BigData: SQL vs NoSQL - Choosing one over the other

You have a project that needs to handle large amounts of data. And you’re not sure whether your database should be using SQL or NoSQL.

So which one should you use when starting the project?

And if you choose one now, can you switch to the other later?

For those who are already somewhat familiar with the concepts of SQL and NoSQL, let’s jump in to compare the pros and cons of using the two. Hopefully this article helps you decide the database type that’s most suitable for you.

Introduction

SQL and NoSQL are two types of databases used for storing and managing data. In the context of BigData, both of these database types have their own uniqueness and usecases. To decide whether SQL or NoSQL is better for handling BigData depends on the specific requirements of the project. Both SQL and NoSQL have their own strengths and weaknesses, and the choice between the two will depend on the type of data being stored, the desired level of structure, and the use cases for the data.

The primary points of consideration when deciding between the two are capacity for complex analytical processing, data mining, predictive modelling, machine learning, scalability, flexibility, performance, quantity of data, schema strictness and complexity of queries.

SQL

SQL databases are relational databases that use a fixed schema to structure data into tables with defined columns and relationships between them. They use a structured query language (SQL) to manage and manipulate data.

Pros

• Structured Data:
  • Based on a well-defined schema, which makes it easy to store and retrieve data in a structured manner.
  • This structure is essential for ad hoc queries, data analysis, and reporting.
• ACID Compliant:
  • Support ACID (Atomicity, Consistency, Isolation, Durability) transactions.
  • Ensures data integrity and consistency even in case of failures or crashes.
• Query Optimisation:
  • Optimised query engines that can quickly search for specific data based on a set of conditions.

Cons

• Scalability:
  • Scaling can be a challenging task, particularly when it comes to handling large amounts of data.
  • Not designed to scale horizontally, which means adding more resources to an existing database may not always be an option.
• Inflexibility:
  • Strict schemas, which can be inflexible and slow down the development process.
  • If the data requirements change, the schema must be changed as well, which can take time and effort.
• Performance:
  • As the data grows, the performance can start to deteriorate, particularly when it comes to complex queries or large amounts of data.

Use of SQL for BigData

SQL is used for BigData in situations where structured data is a requirement. Some common scenarios where SQL is used for BigData are:

1. Data Warehousing:
   1. Useful for storing large amounts of historical data that need to be analysed and used for reporting purposes.
   2. Well-suited for this type of data because they have optimised query engines that can quickly search for specific data based on a set of conditions.
2. Data Integration:
   1. Can be used for integrating data from multiple sources, including both structured and unstructured data.
   2. The structured nature of SQL databases makes it easy to map the data from different sources into a common schema.
3. Transactional Data:
   1. Can be used to store transactional data that requires ACID (Atomicity, Consistency, Isolation, Durability) transactions.
   2. This ensures that the data remains consistent and that transactions are completed successfully, even in the event of failures or crashes.
4. Analytical Processing:
   1. Can be used to perform complex analytical processing, such as data mining, predictive modelling and machine learning.
   2. This is possible as SQL databases have optimised query engines that can quickly search for specific data based on a set of conditions.

NoSQL

NoSQL databases, on the other hand, are non-relational databases that use a variety of data models to store and manage data, such as key-value, document, columnar or graph. They are designed to handle large amounts of unstructured or semi-structured data, such as text, images or videos. NoSQL databases provide more flexibility in terms of data modelling and can scale to handle BigData more easily.

Pros

• Scalability:
  • Designed to scale horizontally, which makes it easy to add more resources to an existing database.
  • This makes NoSQL databases a good choice for handling large amounts of data.
• Flexibility:
  • Do not have strict schemas, which makes it easy to store data in a variety of formats.
  • This makes it easier to accommodate changes in the data requirements.
• Performance:
  • Optimised for handling large amounts of data and complex queries.
  • They are often faster than SQL databases, particularly when dealing with unstructured data.

Cons

• Unstructured Data:
  • Do not enforce strict schemas, which can lead to unstructured data.
  • This can make it difficult to search for specific data or perform ad hoc queries and analysis.
• No ACID Compliance:
  • Do not support ACID transactions, which can make it difficult to ensure data consistency and integrity.
• Query Optimisation:
  • May not have optimised query engines, which can slow down data retrieval times for complex queries.

Use of NoSQL for BigData

NoSQL is used for BigData in situations where scalability, flexibility, and performance are the primary concerns. Some common scenarios where NoSQL is used for BigData are:

1. Large-scale Data Storage:
   1. Designed for scaling horizontally, it makes them well-suited for handling large amounts of data.
   2. They can easily store and retrieve data in a variety of formats, which makes them a good choice for BigData projects.
2. Unstructured Data:
   1. Often used to store unstructured data, such as images, videos, audio, and text.
   2. This is because NoSQL databases do not have strict schemas, which makes it easy to store data in a variety of formats.
3. Real-time Data Processing:
   1. Optimised for real-time data processing, which makes them a good choice for applications that require quick response times, such as online gaming, social networking and e-commerce.
4. Distributed Systems:
   1. Designed to run across multiple nodes, which makes them well-suited for distributed systems.
   2. This allows for automatic data replication and failover, which improves data availability and reduces downtime.
5. Cloud Computing:
   1. Often used in cloud computing environments because they are easy to scale and manage.
   2. This makes it possible to quickly add more resources as the data grows, without having to worry about the underlying infrastructure.

Starting with one and switching to other

It is possible to switch between SQL and NoSQL databases, but it is not necessarily an easy process.

Moving from a SQL database to a NoSQL database requires a change in the data model and the way data is accessed and manipulated, which can be a significant undertaking. This is because NoSQL databases have a different data model and don't support SQL queries, so data needs to be migrated and applications need to be modified to use the new database.

Moving from a NoSQL database to a SQL database can also be challenging, especially if the application relies heavily on the flexible schema and horizontal scaling capabilities of NoSQL databases. This is because SQL databases have a fixed schema and may require complex queries to achieve the same level of flexibility as NoSQL databases.

That being said, if the needs of the application change over time, it may be worth considering a migration to a different type of database.

If you’re working with smaller database sizes, in terms of complexity of data structure as well as the volume of the data, it could a lot easier to move between SQL and NoSQL. For databases with higher complexity and/or larger volumes of data, it would be ideal to start with one system, based on your best judgement, and then trial a certain subset of your data structure and data. This allows you to to see if the chosen database type is suitable within that subset and then allows you to make a call whether to stick with the current type or switch now before you get further invested.

Summary

In simple terms, SQL databases are best for structured data that needs to be organised into tables with defined relationships, while NoSQL databases are best for handling large amounts of unstructured or semi-structured data.