SQL vs NoSQL Databases — Part 1

Databases are organized collections of data that are structured to enable efficient storage, retrieval, and manipulation of information. They’re the digital equivalent of a perfectly organized file cabinet, but instead of folders and papers, they handle all sorts of data — names, numbers, images, and more.

They’re vital across various applications — powering websites, mobile apps, financial systems, healthcare records, and countless other platforms. When it comes to types, databases mainly fall into two categories: SQL databases and NoSQL databases.

In this article, we’ll delve into SQL databases, examining their characteristics and functionalities. Subsequently, we’ll explore NoSQL databases in the next article.

SQL Databases

SQL databases, also known as relational databases, organize data in a structured manner using tables with rows and columns. The term “relational” signifies the ability to establish connections or relationships between different tables, creating a network of related data.

Let’s explore some key characteristics of SQL databases;

Database schema — https://popsql.com/static/images/templates/sample_db_erd.png

1. Tabular Structure

In SQL databases, data is structured within tables, which resemble grids or spreadsheets. These tables are composed of rows (also called records or tuples) and columns (also called fields or attributes). Each column is designated for a specific type of data, such as text, numbers, or dates, representing a particular attribute. Rows, on the other hand, house individual instances or records of data, with each row containing a unique set of values across the columns. This tabular structure helps organize and categorize information systematically, allowing for efficient storage, retrieval, and manipulation of data.

2. Structured Query Language(SQL)

SQL, which stands for Structured Query Language, serves as the standardized language used to communicate with SQL databases. This language offers a wide array of commands and statements that allow users to perform various operations:

1. Data Manipulation: SQL facilitates adding, modifying, deleting, and retrieving data from the database tables.
2. Querying: Users can formulate queries using SELECT statements to retrieve specific data based on defined criteria or conditions.
3. Schema Modification: SQL enables the modification of the database structure, such as creating or altering tables, defining constraints, or modifying existing schema elements.
4. Access Control: It provides mechanisms for managing user permissions, controlling access to specific data or functionalities within the database.

SQL’s versatility and comprehensive syntax empower users to interact with the database efficiently, ensuring effective database management.

3. Relational Model

SQL databases rely on the relational model to establish connections between tables using keys like primary keys and foreign keys. These keys serve as crucial components in creating relationships between different tables within the database.

• Primary Keys: They uniquely identify each record in a table, ensuring that every row has a distinct identifier.
• Foreign Keys: These keys establish connections between tables by referencing the primary key of another table. They ensure referential integrity by enforcing relationships and constraints between linked data across tables.

These relationships maintain the accuracy and consistency of data across the database. They prevent anomalies such as inconsistent or orphaned data by enforcing rules that ensure the integrity of related information.

Moreover, these established relationships enable powerful querying capabilities, particularly through JOIN operations. JOINs allow users to retrieve data from multiple tables simultaneously by leveraging the connections defined by primary and foreign keys.

4. Predefined Schemas

Schemas act as the architectural blueprints within relational databases, defining the structure and organization of data storage and access.

Here’s how schemas play a crucial role:

1. Table Definition: Schemas specify the tables present in the database, outlining their names, attributes, and the data types of each column. This includes defining constraints like uniqueness, not null values, or default values for columns.
2. Relationship Establishment: They define relationships between tables through the use of keys, primarily primary keys and foreign keys. These keys establish connections, ensuring referential integrity and maintaining the relationships between related data across tables.
3. Data Integrity Enforcement: Schemas enforce rules and constraints, ensuring data accuracy and consistency. They prevent anomalies like duplicate records, null values in mandatory fields, or data that violates defined constraints.

5. ACID Compliance

SQL databases adhere to the ACID principles which serve as the backbone of reliable and consistent database transactions within SQL databases. These principles are:

1. Atomicity: Atomicity guarantees that transactions are treated as indivisible or “all-or-nothing” units. This principle ensures that either all the tasks within a transaction are executed successfully and committed to the database, or if any part fails, none of the changes are applied.
2. Consistency: Consistency ensures that the database moves from one valid state to another after each transaction. It involves enforcing all predefined rules, constraints, relationships, and data formats consistently, both before and after the execution of a transaction. These rules might include constraints like ensuring uniqueness in certain columns, data type adherence, or maintaining referential integrity between related tables.
3. Isolation: Isolation ensures that multiple transactions can occur concurrently without interfering with each other. It guarantees that transactions are executed independently and in isolation, as if they were the sole operations running in the system. This prevents issues like “dirty reads”, where one transaction reads data that is being modified by another transaction.
4. Durability: Durability guarantees that once a transaction is committed, the changes made by that transaction are permanently saved and will not be lost, even in the event of a system failure. The changes are stored in a durable form, typically on a persistent storage, ensuring that they persist even after a system crash or restart.

6. Normalization

Normalization in relational databases is a process used to organize data efficiently by reducing redundancy and dependency. It involves structuring tables and their relationships to minimize data duplication and anomalies, ensuring data integrity and efficient querying.

Examples of Relational Database Management Systems (RDBMS)

RDBMS oversee the management of relational databases. They consist of software tools, applications, and services designed for creating, maintaining, and interacting with relational databases. A few examples include:

1. MySQL: An open-source RDBMS widely used for web applications due to its reliability, ease of use, and scalability.
2. PostgreSQL: Another open-source RDBMS known for its advanced features, extensibility, and support for complex queries and transactions.
3. Oracle: A commercial database used for enterprise applications, known for its scalability, security, and high performance.
4. Microsoft SQL Server: A robust RDBMS developed by Microsoft, suitable for Windows-based environments, offering strong integration with other Microsoft products.
5. SQLite: A lightweight, self-contained SQL database engine that doesn’t require a separate server process, commonly used in embedded systems, mobile apps, and small-scale applications.
6. MariaDB: A community-developed, forked version of MySQL, designed for compatibility with MySQL and enhanced performance and features.
7. Microsoft Access: A desktop-based database management system part of the Microsoft Office suite, suitable for small-scale applications and individual use.

Some Use Cases of SQL Databases

1. Transactional Systems: SQL databases are ideal for systems requiring ACID properties, such as banking systems, e-commerce platforms, stock trading platforms, healthcare systems, booking and reservation systems, and online payment systems, where data integrity is crucial.
2. Content Management Systems (CMS): SQL databases often power CMS platforms that handle structured content like articles, posts, user information, etc.
3. Data Warehousing: Data warehousing is the process of gathering, storing, and organizing vast amounts of structured data from different sources. Relational databases play a pivotal role in this environment, as they’re used to both store and analyze this structured data. They excel in handling complex queries necessary for analyzing historical data in data warehousing setups.

Pros of SQL Databases

1. ACID Compliance: Support for ACID properties ensures reliable transaction processing.
2. Ensure Data Integrity: Relational databases ensure data integrity through constraints and relationships, preventing inconsistencies.
3. Ease of Use: Relational databases have been around for a long time, leading to a wealth of resources, tools, and expertise available for developers and administrators.
4. Facilitates Complex Queries: The relational model of relational databases enables easy data retrieval through joins, facilitating complex querying and reporting.
5. Standardized SQL Language: The standardized SQL language fosters a common ground for working with relational databases, promoting ease of use, portability, and access to extensive resources and expertise across different database platforms.

Cons of SQL Databases

1. Performance Impact with Complex Queries: As queries become more complex, performance can decrease, especially with large datasets.
2. Scalability Challenges: Horizontal scalability that is, adding more servers or nodes to handle increased load can be complex due to the emphasis on maintaining strong relationships between data.
3. Normalization Overhead: Normalization, while ensuring data consistency, can lead to increased complexity in data retrieval across multiple tables.
4. Rigid Schema Changes: Changes to the database schema might be challenging and require careful planning, impacting flexibility.

In Conclusion,

SQL databases generally refer to relational databases that use SQL as their query language and adhere to the relational model for data management and organization. This adherence to the relational model, coupled with their commitment to the ACID principles, makes them invaluable for applications demanding precise data handling, such as financial systems and e-commerce platforms.

Stay tuned for the next article(link here) where we will shift gears towards NoSQL databases, uncovering their diverse structures, scalability advantages, and suitability for dynamic and evolving data needs.