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SQL vs NoSQL Databases: Key Differences and Practical Insights

Discover how to decide between SQL and NoSQL databases in data science and application development. Learn their strengths, use cases, and industry applications.
Nov 2023  · 15 min read

Database selection is crucial in data science and application development as it directly impacts performance, scalability, and the ability to handle the data being used for the application. The choice between NoSQL vs. SQL databases impacts the efficiency of data retrieval, storage, and processing, which is critical to the success and responsiveness of data-driven applications in meeting specific needs.

SQL and NoSQL are two different database technologies, with SQL databases emphasizing structured, relational models, and NoSQL databases adopting a flexible, schemaless approach for handling diverse and dynamic data with more emphasis on scalability and speed.

This article will discuss the differences between the two database systems. However, we will not be covering the fundamentals of either. Please check out our SQL Fundamentals learning path to learn how to access data from SQL databases.

What is SQL?

The Genesis of SQL Databases

In 1970, the relational model for databases, a model for effectively organizing and storing data in tables in a column and row structure, was outlined by Dr. Edgar Codd in his landmark paper, "A Relational Model of Data for Large Shared Data Banks." By 1974, IBM developed System R, the first project to implement the relational model for storing data, and they developed SQL as the language to interact with these relational databases.

Over the years, new standards for SQL have been developed, and Oracle, IBM, and Microsoft have each developed relational database management systems (RDBMS) for efficiently, securely, and conveniently using SQL to interact with SQL databases.

Next, open-source languages like MySQL began making SQL and relational databases more accessible to the general population. SQL is now widely accessible both on physical and cloud systems such as AWS, Azure, and Google Cloud.

Characteristics of SQL Databases

In SQL databases, the relational model serves as the conceptual foundation, organizing data into structured and interconnected tables. SQL databases are made up of schemas and tables:

  • Schema: defines the structure of the database, including tables, their fields, data types, available values, and relationships.
  • Tables: The foundational units of a database, representing entities (e.g., information about customers, products, or transactions), with rows containing individual records for each entity (e.g., a specific customer) and columns storing specific attributes about the entity (e.g., the customer’s name or email).

The relational model enforces consistency through two types of keys: (1) primary keys uniquely identifying each record and (2) foreign keys establishing relationships between tables. You can learn more about relational databases in our Introduction to SQL course.

SQL, or Structured Query Language, serves as the interface for managing these databases, enabling the creation, retrieval, updating, and deletion of data through standardized commands. A list of these commands and SQL’s capabilities can be seen in our SQL Basics Cheat Sheet. This model and its components provide a powerful and flexible framework for organizing and querying data in a manner that ensures integrity and coherence across the database.

A look at our our SQL Basics Cheat Sheet

A look at our our SQL Basics Cheat Sheet

Advantages of SQL

SQL provides a standard set of commands for defining, querying, updating, and administering data in a relational database management system (RDBMS). Key SQL operations include SELECT (querying data), INSERT (adding new records), UPDATE (modifying existing records), and DELETE (removing records).

When using these commands, SQL ensures that ACID (Atomicity, Consistency, Isolation, and Durability) properties are maintained. This ensures the reliability and consistency of database changes.

  1. Atomicity: This property ensures that each attempted set of changes to a database is treated as a single, indivisible unit of work. Either the whole unit is committed to the database, or none of them are if any part of the transaction fails.
  2. Consistency: Ensures that a transaction ends with the database in a valid state. The database must satisfy a set of integrity constraints both before and after the transaction.
  3. Isolation: Ensures that simultaneous transactions do not lead to inconsistencies in the database. Each transaction appears to execute in isolation, unaware of other transactions running. Isolation prevents interference between transactions and maintains their integrity.
  4. Durability: Durability guarantees that once a transaction is committed, its effects persist even in the event of a system failure. The changes made by the transaction are permanently stored in the database, and they survive system crashes or power outages.

These fundamental properties of SQL databases ensure that database systems are reliable and consistent, even in the case of unexpected events or system failures.

Common SQL Database Systems

There are three commonly used RDBMS:

  1. MySQL: MySQL is an open-source RDBMS, now owned by Oracle, known for speed, reliability, and ease of use. MySQL is often used in LAMP stack (Linux, Apache, MySQL, PHP/Python/Perl) environments for small to medium-sized web applications.
  2. PostgreSQL: An open-source object-relational database system with advanced features (e.g., support for custom functions and procedures as well as for complex queries, indexing, and transactions). PostgreSQL is best for large-scale applications, data warehousing, and geospatial data.
  3. Microsoft SQL Server: A proprietary RDBMS by Microsoft, which is part of a suite with editions like Express, Standard, and Enterprise. Microsoft SQL Server integrates well with Microsoft's ecosystem and is suitable for diverse applications from small businesses to large enterprises.

Check out our MySQL in Python tutorial to learn more about getting started with this RDBMS, as well as our Beginner’s Guide to PostgreSQL.

What is NoSQL

You can read our full guide on What is NoSQL to get an in-depth understanding of Not Only SQL databases. Below, we’ve summarized our findings.

The Rise of NoSQL Databases

NoSQL (Not Only SQL) databases were created as a response to the challenges posed by the expansion of big data and the increasing need for scalability in database systems. Traditional relational databases struggle to efficiently manage vast amounts of unstructured and semi-structured data commonly seen in modern big data. NoSQL databases represent a more flexible and scalable alternative to relational databases.

They effectively handle diverse data types, accommodate rapid data growth, and distribute data across multiple servers efficiently.

NoSQL solutions like MongoDB, Cassandra, and Couchbase don’t use the same rigid structures that the relational model supports. These systems favor schema flexibility and horizontal scaling, allowing organizations to adapt to the dynamic nature of big data while maintaining performance and reliability. The correlation between NoSQL, big data, and scalability underscores a pivotal evolution in database management, empowering businesses to tackle the challenges presented by the recent exponential growth of data.

Characteristics of NoSQL Databases

Contrary to SQL databases, NoSQL databases use dynamic schemas and support various data models to address diverse application needs.

One defining characteristic of non-relational databases is the use of dynamic schemas. Unlike the predefined, fixed schemas of relational databases, non-relational databases allow for flexibility. Different records in the same database may have different fields. For example, in a customer database, one customer may have a name and age field while the other has a name and email field.

There are various types of NoSQL databases:

  1. Document Stores: Utilize a flexible, JSON-like structure to store data as documents. They are useful for handling complex, hierarchical data and support dynamic schemas. They are most commonly used in content management systems, e-commerce platforms, and real-time applications. A popular example of this system is MongoDB.
  2. Key-Value Database: This is the simplest form of NoSQL database, where each unit of data is stored as a key-value pair. These systems are efficient and useful in scenarios where lots of data needs to be accessed quickly with fast response times. Two popular examples of key-value NoSQL databases are Redis and Amazon DynamoDB.
  3. Graph Database: These databases represent relationships between data entities. Thus, they are most useful in scenarios when relationships are important, such as in social networks or recommendation systems. Two popular examples of Graph databases are Neo4j and Amazon Neptune.
  4. Column-Family Database: Column-Family databases represent data in columns instead of rows. They are best for large-scale distributed systems and systems that read and write often such as time-series applications and IoT applications. Commonly used Column-Family databases include Apache Cassandra and HBase.

You can learn more about MongoDB in our Introduction to MongoDB in Python course.

Deciding which one to use depends on the application you are using a NoSQL database for.

Advantages of NoSQL

NoSQL databases offer several advantages, with notable strengths in scalability, flexibility, and performance when dealing with unstructured data.

NoSQL databases offer scalability in the form of horizontal scalability and elasticity. They are designed to scale horizontally, allowing organizations to handle increasing amounts of data by adding more servers to a distributed system. This makes them well-suited for applications with growing or unpredictable workloads.

Many NoSQL databases also provide automatic sharding and load balancing, distributing data across multiple nodes to ensure efficient resource utilization and improved performance. This capability enables systems to scale up or down dynamically based on demand and is known as elasticity.

They also provide schema flexibility. NoSQL databases use dynamic schemas, allowing for flexibility in data representation. This means that fields in a record can vary across different documents, accommodating the diverse and evolving data structures commonly encountered in modern applications.

They also perform well on unstructured and semi-structured data types like JSON and XML. Data today is often unpredictable, especially when user-generated, and NoSQL handles storing this data well.

The advantages of NoSQL from our article

The advantages of NoSQL from our article

Common NoSQL Database Systems

A few of the most commonly used NoSQL database systems are:

  1. MongoDB: popular document-oriented NoSQL database that stores data in flexible, JSON-like BSON documents, providing scalability and high performance for diverse data types.
  2. Cassandra: A distributed and highly scalable NoSQL database designed for handling large amounts of data across multiple servers with a decentralized architecture, making it suitable for high-speed and high-volume applications.
  3. Redis: An in-memory data structure store known for its speed and versatility, serving as a caching mechanism, message broker, and key-value store, with support for various data structures like strings, hashes, and sets.

Key Differences Between SQL and NoSQL

Let’s compare SQL vs NoSQL to get a better understanding of where the two overlap and contrast:

Data structure

SQL databases, characterized by structured data models, enforce a predefined schema where data must fit into tables with specific columns and data types. This rigid structure ensures consistency, and it works well for applications with stable and predictable data requirements.

In contrast, NoSQL databases adopt flexible data models, allowing for dynamic and schemaless data storage. This flexibility enables developers to insert data without a predefined schema. NoSQL databases are most useful in scenarios where data structures may be undefined or frequently change.

Scalability

SQL and NoSQL servers emphasize different scaling strengths due to their designs. SQL systems typically rely on vertical scaling, which involves improving and adding resources to the same server to handle increased load. Horizontal scaling, typically seen in NoSQL systems, is achieved by adding more servers or nodes to a distributed system, which then helps increase capacity.

In NoSQL systems, the nodes communicate with each other and distribute the load, so adding more nodes helps increase the overall capacity of the system. This is a more scalable and cost-effective solution for managing a growing database and increased database traffic.

Schema flexibility

SQL databases use predefined schemas, enforcing a rigid structure for data organization and requiring tables to adhere to a predefined structure of rows and columns. In contrast, NoSQL databases embrace dynamic schemas, offering flexibility in data representation. These databases allow for the insertion of data without a predefined structure, enabling developers to adapt schemas on the fly.

This flexibility is particularly beneficial for handling diverse, evolving, and unpredictable data types. Thus, NoSQL databases work well for scenarios where data structures may not be fully known in advance or are subject to frequent changes, while SQL databases work well when the data are well-structured and predictable.

Transactional integrity

SQL and NoSQL database management systems take different approaches to ensuring reliability. SQL relies on ACID properties (Atomicity, Consistency, Isolation, Durability), which, as we discussed above, ensure immediate and strict consistency in the database. SQL queries guarantee that either all or none of the changes made during a transaction are committed to the database and have rules for how to handle concurrent transactions and unexpected events.

On the other hand, NoSQL databases, emphasizing scalability and distributed architectures, adopt the concept of eventual consistency. Eventual consistency acknowledges that, in a distributed system, it may take some time for all nodes to converge to a consistent state after an update. While NoSQL databases sacrifice immediate consistency for scalability and fault tolerance, they ensure that, given enough time, all replicas of the data will eventually converge to the same state.

This trade-off allows NoSQL systems to handle large-scale, distributed environments where real-time consistency might be challenging to achieve efficiently.

Choosing Between SQL and NoSQL

If you’re unsure which database to use, we’ve outlined some of the ways you can choose between NoSQL and SQL:

Use Case Scenarios

SQL databases work best in scenarios when data is structured and predictable, complex relationships need to be accurately captured, and immediate data integrity is important.

SQL’s rigid structures and ACID properties make it well-suited for these types of applications.

Some common use cases where SQL databases work well are:

  • Financial applications where strict consistency is very important, and data are typically well structured and representable tabularly
  • Customer relationship management (CRM) systems where data are typically well-structured, and there may be many relationships that must be accurately represented.

NoSQL databases work best in cases when it's useful to have flexible data structures which can dynamically adapt to new information and schemas, when scalability and performance are important, and for unstructured data. NoSQL’s dynamic schemas and horizontal scaling make it great for these types of use cases, which present in scenarios like:

  • Real-time big data analytics where quick and scalable performance is important
  • Social media databases where much of the incoming data are unstructured and unpredictable.

SQL vs NoSQL in Industry

SQL Database Uses

A few examples of industries that rely on SQL databases are:

  1. Finance: Many financial institutions manage transactional data and customer records. SQL’s ACID properties ensure the data are accurate and that the transactions lead to an immediately consistent database once processed.
  2. Retail: Many retail businesses leverage SQL databases, as they must manage complex relationships related to products, shipping, sales, customers, and supplier information. Their data are also typically well-structured and predictable.
  3. Government and Public Sector: Government agencies manage a lot of citizen records and public services which are subject to regulatory requirements. SQL’s structured nature helps with regulatory adherence.

NoSQL Database Uses

A few examples of industries that rely on NoSQL databases are:

  1. Social Media: Social media platforms handle large volumes of unstructured data, such as user profiles, posts, and interactions. The flexibility of NoSQL accommodates the dynamic nature of social media content and data.
  2. Logistics and Supply Chain: They use NoSQL databases for real-time tracking of shipments, inventory management, and other diverse and dynamic data sources across the supply chain. NoSQL’s performance and scalability for real-time data make it well-suited for this industry.
  3. Gaming: The gaming industry leverages NoSQL databases for managing player data, leaderboards, and in-game analytics. The ability to scale horizontally is essential for handling the massive amounts of data generated by online multiplayer games.

Conclusion

The choice between SQL and NoSQL databases plays a pivotal role in the effectiveness and success of data science and application development. The impact on performance, scalability, and adaptability to data types directly influences the responsiveness of data-driven applications.

SQL, with its structured, relational model, excels in scenarios where data is well-defined, and relationships are crucial, ensuring immediate consistency through ACID properties.

Conversely, NoSQL databases offer flexibility and scalability, catering to dynamic, unstructured data types prevalent in modern applications.

As industries evolve, understanding the nuances between these fundamental database technologies becomes essential for architects and developers navigating the diverse landscape of data management.

If you would like to learn more about the underlying language for accessing data in relational databases, please check out our intermediate SQL course.


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Kevin Babitz

Data Science Blogger | Technical Analyst at WayFair | MSE in Data Science at UPenn

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