NoSQL vs SQL- 4 Reasons Why NoSQL is better for Big Data applications

Is SQL really outdated or NoSQL is easy to work with?NoSQL has empowered many Big Data applications.NoSQL vs SQL-Why NoSQL is better for Big Data applications?

NoSQL vs SQL- 4 Reasons Why NoSQL is better for Big Data applications
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Are you struggling to choose between NoSQL Vs. SQL for your big data applications? While SQL has been the go-to technology for many years, NoSQL has emerged as a popular alternative with several advantages. This blog will discuss the key differences between these two technologies and explore their strengths and weaknesses.

Consider an e-commerce platform like Amazon that handles millions of transactions every day. They need a reliable and efficient database management system to manage the growing demand and the massive data flow.

This is where NoSQL and SQL databases come into play.

NoSQL databases are becoming more popular due to their ability to handle unstructured data, while SQL databases remain a top choice for structured data. The NoSQL vs. SQL debate has been ongoing for years, and with the continual growth of big data applications, the importance of choosing the right database cannot be understated.  Almost all big data or data engineering projects require deciding which database technology to use. This comprehensive blog will dive into database technologies and explore the key differences between SQL and NoSQL databases to help you choose the right one for your upcoming big data projects.

SQL Vs. NoSQL - What’s the Difference?

Before jumping into the differences between SQL vs. NoSQL, let us quickly introduce you to these two popular databases and then explore their fundamental differences.

What is a SQL Database?

A SQL (Structured Query Language) database is a database management system that uses a relational model for organizing and accessing data. SQL is a popular choice for managing structured data among leading companies like Microsoft with its SQL Server, Oracle with its Oracle Database, and IBM with its DB2 because of its flexibility, scalability, and reliability. SQL databases are used in several industries, from finance and healthcare to e-commerce and social media. Many companies also use SQL databases for their internal data management needs, such as inventory management, customer relationship management (CRM), and accounting.  

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What is a NoSQL Database?

NoSQL is a database technology driven by Cloud Computing, the Web, Big Data, and Big Data users. NoSQL now leads the way for popular internet companies such as LinkedIn, Google, Amazon, and Facebook - to overcome the drawbacks of the 40-year-old RDBMS.NoSQL Database, also known as “Not Only SQL”, is an alternative to SQL database which does not require any fixed table schemas, unlike SQL.NoSQL generally scales horizontally and avoids primary join operations on the data. NoSQL databases can be referred to as structured data storage, which consists of relational databases as the subset.

A NoSQL (Not Only SQL) Database covers various databases with different data storage models. Graphs, Key-Value pairs, Columnar, and Documents are the most popular types of non-relational databases.

Below is a brief overview of the differences between SQL Vs. NoSQL-

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SQL Vs. NoSQL Examples

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SQL and NoSQL databases come in various types and are used for different purposes. Popular types of SQL databases include MySQL, Oracle, Microsoft SQL Server, and PostgreSQL, which are widely used in enterprise applications, e-commerce, finance, and other areas that require transaction processing and data consistency. MySQL is one of the most popular open-source relational databases used by organizations worldwide. Oracle is a powerful database used by large enterprises for handling complex transactions and data relationships. Microsoft SQL Server is also a popular database with high availability and scalability for critical applications.

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On the other hand, popular types of NoSQL databases include document-oriented databases like MongoDB and Couchbase, key-value stores like Redis and Riak, column-family databases like Apache Cassandra and HBase, and graph databases like Neo4j and OrientDB. MongoDB is a document-oriented NoSQL database popular for its flexibility and scalability. Cassandra is a column-family database ideal for handling large volumes of data and providing horizontal scalability. Couchbase is a key-value store that provides high performance and scalability for real-time applications. These databases are ideal for handling unstructured and semi-structured data, distributed computing, and real-time data processing. 

Each type of database has its strengths and weaknesses, and the choice depends on the application's specific needs. SQL and NoSQL databases can also be combined to achieve the best of both worlds.

Pros And Cons of NoSQL Vs. SQL

This section will explore the key advantages and disadvantages of the two databases- NoSQL and SQL. Let us first get a brief overview of the benefits and drawbacks of using SQL and NoSQL databases-

Pros of NoSQL

Cons of NoSQL

Pros of SQL

Cons of SQL

  1. Flexible data models, ideal for handling unstructured data.

Not as mature as SQL, lacking standardization and consistency across different NoSQL databases.

Structured data model, ideal for handling data with defined relationships.

Less flexible and requires a predefined schema/data structure.

  1. Horizontal scalability and distributed database computing capabilities.

Limited query functionality to manage data compared to SQL.

Mature technology with a wide range of tools and support to manage data.

May not be suitable for specific data types, such as unstructured or semi-structured data

  1. Lower cost and easier to maintain compared to SQL.

Less suited for handling complex, interrelated data relationships.

Strong community support and established best practices.

Limited scalability compared to NoSQL.

  1. Highly (horizontally) scalable and can handle large volumes of data.

Not ideal for complex transactions that require ACID (atomicity, consistency, isolation, durability) compliance.

ACID compliance ensures data consistency and accuracy.

Can be slower than NoSQL for large-scale data processing.

  1. Ability to handle multiple data formats, including JSON, XML, and key-value stores.

Limited reporting and analysis capabilities compared to SQL.

Strong data security and user access controls.

Can be more complex and time-consuming to set up and maintain.

Let us understand the key advantages and disadvantages of using SQL and NoSQL databases for your big data applications.

Advantages Of NoSQL Database Vs. SQL Database

Let us first understand the benefits of NoSQL database vs. SQL database for various use case scenarios.

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Advantages Of NoSQL

Here are some essential benefits of using NoSQL databases-

  1. Scalability- NoSQL databases are designed to be easily vertically/horizontally scalable by adding more nodes or expanding the infrastructure. This makes them a good fit for large-scale applications requiring high throughput and low latency.

  2. Flexibility- NoSQL databases can handle unstructured and semi-structured data formats, which makes them more flexible than SQL databases that require a predefined schema and rigid data structure. NoSQL systems can adapt to flexible data models and requirements more easily.

  3. Performance- NoSQL databases are optimized for fast reading and writing, which can be especially beneficial for real-time applications, such as gaming, social media, or e-commerce. NoSQL databases can handle vast data and complex queries without affecting performance.

  4. Availability- NoSQL databases are highly available and fault-tolerant, with built-in replication and sharding capabilities. NoSQL databases can continue functioning even if some nodes or servers fail without losing data or experiencing downtime.

  5. Cost-Effectiveness- NoSQL databases are more cost-effective than SQL databases, especially for large-scale applications that require a lot of hardware and infrastructure. NoSQL databases can run on commodity hardware and open-source software, which can reduce licensing and maintenance costs.

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Advantages Of SQL

Here are some of the advantages of SQL (Structured Query Language) relational databases-

  1. Data Integrity- SQL relational databases enforce data integrity rules and constraints, ensuring the data is accurate, consistent, and reliable. SQL databases have strict rules for data types, relationships, and foreign keys, preventing inconsistencies and errors.

  2. Relational Data Management- SQL databases use a relational data model, which allows for easy management of data relationships and dependencies. SQL databases can handle complex queries that involve multiple tables and data sources.

  3. Transaction Management- SQL relational databases have built-in transaction management capabilities, which ensure that data changes are atomic, consistent, isolated, and durable (ACID). This means that transactions can be rolled back or undone if they fail or encounter errors.

  4. Mature Ecosystem- SQL relational databases have a highly-advanced ecosystem of tools, libraries, and technologies supporting various applications and use cases. SQL databases have existed for several years, and thus, they have a solid and well-established user base.

  5. Compliance And Security- SQL relational databases have built-in security and compliance features, such as authentication, authorization, encryption, and auditing. SQL databases comply with various regulatory standards, such as GDPR, HIPAA, and PCI-DSS.

Disadvantages Of NoSQL Database Vs. SQL Database

Let us now understand the drawbacks of NoSQL databases vs. SQL relational databases for various use case scenarios.

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Disadvantages Of NoSQL

Here are some of the disadvantages of NoSQL databases-

  1. Limited Query Capabilities- NoSQL databases are optimized for specific use cases and data formats, meaning they may not support complex queries, data relationships, and SQL databases. This can make performing ad-hoc or exploratory data analysis on NoSQL databases challenging.

  2. Lack Of Standardization- NoSQL databases do not have a standard query language or data model, making it harder to migrate data or integrate with other systems. Each NoSQL database has its syntax and semantics, making it harder for developers to learn and use.

  3. Inconsistent Data Quality- NoSQL databases do not enforce strict data types or constraints, meaning data quality and consistency may be more difficult to ensure with SQL databases. This can lead to data duplication, inconsistencies, or errors, especially in large and distributed systems.

  4. Limited Transaction Support- NoSQL systems may not support full ACID transactions, meaning they may not guarantee consistency, isolation, and durability of data changes in all situations. This can make it harder to ensure data integrity and recover from failures.

  5. Lack Of Mature Ecosystem- NoSQL databases are relatively new compared to SQL databases, meaning they may not have as mature an ecosystem of tools, libraries, and technologies. This can make finding support, documentation, and best practices for specific use cases and applications harder.

Disadvantages Of SQL

Here are some advantages of SQL (Structured Query Language) databases-

  1. Limited Scalability- A SQL relational database may have scalability limitations, especially regarding horizontal scaling across multiple nodes or servers. This can make it harder to handle large-scale data and high throughput workloads.

  2. Rigid Data Modeling- A SQL relational database requires a predefined schema and rigid data structure, making it harder to adapt to changing data models and requirements. This can lead to schema evolution issues, especially in agile and fast-paced development environments.

  3. Slower Performance- A SQL relational database may have slower read and write performance than NoSQL databases, especially for complex queries and large data volumes. This can make it harder to achieve real-time or near-real-time processing of data.

  4. Expensive- SQL databases may be more expensive than NoSQL ones, especially for large-scale applications requiring enterprise-level features and licensing. This can make it harder to justify the cost for smaller or budget-constrained organizations.

  5. Complexity- A SQL relational database can be more complex to set up, maintain, and optimize than NoSQL databases, especially for applications that require advanced features, such as replication, sharding, or clustering. This can make finding qualified DBAs and developers with SQL expertise harder.

Difference Between NoSQL And SQL Databases With Example

Unlike traditional relational databases, NoSQL and SQL databases have different architectures and design principles that significantly differ in functionality, implementation, scalability, and flexibility. Here are some primary differences between these two types of databases:

A SQL relational database uses a relational model based on tables with predefined schemas (columns and rows). For example, consider a Customer table with name, address, and contact number columns. You can join this table with an Orders table to create a relationship between customers and their orders. However, this rigid schema can make it difficult to alter data models, handle complex data types, or store data.

NoSQL databases use dynamic schemas/data models optimized for different use cases. For example, a document database like MongoDB can store JSON documents with nested data structures that can be queried using a flexible syntax. This allows faster and more efficient access to data, especially for applications that involve large and diverse data structures.

A SQL relational database uses a standard query language called Structured Query Language (SQL) for manipulating data. Structured Query Language (SQL) database supports complex queries, multi-row transactions, and joins between tables, which makes it easier to analyze and retrieve data in a relational structure. For example, consider a query that selects all customers who purchased in the last month:

SELECT * FROM customers WHERE last_purchase_date > DATE_SUB(CURDATE(), INTERVAL 1 MONTH)

NoSQL databases often use query languages or APIs optimized for specific data models. These query languages may not support complex joins or transactions, but they can provide faster and more flexible access to data. For example, a Redis database having key-value stores can be queried using simple commands to retrieve or modify specific key-value pairs:

SET customer:1 '{"name": "John", "age": 35}'

GET customer:1

A SQL relational database is vertically scalable, meaning it can handle more data and users by upgrading the hardware resources of a single server. For example, you can scale up a MySQL database by adding CPU, RAM, or storage to the SQL server. However, this can be expensive and limit the maximum scale of the database.

NoSQL non-relational databases are horizontally scalable and can distribute data across multiple servers and nodes. For example, you can scale out a Cassandra database by adding more nodes to the cluster, which allows for easier scaling and better fault tolerance. However, this can make managing data consistency and partitioning more complex.

SQL databases are designed to be ACID compliant, meaning they guarantee the atomicity, consistency, isolation, and durability of transactions. This ensures that data changes are either fully committed or rolled back in case of failures. For example, a transaction that transfers funds from one account to another must succeed or fail to ensure data consistency.

NoSQL databases may not be fully ACID compliant, making it challenging to ensure data consistency and integrity in distributed database systems. However, some NoSQL databases provide eventual consistency, allowing faster data access and higher availability. For example, a Riak database can provide read and write operations without locking or blocking but with a risk of some older data.

SQL databases can provide high performance for structured queries and data manipulation, especially for small and medium-sized datasets. However, they may not be optimized for large-scale and unstructured data processing. For example, a PostgreSQL database can provide fast response times for complex queries on a few million rows.

NoSQL non-relational databases can provide faster performance for unstructured and non-relational data access, especially for a large and distributed database. However, they may not be optimized for complex joins or multi-row transactions. For example, a Hadoop database can handle huge databases with high throughput and parallelism.

SQL databases can be more expensive than NoSQL databases, especially for enterprise-level features and licensing. For example, a Microsoft SQL Server or Oracle database can require significant upfront costs for licenses, hardware, and maintenance. Additionally, SQL databases may require specialized personnel for database administration, which can add to the overall expenses.

NoSQL databases can be more cost-effective for specific use cases, especially for startups and small businesses. For example, you can deploy a MongoDB or Apache Cassandra database on commodity hardware or cloud-based infrastructure, which minimizes upfront costs and allows for flexible database scaling. Additionally, NoSQL databases often have open-source versions that are free to use and can provide most of the essential features.

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NoSQL Database Vs. SQL Database- Use Cases

NoSQL and SQL databases have various benefits and drawbacks, making them suitable for different use cases, depending on the application's specific requirements, such as data model, scalability, consistency, and security. Let us look at a few suitable examples of when to use SQL and NoSQL databases.

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Use Cases For NoSQL Databases

Here are some ideal use cases for NoSQL databases-

  1. Big Data And Real-time Analytics- NoSQL databases are designed to handle large and unstructured data sets with high availability and scalability. They are ideal for use cases that require real-time analytics and decision-making, such as IoT data streams, social media feeds, and log data analysis. For example, Apache Cassandra is a popular NoSQL database used by companies like Netflix and eBay for their high-volume and high-velocity data workloads.

  2. Content Management And Personalization- NoSQL databases are well-suited for content management and personalization use cases, where the data constantly changes and needs to be updated quickly. For example, MongoDB is a popular NoSQL database used by content management systems and e-commerce websites to store data and serve dynamic content and recommendations.

  3. Mobile And Web Applications- NoSQL databases are optimized for horizontal scaling and can handle high read and write traffic with low latency. They are ideal for mobile and web applications that need to serve millions of users simultaneously. For example, Firebase is a popular NoSQL database used by mobile app developers to store user data, push notifications, and real-time messaging.

Use Cases For SQL Databases

Here are some ideal use cases for SQL databases-

  1. Transactional Systems And Financial Applications- SQL databases are ideal for transactional systems that require strong consistency and transactional support. They are ideal for applications that deal with financial transactions, inventory management, and order processing. For example, large enterprises use Oracle and Microsoft SQL Server as popular relational database management systems for critical applications.

  2. Business Intelligence And Data Warehousing- SQL databases are optimized for complex queries and data analysis. They are suitable for business intelligence and data warehousing use cases, where the data is structured and needs to be organized into tables and relationships. For example, Amazon Redshift is a popular SQL database used by data analysts and business intelligence teams to analyze large data sets and generate insights.

  3. Compliance And Security- SQL databases are ideal for compliance and security use cases where the data needs to be secured and audited. They provide strong authentication, authorization, and encryption features to protect sensitive data. For example, MySQL database is a popular relational database management system used by many financial institutions and government agencies for secure data management.

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When To Use SQL Vs. NoSQL For Your Big Data Project?

Choosing between SQL and NoSQL databases depends on the type of data you are using and the requirements of your application. The following guidelines will help you choose when to use NoSQL vs. SQL databases for your big data projects:

When To Use NoSQL

NoSQL databases are ideal for big data applications that need scalability, flexibility, agility, and the processing of unstructured and semi-structured data. You should use NoSQL databases-

  • When working with a large amount of semi-structured or unstructured data, such as IoT sensor data, videos, images, and social media posts.

  • For real-time data processing and storage in tasks like tracking web traffic, performing real-time analytics, and processing financial transactions.

  • When you need higher scalability to handle massive amounts of data, and you want to scale horizontally by adding more database nodes.

  • When you work with a dynamic schema, i.e., you need to add or change data fields and don't want to be constrained by a rigid schema.

  • When you can overlook some data consistency and don't have to execute complex queries and transactions.

When To Use SQL

SQL databases are ideal for handling structured data, complex queries and transactions, and applications requiring data accuracy and consistency. You should use SQL databases-

  • When you are working with structured data, such as that related to banking, healthcare, or the government.

  • When you have to carry out complex queries and transactions that demand consistency and restrictions on data integrity.

  • When you are working with a small amount of data and can avoid horizontal scaling.

  • When combining or joining data from various tables and when you need to ensure high data durability and consistency.

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SQL Database Vs. NoSQL Database- Project Ideas For Practice

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Here are a few real-world SQL and NoSQL project ideas to help you understand better the implementation of each database for your big data applications.

NoSQL Projects For Big Data Experts

Here are a few big data projects you must practice to understand the right use of NoSQL non-relational database systems-

  1. NoSQL Project On Yelp Dataset Using HBase And MongoDB

In this NoSQL project, you will learn how to store Yelp business characteristics in two NoSQL databases (HBase and MongoDB) and how to retrieve this data for processing or querying relational databases. You will learn how to combine Spark and NoSQL databases to retrieve data for processing and combine Hive and NoSQL databases for data retrieval using queries.

Source Code- NoSQL Project On Yelp Dataset Using HBase And MongoDB

  1. Integrating Spark And NoSQL Database For Data Analysis

In this NoSQL project, you will look at two database platforms - MongoDB and Cassandra and explore the conceptual difference in how these databases work. You will create sample tables, perform analytical queries and integrate the spark application to load the UK MOT vehicle testing dataset into them.

Source Code- Integrating Spark and NoSQL Database for Data Analysis

  1. Spark Integration And Analysis With NoSQL Databases 2 - Cassandra

In this NoSQL project, you will explore data modeling in Cassandra and how it is suited, especially in a Hadoop environment. You will learn how to integrate the UK MOT vehicle testing dataset with Spark and also compare analytical queries of MongoDB and Cassandra.

Source Code- Spark integration and analysis with NoSQL Databases 2 - Cassandra

SQL Projects For Big Data Experts

Here are a few big data SQL projects you must practice to understand the right use of SQL database systems-

  1. SQL Project For Data Analysis Using Oracle Database-Part 1

You will learn the fundamentals of Oracle SQL programming and its applications through this project. In this SQL project, you will work with several commands for data analysis and connect the Oracle SQL developer to a SQL database. You will explore SQL tables, understand how they relate to one another, create tables, and add data to them.

Source Code- SQL Project For Data Analysis Using Oracle Database-Part 1

  1. SQL Project For Data Analysis Using Oracle Database-Part 2

This project will teach you the application of joins in SQL and how to code for different kinds of operators in SQL. You will also understand the difference between ANSI and standard queries. Additionally, you will learn about the UNION operators and experiment with integrating different tables using ANSI queries. Working on this project will also teach you how to deal with column ambiguously defined errors.

Source Code- SQL Project For Data Analysis Using Oracle Database-Part 2

  1. SQL Project For Data Analysis Using Oracle Database-Part 3

This project discusses the aggregate functions (Min, Max, Count, and Avg) in detail. In addition to learning about the inline view and collecting data in a single row, you will also explore clauses and the basics of subqueries. Working on this SQL project will show you how to filter aggregated data and conduct data analysis using the group-by and exit commands.

Source Code- SQL Project For Data Analysis Using Oracle Database-Part 3

NoSQL Vs. SQL- Which Is Better?

The debate over which database is better - NoSQL or SQL - has been ongoing for years, and the truth is that neither is better. Each database has significant strengths and weaknesses, making them suitable for different use cases. NoSQL databases can handle unstructured and large volumes of data with high availability and scalability, while SQL databases handle transactional data with strict data consistency and complex queries. However, choosing the right database can be challenging, especially for big data professionals who must manage and analyze vast amounts of data daily. This is where ProjectPro comes in. 

Master SQL Vs. NoSQL Database Technologies With ProjectPro

ProjectPro offers industry-level end-to-end solved SQL and NoSQL big data projects to help professionals gain hands-on experience and better understand these databases' suitable use cases. With ProjectPro, big data professionals can develop their skills and stay up-to-date with the latest big data and data science technologies, making them more valuable to their organizations and more competitive in the IT job market. So, whether it's NoSQL or SQL relational database management system, the key is choosing the right database for the right use case and continuously developing one's skillset to stay ahead in the rapidly evolving world of big data. So what are you waiting for? Start your learning journey with over 250 Big Data and Data Science end-to-end solved projects by ProjectPro!

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FAQs on NoSQL vs. SQL

NoSQL databases can be faster than SQL databases for specific operations, such as read-heavy workloads and horizontal scaling. However, SQL databases can be faster for complex queries and have strong consistency.

NoSQL is better than SQL database systems when handling unstructured or semi-structured data and enabling high scalability and availability, while SQL databases are better than NoSQL in handling structured data and ensuring strong data consistency and transactional support.

The difference between NoSQL and MySQL is that NoSQL is a non-relational database management system to manage vast and unstructured data sets with high availability and scalability, while MySQL database is a relational database management system to organize data into tables with defined relationships.

 

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