and scalability, there are still situations where a highly structured SQL database may perform better; choosing the right technology depends on the use case. Let’s see the benefits of SQL vs NoSQL

Reasons to use SQL database

We need to ensure ACID compliance because ACID compliance reduces anomalies and protects the integrity of your database by prescribing exactly how transactions interact with the database. Generally, NoSQL databases sacrifice ACID compliance for scalability and processing speed, but for many e-commerce and financial applications, an ACID-compliant database remains the preferred option.
Your data needs structured and consistent. If your business is not experiencing massive growth that would require more servers, and you are working with data that is consistent, then there may be no reason to use a system designed to support various data types and high traffic volume.

Reasons to use NoSQL database

When Storing large volumes of data that often have little to no structure and a NoSQL database sets no limits on the types of data we can keep together and allows us to add different new types as the need changes. With document-based databases (like MongoDB and ElasticSearch), you can store data in one place without defining their “types” in advance.
Making the most of cloud computing and storage.
It’s very useful for rapid development since NoSQL doesn’t require any pre-preparation and if you’re working on quick iterations of your system that require frequent updates to the data structure without a lot of downtime between versions, a relational database will slow you down.

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